Python for Engineers & Robotics – Master NumPy, Pandas, and ChatGPT Automation

[00:00] In this comprehensive course, you will [00:02] learn Python programming from scratch [00:05] for mechanical engineering. Starting [00:07] with the basics like variables, [00:09] operators, and the interactive shell, [00:12] you will quickly progress to powerful [00:14] data and computation libraries. You'll [00:17] master numpy for numerical computations [00:20] and matrix operations, pandas for [00:23] handling CSV and Excel data and mapplot [00:27] lib to visualize your engineering [00:29] analysis. This course is even good if [00:31] you already know Python. [00:33] >> Hello everyone, welcome to the course on [00:35] Python for mechanical engineers and [00:37] robotics using chat GPT. I am Chintan [00:40] Morsia your instructor and I will be [00:42] guiding you through this course using [00:44] different modules to understand the use [00:46] of Python in the field of mechanical [00:48] engineering. Python as many of you may [00:50] already know is a versatile and [00:52] beginnerfriendly programming language. [00:54] Today we will dive into why the Python [00:57] has become an essential tool for modern [00:59] mechanical engineers and explore its [01:01] numerous applications in this field. [01:03] Python is highle interpreted interactive [01:07] and object-oriented scripting language. [01:10] Python is designed to be highly [01:12] readable. It uses English keywords [01:14] frequently where as other languages use [01:16] punctuations and it has fewer [01:18] syntactical constructions than other [01:20] languages. Why these all are the [01:22] advantages? We will understand in our [01:24] further sessions. Python is an [01:26] open-source and cross-platform [01:28] programming language. It is available [01:30] for use under Python software foundation [01:33] license and it is available on all the [01:36] major operating system platforms like [01:37] Linux, Windows and Mac. To facilitate [01:40] new features and to maintain that [01:42] readability, the Python enhancement [01:45] proposal process was developed. This [01:47] process allows anyone to submit a PEP [01:50] for a new feature, library or other [01:52] addition. The design philosophy of [01:55] Python emphasizes on simplicity, [01:57] readability and unambiguity. Python is [02:00] known for its batteries included [02:02] approach as Python software is [02:04] distributed with a comprehensive [02:06] standard library of functions and [02:08] modules. Python supports imperative [02:10] structured as well as object-oriented [02:13] programming methodologies. As we already [02:15] discussed, Python code style is actually [02:17] more of a design philosophy and suggest [02:20] to write a code which is clean, simple, [02:23] beautiful, explicit and readable. Python [02:26] was developed by Guidoan Razam in the [02:29] late '90s and in early '90s at the [02:32] National Research Institute for [02:33] Mathematics and Computer Science in [02:36] Netherlands. Now let us discuss the [02:38] important features of the Python. Python [02:41] is evolving every day where now the [02:43] Python 3.x is receiving regular updates. [02:46] Python's developer community is focusing [02:49] on performance improvements making it [02:51] more efficient while retaining its ease [02:53] of use. The first feature is the Python [02:56] is interpreted. Python is processed at [02:58] runtime by the interpreter. Here you do [03:01] not need to compile your program before [03:03] executing it. Python converts the source [03:06] code to byte code using the interpreter [03:08] and then executes the byte code using [03:11] the Python virtual machine which [03:13] translate it into the machine code [03:15] instructions for the processors to [03:17] execute. As we already know computer can [03:19] only understand the binary language that [03:21] is the zero and one. So to get it [03:23] converted into the machine code the [03:25] Python virtual machine is provided. [03:27] Python language is interactive as you [03:30] can actually sit at a Python prompt and [03:32] interact with the interpreter directly [03:34] to write your programs. Python is [03:36] object-oriented that means Python [03:38] supports object-oriented style or [03:41] technique of programming that [03:42] encapsulates code within objects. The [03:45] meaning of object-oriented is you may [03:48] see any physical entity it may be a [03:51] living or non-living nearer to your [03:53] environment. You can convert each entity [03:55] into the different variables, methods [03:58] and the functions using this concept of [04:01] object- oriented language. How this [04:03] object- oriented language is useful that [04:05] again we will understand in our further [04:07] sessions. Python is a greatest language [04:09] for the beginner level programmers and [04:11] supports the development of wide range [04:13] of applications from simple text [04:15] processing to browsers to the games. [04:19] Python is being heavily used for machine [04:21] learning, artificial intelligence and [04:24] data science. So for sure its future [04:27] remains bright. Its role in these [04:29] rapidly growing fields ensures that [04:31] Python will stay relevant for further [04:34] years. Python is also increasingly [04:36] becoming the first programming language [04:38] taught in the schools and universities [04:40] worldwide. So it solidifies its place in [04:43] the tech landscape. So I hope you got [04:45] the idea that how important to [04:48] understand the Python language is not [04:49] only with the perspective of mechanical [04:51] engineering but with the perspective of [04:53] the current development. We must need to [04:55] have the knowledge of any of the best [04:58] used programming language in the current [04:59] market era. In our next session we will [05:02] also understand the significance of [05:04] Python in the mechanical engineering [05:06] field. Looking forward to see you all in [05:08] our further session. Till then thank [05:10] you. Hello everyone. In this session, we [05:13] will understand the significance of [05:15] Python in mechanical engineering. [05:17] Computer programming may not be our main [05:19] focus as mechanical engineers, but in [05:22] today's digital world, these skills are [05:24] crucial. Learning a user-friendly [05:26] language like Python can pay a huge [05:28] dividends. Since this language [05:31] pre-quipped with all the scientific and [05:33] mathematical tools we need, it's easy to [05:35] expand Python scripting into many use [05:38] cases. As your command over it grows, [05:40] Python can open doors to faster and more [05:43] optimized workflow. It can simulate a [05:46] huge amount of data and most crucially, [05:49] it can automate boring and tedious tasks [05:51] so that they are finished in a matter of [05:54] seconds. Using Python, you can use [05:56] built-in functions to run numerical [05:58] analysis faster and easier than ever [06:01] before. You may make use of many [06:03] packages that come with Python like [06:05] numpy, pandas, scypi, mplot lib and many [06:09] more to make your work faster based on [06:11] your requirements. With just a little [06:14] bit of setup in the beginning, you can [06:15] use python to write a program that will [06:18] execute any calculations you need in an [06:21] instant. Considering so many different [06:23] uses of Python in the mechanical [06:25] engineering, there are top four uses of [06:27] Python in mechanical engineering. Let us [06:30] understand the use of Python in data [06:32] analysis and the simulation field. The [06:35] most widely used Python application is [06:37] numerical analysis. It would take a long [06:40] time to answer problems analytically [06:42] when linear equations and ordinary [06:44] differential equation and partial [06:46] differential equations are inward. In [06:48] mechanical engineering, boundary [06:50] conditions are frequently present making [06:52] numerical analysis problems twice as [06:54] difficult to solve. Assume you are [06:56] attempting to determine the pressure [06:58] difference across a pipe with a liquid [07:00] running through it. Not only will [07:03] solving the problem take an eternity, [07:05] but obtaining correct data and plotting [07:07] the difference in a graph will be nearly [07:10] impossible. You may solve such problems [07:12] in a couple of seconds with programming [07:14] languages and get graphical simulation [07:16] at the same time. Learning, numerical [07:19] analysis and coding gives up a world of [07:21] possibilities in industries such as [07:23] manufacturing, automotive, energy, and [07:26] even software engineering like thermal [07:28] engineers. Mechanical or thermal [07:31] engineers are employed by software [07:33] businesses such as Google and Facebook [07:35] to ensure the efficient and safe thermal [07:38] management of their database and cluster [07:40] systems. These engineers build scripts [07:42] in programming languages like Python [07:45] using NumPy and Pandas library and then [07:48] import them into the CFD software to [07:51] evaluate a variety of ideas. So you may [07:54] think of the possibilities of using [07:57] Python in your field. It is having a [07:59] vast variety of uses but you need to be [08:02] have the open mind to understand all [08:04] these different possibilities. Secondly, [08:06] let us understand the use of Python in [08:09] thermodynamics. Python can be used to [08:11] address problems with classical [08:13] thermodynamics. You may develop a code [08:15] to solve your problem and save time. [08:17] Whether it includes chemical kinetics or [08:20] fluid dynamics, industries in the real [08:23] world are unconcerned about how you [08:25] solve problems or finish jobs. You may [08:27] either spend 60% of your time addressing [08:30] the mathematical or thermodynamic [08:32] problems and rushing through the [08:34] remaining 40% of the assignment or you [08:37] can spend 20% of your time tackling [08:40] these problems in Python and focusing on [08:42] the genuine issues at the end. Your [08:45] efficiency is the sole aspect of your [08:47] result that matters. Python has a large [08:50] library and a straightforward syntax [08:52] that makes it simple to address [08:54] difficult issues. Third major [08:57] application is the computational fluid [08:59] dynamics. Python has a wide range of [09:01] applications in the field of [09:03] computational fluid dynamics. You will [09:05] need to develop your scripts in [09:07] programming languages like MATLAB or [09:09] Python to simulate difficulties with the [09:11] CFD softwares. You may write scripts for [09:15] pre-processing simulations and [09:17] post-processing in CFD. These [09:19] efficiently handles the complex fluid [09:21] dynamics problems. Also, you may also [09:24] integrate the Python libraries like [09:26] Numpai and Scypi with the CFD software [09:29] to streamline your workflows. Fourth [09:31] major use of Python is in control system [09:34] and in robotics. Control systems are [09:36] essential in mechanical engineering for [09:39] designing automated machines, robotics [09:41] and processes. They help ensure that a [09:44] system behaves in a predictable and [09:46] desired manner by regulating its inputs [09:49] and outputs. Python with its robust [09:52] libraries like control and metplot lib [09:55] simplifies the analysis, design and [09:57] simulation of control systems making it [10:00] accessible even to those with minimal [10:02] programming experience. In conclusion, [10:06] Python is revolutionizing the field of [10:08] mechanical engineering. Its versatility, [10:11] ease of use, and the powerful libraries [10:14] make it an indispensable tool for [10:16] addressing both routine and complex [10:18] engineering problems. Whether you are a [10:21] student exploring new possibilities or a [10:23] seasoned professional looking to enhance [10:25] efficiency, Python has something to [10:28] offer you. So, we may say that Python is [10:30] a gamecher for mechanical engineers. [10:33] Thank you for joining me today and I [10:35] encourage you to start integrating [10:37] Python into your engineering workflow. [10:40] You will be amazed by what you can [10:42] achieve. Hello everyone. In this [10:44] session, you will understand how to set [10:46] up a Python development environment to [10:48] run a Python program in your computer. [10:51] For those who prefer to install Python [10:54] on your computer, this guide will walk [10:56] you through the installation process on [10:58] Windows. To install Python on your [11:00] Windows, first go to the Google Chrome. [11:04] Here you need to go to the Visual Studio [11:06] Code website. For that, type Visual [11:09] Studio Code download. In the first link, [11:12] you will find the option Visual Studio [11:15] Code link. So, select that link. Here [11:18] you will see the setup is available for [11:21] the Windows, for different Linux [11:23] operating systems and for the Mac. So [11:26] for your required system you may [11:28] download any setup. Here we will [11:30] download the Windows setup. So go to [11:32] Windows and select it. [11:38] Now Chrome will automatically start [11:40] downloading the VS code. [11:44] Once the downloads complete you will [11:45] find it in the download section and here [11:48] you will find the VS Code setup. Now run [11:51] this installer and here just you need to [11:53] follow the installation process. So [11:56] first you need to select the agreement. [11:59] Select next. Here keep these two options [12:01] selected. Register code as an editor and [12:04] add to path. Select next and click on [12:07] install. [12:09] So it will take some time based on your [12:11] computer system and the VS code gets [12:14] installed into your system. Once the [12:16] installation completes you will see this [12:18] screen. Currently, we do not want to [12:20] launch the Visual Studio Code. So, [12:23] unselect this and click on finish. Next, [12:26] we need to download the Python [12:28] installer. So, to download the Python [12:30] installer, go to the Python official [12:32] website. So, for that, go to the search [12:34] bar and now type Python download. Here [12:38] in the first link, you will see the [12:40] official Python website. Select this [12:42] link. Once the link is open, you will [12:45] see that the website automatically [12:47] detects your operating system and gives [12:49] you the right installer. So here it is [12:51] already mentioned download the latest [12:53] version for Windows. At the time I am [12:56] downloading this software. The current [12:58] version is the Python 3.13.0. [13:02] There might be a possibility the version [13:04] might differ based on your timing. [13:06] Select download Python and it gets [13:09] downloaded and you will see it into the [13:11] download history. Now run the installer. [13:14] Depending on your security settings, you [13:16] might be prompted to allow the access. [13:19] Simply allow it and go to proceed. Once [13:22] you have run the installer, you will [13:23] come across this screen. On the screen, [13:26] you will see two options, install now [13:29] and customize installation. We suggest [13:31] you to skip all customization steps and [13:34] simply click install now. Before you [13:36] click on the install now, you need to [13:38] check on add python.exe exe to path as [13:42] it ensures Python is added to our [13:44] systems path variable which is [13:47] recommended and you may also click on [13:49] use admin privileges when installing [13:51] python.exe exe. Click install now as it [13:55] will include all the necessary files [13:57] needed later. Selecting these options [14:00] make it easier to run a Python program [14:02] from the command prompt directly without [14:04] specifying the full path of the Python [14:07] executable. Setup progress may take time [14:10] based on your computer system. Once the [14:12] setup was successful, you will see this [14:14] screen and click on close. After the [14:17] installation is complete, you can verify [14:19] whether the Python is installed by using [14:22] some of the commands. So for that first [14:24] you need to go to the command prompt. So [14:26] for that click on Windows button and [14:29] type command prompt and open this [14:32] application. Now to verify the Python [14:34] version you need to type python space [14:37] dash dash version and hit enter. So here [14:41] you will see the python number 3.13.0. [14:45] Keep in mind that the version number [14:47] might differ from the one above [14:49] depending on your installed version. So [14:52] with this you are all set to run Python [14:54] program on your device. In our next [14:57] video we will start with running your [14:59] first Python program. I hope you are [15:01] excited to run your first Python [15:03] program. So stay connected in the next [15:05] video and we will start our journey with [15:07] the Python programming. Hello everyone. [15:10] Now we are at the point where you can [15:12] run your first Python program. So to run [15:15] your first program first open VS code [15:19] here click on the file in the top menu [15:22] and then select a new file. So go to [15:25] file select a new file. Here you will be [15:28] prompted to select the file type or to [15:30] enter the file name. So let us enter the [15:33] file name first [15:36] program. Here you need to keep in mind [15:38] that you need to save this file with py [15:42] extension. This is the Python file [15:44] extension. So let us keep it py. Hit [15:48] enter. Once you hit enter, you will be [15:50] prompted to save the file. You need to [15:52] select the folder where you want to save [15:54] your file. After selecting this folder, [15:57] go to this file name here. File name [16:00] will appear that is the first program [16:02] and provide the extension py and select [16:06] create file. Now once you save this file [16:09] in a 99%age cases you will see the popup [16:12] asking do you want to install the [16:14] recommended Python extension from the [16:16] Microsoft for the Python language and it [16:20] is recommended that you select install. [16:22] Once you select install the extension [16:24] will appear and it will start installing [16:27] extension of Python in your visual [16:29] studio code. You need to wait till [16:32] Python gets installed in your VS code [16:35] system. Once Python is installed, you [16:37] will see that it will appear that the [16:40] system is installed. Now there might be [16:42] a possibility you have not received that [16:44] pop-up. So no need to worry again. You [16:47] can install this Python by going to the [16:49] extension section. So for that in the VS [16:52] code you need to click on the extension [16:54] on the left side bar. So here you will [16:57] find the option extensions. Click on [16:59] extensions. Once you click on [17:01] extensions, you will find the search [17:03] option and here in this search option [17:06] you need to type Python. You will find [17:08] so many different Python extensions. But [17:10] from that you need to select Python [17:12] extension by Microsoft and click on [17:15] install. So once you click on this again [17:17] you will find the same window and you [17:19] need to select on the install. Once this [17:22] extension is installed we are good to go [17:24] with our first program. So now click on [17:27] close of this window. Again let us hide [17:29] this extension. And now the file is [17:32] created with the name first_program. [17:35] py. Now you need to write the code as [17:38] mentioned over here keeping in mind the [17:41] capital and smaller cases also because [17:43] this language is also case sensitive. [17:46] Now write the code in this file as I'm [17:48] mentioning. First write print within [17:51] round bracket and within double quote [17:53] type welcome to Python learning course. [17:57] So here we have wrote the first file [18:00] related to the Python. Now to run this [18:03] file you need to click on the run button [18:05] on the top right side of your screen. So [18:08] at this top right side you will find the [18:10] run button and here it is mentioned run [18:12] Python file. So click on it. So at the [18:15] bottom terminal window will open and [18:17] here you will see the output welcome to [18:19] Python learning screen. Using this print [18:22] option we have printed whatever output [18:25] we are requiring on the screen. So I [18:27] hope you are excited after running this [18:29] first Python program and you are happy [18:32] to learn more about this Python [18:34] programming language and how we can [18:35] create a more complex problems and we [18:38] can solve these more complex problem [18:40] using this Python language. In our next [18:42] video onwards we will learn about the [18:44] Python interactive cell and running [18:46] different Python scripts. So looking [18:49] forward to see you all in our further [18:51] videos. Till then thank you. Hello [18:53] everyone. Welcome to the next session on [18:56] Python interactive cell and its scripts. [18:59] The Python interactive cell and the [19:01] script running capabilities form the [19:04] foundation of working with Python. These [19:06] tools allow engineers and programmers to [19:09] quickly execute Python commands, debug [19:11] code, and run complex programs. Let's [19:14] explore how to use the interactive cell, [19:17] create a Python scripts, and execute [19:19] them effectively. The Python interactive [19:21] cell is also known as the ripple that is [19:24] the read, evaluate, print and loop. It [19:27] is a command line environment that lets [19:29] you execute Python commands one at a [19:31] time and see results immediately. So [19:34] some of the features of this interactive [19:36] shell are it has instant feedback. That [19:39] means you can execute commands and view [19:41] the results instantly. It is also useful [19:44] for experimentation. So mostly it is [19:47] ideal for testing small pieces of code [19:49] or learning Python syntax and it is [19:52] easily accessible as it comes with [19:55] pre-installed with Python. So it [19:57] requires no additional setup to launch [19:59] the interactive cell. In Windows you [20:01] need to go to the command prompt or in [20:03] Mac you need to go to the terminal. So [20:06] for the case of Windows first open the [20:08] command prompt. After opening the [20:10] command prompt type python in Windows. [20:14] If you are having the Mac then you need [20:16] to type Python 3 in the terminal and [20:18] then hit enter. Once you hit the Python [20:21] you will see the prompt like this and [20:23] now you are present in the Python [20:25] interactive cell. So here you can try [20:27] simple commands including the arithmetic [20:30] operations or maybe the variable [20:31] assignments and many more. Let us say we [20:34] want to perform the arithmetic [20:35] operations. So type 2 + 3 hit enter and [20:40] here you will get the output five. [20:42] Suppose I want to create a variables. [20:44] That means I want to assign values to [20:46] the variable and then want to perform [20:48] mathematical operations. So let us say I [20:51] want to assign x= 10. Hit enter y = 5. [20:56] And now I want to print the [20:57] multiplication of x and y. So we can [21:00] write x into y and hit enter. So again [21:04] you will get the output to be 50. So as [21:07] you can see you can perform very simple [21:10] operations. You can understand syntax [21:12] using this Python interactive cell. Here [21:15] I will be giving you some basic [21:17] functionalities to make you aware of the [21:19] Python use. But we will understand each [21:22] functionality in detail in our further [21:24] sessions. So let us say I want to assign [21:27] a name and name equal to I want to type [21:30] Gho. So I will write within double quote [21:33] Gaz and double quote over. Now hit [21:36] enter. Now suppose I want to convert [21:39] these name into the uppercase letter. So [21:42] again this functionality is available [21:44] inbuilt in the Python. So for that you [21:46] need to write name dot we want to [21:50] convert it into the uppercase. So [21:51] inbuilt functionality available is you [21:54] need to type upper and then round [21:56] brackets and hit enter. So now you will [21:59] get the output in capital letters. Let [22:02] us say I am having the list of numbers [22:04] and I want to find the sum of that [22:06] numbers. So for that I may write like [22:08] these nums equal to within square [22:11] bracket I will provide the values. So [22:14] values being the 1 2 3 4 and five hit [22:18] enter. Now I want to find the summation [22:21] of these values. So again inbuilt [22:23] functionality available is I will type [22:25] sum and within bracket I will pass [22:27] variable nums and hit enter. So here you [22:31] will see the output to be 15. So as you [22:34] can see you can perform very basic [22:36] operations in these interactive cell and [22:39] you can understand the different [22:40] concepts or the syntax in interactive [22:43] cell very easily. The major limitation [22:45] of this interactive cell is it is great [22:48] for quick task but it's not suitable for [22:50] large programs or saving work. For such [22:53] cases you need to use the Python script. [22:56] Python script is simply a text file [22:58] containing a Python code and saved with [23:01] the py extension and we have already [23:04] created the Python script in our [23:06] previous video in which we have ran our [23:09] first program. So to create the Python [23:11] script this Python interactive cell is [23:13] not useful and we need to go to the [23:15] visual studio code. So to exit from this [23:18] command prompt or the Python interactive [23:20] cell you can type exit and hit enter. [23:23] And to close the command prompt you can [23:26] directly click on this close button. Now [23:28] open the Visual Studio Code. Once the VS [23:31] code is open again go to file select a [23:34] new file and suppose we want to create a [23:36] Python script to find the area of [23:38] rectangle. So provide the name of file [23:41] to be area py. Hit enter. Select the [23:45] file location. Again provide the file [23:47] name to be the area py and select create [23:50] file. It will create a new file and new [23:53] window will open with the name area py. [23:56] Now as we already know the area of the [23:59] rectangle is length into width. So to [24:01] find the area we need to provide the [24:03] length and width and then we need to [24:05] perform the multiplication operation. So [24:08] let us define the variable length equal [24:10] to 5 width equal to 10. We will store [24:15] the answer into the variable area. So [24:18] area equal to length into width hit [24:22] enter. Now to get the output on the [24:24] screen we need to use the command print [24:26] within round bracket. Let us type the [24:28] area of rectangle is then after double [24:31] quote comma and we will provide the our [24:34] variable that is the area. So it will [24:37] print first area of rectangle is and the [24:39] value of area. We will understand how to [24:42] use this print function in our further [24:44] videos. Now to run the program go to [24:46] this run button and click on it. So it [24:49] will open the terminal window and here [24:51] you will see the output. The area of [24:53] rectangle is 50. Now so this file is [24:56] saved and you can change the value of [24:58] length and width and accordingly every [25:00] time you will get your output. So these [25:02] script are useful where we want to store [25:04] our programs and want to run these again [25:07] and again based on requirement. So [25:09] understanding the interactive sale and [25:11] Python script lays the groundwork for [25:14] efficient Python programming. The sale [25:16] is perfect for quick experiments while [25:18] scripts are better suited for reusable [25:21] and complex projects. With these tools, [25:23] you can quickly prototype ideas and [25:26] scale them into a full-fledged [25:27] applications. I hope you got the idea [25:30] how to make use of these Python [25:32] interactive cell and the Python scripts. [25:34] Now from our next video onwards we will [25:37] start understanding basic Python syntax. [25:40] So looking forward to see you all in our [25:42] next video. Till then thank you. Hello [25:45] everyone. In this session we will [25:47] understand how to provide commands in [25:49] Python programming. Comments in Python [25:51] are lines of text in your code that are [25:54] ignored by the Python inter. They are [25:56] used to explain the purpose of the code, [25:58] clarify complex logic and make the [26:00] program easier to understand for others [26:02] and yourself also. So this video [26:04] explains the different types of commands [26:06] in Python, their use and best practices [26:09] for writing effective commands. There [26:11] are two types of commands in Python. [26:13] Single line commands and the multi-line [26:15] commands. To understand that, let us go [26:17] to the Visual Studio Code. Now, as we [26:20] have already discussed, you can create a [26:22] folder and save your file in that [26:24] specific folder with the extension py. [26:27] Now once you are opened in that folder [26:29] you can directly create a new file by [26:32] going to these left hand side bar of the [26:34] VS code. Click on this explorer and here [26:37] you will see list of programs that you [26:39] have created. So in this section [26:42] programs is mentioned that is folder [26:44] name in my case. In your case folder [26:46] name may be different. To the right hand [26:48] side of it you will find the different [26:50] options to create a new file new folder. [26:53] So here we want to create a new file. So [26:55] click on it and provide the file name [26:57] with the py extension. Let us give file [27:01] name to be commands py. Now you can hide [27:04] this explorer by again clicking on it. [27:07] First we will understand the single line [27:08] commands. The single line command start [27:11] with the hashtag symbol. So to provide [27:14] the command first you need to type [27:16] hashtag and now you can write any of the [27:19] command that you want to provide to your [27:22] program. Let us write use of comments in [27:25] Python. Again hit enter. Now suppose I [27:29] am considering x= 10 and I want to print [27:32] x. So type print x and run the program. [27:36] So here in the terminal you will find [27:38] that the output is 10 but the statement [27:41] that we have written using the hash [27:43] symbol is not printed or not executed in [27:46] the program. So this comment is used on [27:49] a specific line to explain a piece of [27:52] code but it is not executed by the [27:55] interpreter. So how this comment is [27:57] useful? Let us say we have already [27:59] mentioned x= 10. So now we want to [28:02] explain to someone that what is the [28:04] meaning of x= to 10. So we may use these [28:07] hash symbol and now type it is the [28:09] assignment of value 10 to the variable [28:12] x. So we can write assigning value 10 to [28:15] the variable x. So this comment explain [28:19] what is the meaning of x= to n. Same way [28:22] next statement we have written the print [28:24] x. So if you want to explain someone [28:26] what is the meaning of print x again we [28:28] can type hash and the purpose of this [28:30] print function is it prints output on [28:33] the terminal or the console. So let us [28:35] write useful for getting output on [28:39] console. So I hope that you got the idea [28:42] what is the use of commands in this [28:44] Python program. Now second one is the [28:46] multi-line commands. So as the name [28:49] suggest we can provide the commands to [28:51] the multiple line also. The first useful [28:54] approach is we may use multiple time [28:56] hashtag symbols to provide the [28:58] multi-line comments. Let us use the [29:00] multiple hash symbol. First comment is [29:03] this is multi-line comment. Suppose this [29:06] is line one of the command and second [29:09] one is the line two of the command. So [29:13] here as you can see we can provide the [29:15] multiple commands in individual lines by [29:17] using this a symbol. The Python doesn't [29:20] have a direct syntax for multi-line [29:22] commands but you can make use of the [29:25] ripple quotes as a workaround. As you [29:27] can see if our comment is of more than [29:29] 10 to 15 lines. So it is somewhat [29:31] tedious to provide the hashic symbol to [29:33] each and every individual lines. So for [29:35] that we may use the concept of triple [29:38] quotes in the Python to provide the [29:40] comment. Let us copy above all three [29:42] lines and again use it for the purpose [29:44] of triple quotes. Now for the reference [29:47] you can comment all these lines by using [29:49] the shortcut also. You can press control [29:52] + forward slash in the windows and [29:55] command plus forward slash in your Macs. [29:58] Suppose we want to uncomment all these [30:00] three lines. So again press control plus [30:03] forward slash. Again if you want to [30:05] provide the multiple commands so you can [30:06] press command plus forward slash or [30:09] control +/ in your system. As we want to [30:12] provide the multi-line commands let us [30:14] uncomment all these lines. Now to [30:17] provide the multi-line commands you can [30:18] use triple quotes symbols. So first [30:21] provided triple quote at the starting of [30:23] the command. So here you will see we [30:25] have provided the triple quotes and [30:27] within these triple quotes we need to [30:29] provide our multiple lines. So let us [30:32] cut these lines from here and paste it [30:34] in between. So here you can see all [30:36] these lines are considered as the [30:38] command by the Python. So this is a [30:41] multi-line command and it spans across [30:43] multiple lines useful for the detailed [30:45] explanations. Here just keep in mind [30:48] that these triple quoted commands are [30:50] typically a string lit but are often [30:52] used as commands when not assigned to a [30:55] variable. What is the string lit is? We [30:57] will understand this in detail in our [30:59] further sessions. So using a single hash [31:02] symbol you can provide a single line [31:04] command or using the triple quotes you [31:06] can provide the multi-line comments. So [31:08] why to use these commands? So these [31:11] comments enhance the readability. That [31:13] means it helps others to understand the [31:16] purpose of the code. It clarifies the [31:18] complex logic by breaking down the [31:20] complicated sections of the code in [31:22] explanatory comments. It also aids in [31:25] debugging aid. That means you can [31:27] temporarily disable parts of the code [31:29] during debugging and you can [31:31] specifically select a portion of the [31:33] code that you want to check. And the [31:35] last one is the documentation. So you [31:37] can provide the details about the course [31:39] functionality, inputs, outputs and the [31:42] limitations. I hope you got the idea how [31:44] to use the Python commands and what is [31:46] the importance of Python commands in [31:48] Python. Hello everyone. In this module, [31:51] we will understand different Python [31:53] variables. A variable is a named [31:55] reference to a memory location that [31:57] stores data. The values stored in a [32:00] variable can change during the program [32:02] execution. to understand the Python [32:04] variables. Again let us go to DBS code [32:07] for the reference you can create a new [32:09] file and in that file you can practice [32:11] with this video together. Let us [32:13] consider first example. If I am writing [32:15] X= 10. So here X is a variable storing [32:19] the value n. There are mainly two [32:22] features of variable. The first one is [32:24] the dynamic typing and second one is the [32:26] mutable in nature. So what is the [32:29] meaning of dynamic typing? Here as you [32:31] can see we have written x = 10. So here [32:34] x represents the integer type. We may [32:37] also write like name equal to python. So [32:41] here you can see by default interpreter [32:44] understand that it is a string. Same way [32:47] if I'm writing z= to4. [32:50] So here interpreter will understand it [32:53] as dot. So here no need to declare the [32:55] data type explicitly. Python determines [32:58] it automatically. Second is the value of [33:01] the variable can be reassigned based on [33:04] the requirement. So here first the value [33:06] of x is equal to 10. So if we are [33:08] printing value of x it gets printed as [33:11] the if you want to change its value we [33:13] can also write x= to 20. So now the [33:16] value of x is 20. If we print x again [33:21] python will consider its value as 20. [33:23] Let us run program. So here you will see [33:26] that the first value of x is 10 and [33:29] secondly value is 20. So I hope you got [33:32] the idea how to create different [33:34] variables. There are certain rules are [33:36] there for naming the variables. So let [33:39] us understand the rules for naming [33:41] variables. First rule is we must start [33:44] with letter or underscore. So suppose I [33:47] want to provide a variable name the [33:49] valid variable equal to 10. So this is [33:53] the valid variable name as it is [33:55] starting with the letter four. You can [33:57] also start with the underscore that is [33:59] underscore private equal to five. So [34:03] again this name is valid. Second [34:06] important rule is we cannot start with a [34:09] digit. So suppose the name is one [34:12] variable equal to 10. So as you can see [34:15] here Python highlights it with the red [34:18] color that means this name is not valid. [34:21] So keep in mind that we cannot start the [34:23] variable name with digit. Let us comment [34:25] it to avoid the error. Third important [34:28] rule is it can contain letters, digits [34:31] and underscore and its combination. [34:34] Means if I am keeping variables_1 [34:37] equal to five. So again this name is [34:40] val. Fourth rule is Python variable name [34:43] is case sensitive. That means if I am [34:46] keeping age equal to 25 and second [34:49] capital A with the same name age equal [34:52] to 30. So these both variable names are [34:56] different. So again this point is [34:57] important to note the variable names are [35:00] case sensitive in the case of Python. [35:02] And the last important rule is we cannot [35:05] use reserved keywords. Now what is the [35:07] meaning of reserved keywords that we [35:09] will understand in our further sessions [35:11] also but you can think like that the [35:13] keywords like if else for which are [35:16] inbuilt available in the Python these [35:19] same names we cannot assign as a [35:21] variable names. So I hope you got the [35:23] idea how to define the variable name in [35:26] Python. In our next video we will [35:28] understand how to assign the values to [35:31] the variable in the different manners. [35:34] Hello everyone. In this video, we will [35:36] continue our discussion on Python [35:37] variables and we will understand how to [35:40] assign values to variables in a [35:42] different manners. To understand this, [35:44] let us again go to the VS code. Here the [35:47] first approach is single assignment that [35:49] we have already discussed. You can [35:51] assign a value to the variable using the [35:54] equal to operator means x equal to 10. [35:57] So here value 10 is assigned to the [36:00] variable x and it is the single value [36:02] that's why it is known as the single [36:04] assignment. Second approach is the [36:06] multiple assignment. Here you can assign [36:08] values to multiple variables in one [36:11] line. That means if I want to assign [36:14] value a= 1, b = 2, c= 3. So here you can [36:20] see to assign values to these variables [36:22] we need to write the three different [36:24] lines. In place of that you can use the [36:26] multiple assignment like this you can [36:28] write a comma b comma c equal to 1a 2a [36:33] 3. So it is having the same meaning as [36:36] the abolines that is a= 1 b= 2 and c= 3. [36:41] Now the next approach is you can also [36:44] assign a single value to multiple [36:46] variables. So suppose I want to assign [36:49] value 100 to variable x y and z. So we [36:52] can write x = y = z = 100. So here you [36:57] can use these different approach of the [36:59] assignment based on your requirement. [37:01] Now as you have observed till now python [37:03] variables can hold different data types [37:06] such as numeric data types, string data [37:09] types, boolean data types and collection [37:12] data types. So let us understand these [37:14] data types in detail. So from these [37:16] variable data types, the first one is [37:19] numeric types. Numeric type data types [37:21] means it can hold the numeric values and [37:24] this data type being int, float and [37:28] complex. So these are the inbuilt [37:30] reserved keywords to represent the [37:32] integer, float and complex values. [37:35] Suppose I want to store integer value is [37:37] 10. So num equal to 10. Here Python [37:41] consider it as the integer data. If I'm [37:44] storing pi = 3.14 as this one is being [37:48] the value in point. So Python consider [37:51] it as plot. Suppose we want to store the [37:54] complex number. So let us give the [37:56] variable name as complex number equal to [37:59] if I'm storing 2 + 3 J. So here Python [38:02] consider it as the complex number. So [38:05] these are the three different data types [38:07] available in the Python for the numeric [38:10] values. Now the second data type is [38:12] string. So string is nothing but the [38:15] sequence of characters enclosed in a [38:17] quotes. It means if we want to store the [38:20] name Python. So we need to write a name [38:22] equal to within double quotes Python. So [38:26] whatever the sequence of characters we [38:28] want to store, we need to compulsory [38:31] provide these values between the double [38:33] quotes. It is the compulsory syntax to [38:35] store this string type data. Third type [38:38] is the boolean. So boolean simply [38:41] represents the true or false. So suppose [38:44] in any of my program if I want to [38:46] represent switch is on yes or no. So for [38:50] that we can store it into the boolean [38:52] data type indicating true or false. So [38:54] suppose if I'm storing it under the [38:57] variable name is own equal to if I want [39:01] to say yes then we will store it as the [39:04] true. You may also store it as the false [39:07] based on your need. Fourth important [39:09] data type is the collection that is the [39:12] collection types and it includes list, [39:16] tpple, dictionary and set. If we want to [39:20] store more than one values in a single [39:23] variable, then these different data [39:25] types are useful. Suppose I want to [39:28] store numbers such as 1 2 3 in a one [39:31] variable only. So let us say the [39:32] variable is numbers equal to I want to [39:35] store the values 1 2 and 3. So to store [39:38] it in the format of list you need to [39:41] first provide the square brackets within [39:43] that provide the multiple values using [39:45] the comma separation. So if I am [39:48] entering 1 2 and three here these three [39:50] values are stored in the variable [39:53] numbers and square bracket represent it [39:56] as the list. Now suppose I want to store [39:59] the coordinates in the double type. So [40:01] for that I need to use the round [40:03] brackets and within round brackets you [40:06] can provide the required values. So [40:08] suppose I am storing the coordinate [40:10] being the one. So round brackets [40:12] consider it as the t. Next important [40:15] type is the set and set is useful to [40:18] store the unique values means if we do [40:20] not want any repetition in the values. [40:22] So at that time this set type is useful. [40:25] So set always represent the unique [40:27] values. So let us keep the variable name [40:29] as unique values equal to to represent [40:32] the set we need to use the curly [40:34] brackets and within which again we will [40:36] provide our values. So curly bracket [40:38] represent the set. The last type is the [40:41] dictionary. So dictionary is the [40:44] important type in which the data is [40:46] available in the format of key and [40:48] value. That means with one specific [40:51] entity if we want to represent its [40:54] specific value then this dictionary is [40:56] useful. So let us take the example if we [40:59] want to store multiple data of a person [41:02] in a single variable then this [41:03] dictionary is useful. So let us say I [41:06] want to store the name of a person and [41:08] age of a person in a single variable. So [41:10] in such case dictionary is useful. So [41:13] let us take the variable name is person. [41:15] To represent the dictionary again we [41:17] need to provide the curly brackets [41:19] within this. You can provide multiple [41:22] data types based on your requirement [41:24] using the comma separation. So let us [41:26] say I want to store the name. So I will [41:28] provide the variable name being the [41:30] name. Then we'll separate it with the [41:33] symbol colon. Now we need to provide the [41:35] name. Which name we want to store? [41:37] Suppose I want to store Python. So I [41:40] will type Python. So here this Python [41:43] name is assigned to the name. Second I [41:47] want to provide the age. So I will [41:49] provide the comma to separate the second [41:51] data. Now I want to store it into the [41:53] variable age. So again take age. Next we [41:57] need to store its value. So again [41:58] separate it with a colon and we'll [42:00] provide the value to be 30. So here you [42:03] can see Python is stored in the name and [42:06] value 30 is stored in the age. So this [42:09] type of data type is known as the [42:11] dictionary. So these are the four [42:13] different data types that are the way [42:15] more useful in your different programs [42:17] based on your requirements. You may [42:20] practice the separately for each [42:22] different data types based on your [42:24] requirement. And here you can see that [42:26] we do not need to specify the specific [42:28] data type based on the value. Python [42:30] automatically detects which data type we [42:32] have provided. So we need to be careful [42:34] with the syntax of different data types [42:37] as Python detects the data type based on [42:39] that specific syntax. I hope you got the [42:42] idea about different variables, how to [42:45] provide the name to the variables and [42:47] what are the different rules to provide [42:50] the variable names. Looking forward to [42:52] see you all in our further sessions. [42:54] Till then, thank you. Hello everyone. In [42:57] this video, we will understand what is [42:59] the meaning of Python literal. Literal [43:02] are constant values that are directly [43:04] written in the code. They represent [43:06] fixed data and are assigned to variables [43:09] or used in expressions. To understand [43:11] this, let us go to this visual code. [43:14] Here you can see we have stored multiple [43:17] values in the variables in our previous [43:19] session. So here you can see again x= to [43:22] y= z equal to 100. So this value 100 is [43:25] known as the lit. There are different [43:27] types of lit are there. Let us say the [43:30] first is the numeric lit. Here num equal [43:33] to 10. So this 10 represent the whole [43:35] number and it is the integer lit. I [43:38] equal to 3.14. So it is the numbers with [43:42] decimal. So it is known as the floating [43:44] point lit or the float lit. In the [43:47] complex number value stored is 2 + 3 J. [43:51] So these number 2 + 3 J is known as the [43:54] complex lit which represents the real [43:57] and imaginary part. String lit represent [44:00] the text and are enclosed in single, [44:03] double or triple quotes. Now what is the [44:06] meaning of that? Let us again understand [44:08] with the example. As you can see here, [44:10] we have stored the name equal to P. We [44:13] have stored it with the double quotes. [44:16] Same way you can store string literal [44:19] using the single or triple quotes also. [44:21] So suppose I want to store hello in a [44:23] single line. So I will type single line [44:26] being the variable name and within the [44:29] single quote hello. So again this value [44:32] hello is stored in the variable single [44:34] line. But suppose I want to store string [44:37] data in multiple lines then we need to [44:39] use the triple quotes. So suppose I want [44:42] to store in multi-line. So keep the name [44:44] of variable to be the multi-line equal [44:47] to. We will use the triple quotes [44:49] provide triple quotes. Hit enter. And [44:52] now between these triple quotes you can [44:54] type any of your required line. So let [44:57] us type this is multi-line [45:00] string. So here you can see we have [45:02] stored the value in multiple lines using [45:05] a single variable. So these are the [45:08] three different approach to represent [45:09] the text using the string lit and that [45:12] are enclosed in single, double or triple [45:14] quotes. Next is the boolean lit. Here [45:17] you can represent true or false. So [45:20] these value true or false represents the [45:22] boolean lit. Next is the collection lit. [45:25] It is used for creating list, tpples, [45:28] dictionary and sets. So again you can [45:31] see here the value stored between the [45:33] square bracket, round brackets and the [45:35] curly brackets. These all are known as [45:36] the collection lit. There is one more [45:39] special lit is there and it is the none [45:43] lit which represents the absence of [45:46] value. So suppose if I am storing data [45:48] equal to none. So here none represents [45:51] that there is absence of value in the [45:54] variable data. So it is known as the [45:57] special letter. I hope you got the idea [46:00] what is the meaning of literal in the [46:02] Python context. The variables in Python [46:05] are dynamic in nature. It means if I am [46:08] storing the value a equal to 10. So the [46:12] current data type of variable a is [46:14] integer. But next if I am storing a [46:17] equal to within double quote hello. So [46:20] now the data type of variable a is being [46:23] string. So this is the beauty of using [46:25] Python language and the easiness of [46:28] using this Python language. So this [46:31] understanding of variables and literal [46:33] is crucial for building efficient Python [46:36] programs. Variables act as a flexible [46:39] container while literal provide a fixed [46:41] data values. With this knowledge you can [46:44] efficiently store, manipulate or [46:46] retrieve data in your further Python [46:49] programs. Hello everyone. In this video [46:52] we will start discussion regarding to [46:54] the Python type conversion. Type [46:56] conversion in Python refers to the [46:58] process of converting a value from one [47:00] data type to another. It plays a crucial [47:03] role in Python programming by ensuring [47:05] compatibility between different data [47:07] types during operations or function [47:10] calls. So this video provides a [47:12] comprehensive explanation of type [47:13] conversion, its types, methods and its [47:16] some of the practical examples. So why [47:18] type conversion is required? Type [47:20] conversion is required to ensure [47:22] compatibility during operations between [47:25] different data types. It is used to [47:27] manipulate or format data for specific [47:30] requirements and it is also used to [47:32] handle user inputs which is often [47:34] treated as a string and we can convert [47:36] these into any other required data type [47:38] using the concept of type conversion. So [47:41] to understand this let us go to this VS [47:43] code create a new file for the practice [47:46] purpose. Let us take the example x= 10. [47:49] So here we know that it is of data type [47:52] integer. Now suppose I want to convert [47:55] it into the float data type. So we will [47:57] write another variable y equal to the [48:01] method is first type required data type. [48:03] So our required data type is float. Now [48:06] then after provide the round brackets [48:08] and within round bracket you need to [48:10] provide the variable or the value which [48:13] you want to convert. So let us type x. [48:16] So here with this syntax variable x is [48:19] converted to float. So it is called the [48:22] type conversion. Let us print the y and [48:25] check the value of y. Run the program. [48:28] So here you can see that in the terminal [48:30] we are getting the output 10.0. So this [48:33] decimal value represents the integer is [48:36] converted to the float. Again let us [48:38] take some of the examples. Suppose the [48:40] value of a is 20. So this value is [48:44] available in the string data type and we [48:47] want to convert this string into the [48:48] integer. So let us take variable B equal [48:52] to first type the data type that is int [48:54] and within bracket we will pass the [48:56] value A. So here string A will be [48:59] converted to the integer B. So I hope [49:02] you got the idea. This type conversion [49:05] is used to manage the different data [49:07] types based on the operations. Python [49:09] supports two types of type conversion. [49:11] First one is the implicit type [49:14] conversion. Let us mention here implicit [49:16] type conversion. Implicit type [49:18] conversion means Python automatically [49:21] converts one data type to another. No [49:24] other instruction is required from the [49:26] programmer. So typically it occurs in [49:28] operations involving different data [49:30] types. So let us say if I'm considering [49:33] x equal to 10. So here you can see that [49:36] the default data type provided to this [49:38] 10 will be the integer. Next if I am [49:41] writing y equal to 3.5. So here the data [49:45] type of the y will be float. Now let us [49:48] try to sum these. So result equal to x + [49:52] y. So here you can see that x is the [49:54] integer data type. Y is the float data [49:57] type. Whenever we are adding these two [49:59] integer plus float will be automatically [50:02] converted to the float. So the value or [50:05] the data type float will be assigned to [50:07] this result variable. Let us print the [50:10] result. So here when we run the program [50:12] you can see that the output is 13.5. It [50:15] represents that the data type assigned [50:17] to the result is the float data types. [50:20] Python has automatically assigned the [50:22] required data type to our variable and [50:25] it is due to the implicit type casting. [50:28] To check the type of variable you can [50:30] make use of the function type and within [50:32] bracket pass the variable that is x. We [50:36] want to print the output of data type on [50:38] the screen. So again we will write these [50:40] function within the print function. So [50:42] print type x. Now run the program. So [50:46] here you can see that the class is int. [50:48] That means the data type provided to the [50:50] x variable is integer. Same way if you [50:54] want to check the data type of result. [50:56] So again type print within bracket. Type [50:59] within bracket result. Again run the [51:02] program. And here you will see that the [51:04] type provided is the floppy. So I hope [51:06] you got the idea. The implicit type [51:08] conversion means the automatic type [51:10] conversion that Python performs [51:12] automatically. Second one is the [51:14] explicit type casting. Now it is also [51:17] called the type casting and it is [51:19] performed manually using the built-in [51:21] functions and it allows the programmer [51:23] to control how the data is converted. [51:26] There are some common functions are [51:27] available for explicit conversion. So [51:30] first let us refer to that common [51:32] conversions. So here you can see that in [51:35] the table first function is the int and [51:37] it converts the data type to the integer [51:40] means in the example as you can see if I [51:42] want to convert 1 2 3 into the integer [51:44] then I need to type int and within [51:47] bracket I will provide the variable or [51:49] the value which I want to convert. So [51:51] similarly you will find different [51:54] functions for the explicit conversions. [51:56] So to remember that you can simply keep [51:58] in mind that you need to have the exact [52:00] idea about the name of data type. Let us [52:03] say int, float, string, list, tuple, set [52:06] and bool. And based on that name only [52:08] these functions are created. So you may [52:10] be able to easily remember these [52:12] functions based on your need. Let us [52:15] understand this explicit conversion with [52:17] the examples. So again go to this VS [52:19] code. Now suppose I am storing the value [52:21] pi equal to 3.4. Now I want to convert [52:25] this float variable into the string [52:28] variable that means I want to change the [52:30] data type. So for that we need to use [52:32] the concept of explicit type casting. [52:35] Suppose I want to store it into the Z [52:37] variable. So Z equal to now to convert [52:39] it into the string we will type first [52:42] str inbuilt function. Then within round [52:45] bracket we will provide our variable [52:47] that is the pi. So now here this value [52:49] pi will be converted to the string. Let [52:52] us print zed and run the program. So [52:55] here you can see that at the bottom the [52:57] value printed is the 3.14 but it is of [53:00] type string. To verify that again type [53:03] print type and within bracket z again [53:07] run the program and here in the output [53:10] you can see that the class is string [53:12] means now our float value is converted [53:15] to the string. Let us again take another [53:17] example. Suppose the value a1 equal to [53:20] zero. Now I want to convert this zero [53:22] into the boolean variable and the [53:24] boolean data type that's why let us take [53:27] variable B1 equal to convert it into the [53:31] boolean variable we will type bool [53:33] within bracket value that is the A1. Now [53:36] to print it let us type print B1 and to [53:39] print its type also we will type print [53:42] type and B1 and run the program. So here [53:46] you can see that the zero refers to the [53:48] false value and the data type is the [53:50] boolean. So I hope you got the idea how [53:53] this type casting is useful and there [53:55] are two types of type casting that is [53:57] the explicit and the implicit type [53:59] casting. So the important points to [54:01] remembers are first is type conversion [54:03] is the conversion of an object from one [54:05] data type to another data type. Implicit [54:08] type casting conversion is automatically [54:10] performed by the Python interpreter. [54:12] Python avoids the loss of data in [54:14] implicit type casting. Explicit type [54:17] casting is also called only type casting [54:20] and the data types of objects are [54:22] converted using predefined functions by [54:24] the user. In type casting loss of data [54:28] may also occur as we enforce the object [54:30] to specific data type. So the meaning of [54:32] that is suppose as you can see we have [54:35] provided the variable pi that is equal [54:38] to 3.14. I want to convert it into the [54:41] integer. So let us type C1 equal to int [54:45] 5. So here you can see that we are [54:47] trying to convert the decimal value into [54:49] the integer value. That means in place [54:51] of 3.14 we will get only output three. [54:54] So this 14 is nothing but the loss of [54:57] data. So there may be a chances that due [55:00] to the explicit type casting if we don't [55:02] know exact requirement of our program [55:05] then there might be a possibility that [55:06] we may lose some of the important data. [55:08] So let us check the output again. Type a [55:11] print and see what run the program. So [55:14] here you can see that the output is [55:16] three. That means the other decimal [55:18] values are lost due to this type [55:20] casting. I hope this type casting [55:22] concept is helpful to you and you are [55:24] getting the exact important details that [55:26] are needed for our further programs. [55:29] Looking forward to see you all in our [55:31] further sessions. Till then thank you. [55:33] Hello everyone. In this video, we will [55:36] understand use of basic input and output [55:39] in Python. Basic input and output [55:41] operations in Python allow interaction [55:43] between the program and the user. Input [55:46] lets the program receive data from the [55:48] user while output displays the data to [55:51] the user. These operations form the [55:53] backbone of creating interactive and [55:55] dynamic Python applications. This video [55:58] explores how to use output functions [56:00] effectively including formatting [56:02] techniques and practical examples. [56:05] Python uses the print function to [56:07] display output to the user. Now the [56:10] syntax of print is as you can see on the [56:12] screen. First you need to tap the print [56:14] within round bracket. You can provide [56:17] three different values. First is object. [56:20] Object refers to the one or more items [56:22] to be printed on the screen. Second is [56:25] the separator and for that you need to [56:27] type se equal to. Then within single [56:30] quote or double quote you can insert the [56:34] object with which you want to separate [56:36] the multiple objects and the default [56:38] value provided is a space. Third is the [56:41] end. It defines how the final data is [56:44] printed at the end of your statement [56:47] based on the tool or the value provided [56:49] over here. So here the string is [56:51] appended after the last object and the [56:54] default value is the new line. That [56:56] means once you print the data then after [56:59] new line will be created and the next [57:01] data will be printed on the new line. [57:04] Now to understand this print function in [57:06] detail let us go to VS code. You may [57:09] have already used print function in our [57:12] previous programs also but now here we [57:14] will understand the different use and [57:16] the different methodology how we can use [57:18] this print function. So the first method [57:20] is you can simply type print and within [57:23] which within double quote whatever data [57:26] you want to print you can just provide [57:28] in between this double quote. So here [57:30] currently we have written print within [57:32] double quote python world. So once we [57:34] run this program we will get the output [57:37] python word you can see on the screen. [57:40] Second advantage is you can print [57:42] multiple objects also. Let us take the [57:45] second example and comment this data. So [57:48] here you can see that we have printed [57:50] four different entities. First name, [57:53] John, comma, age, comma, 30. So this [57:57] comma separator combines all these four [58:00] different words into a one single line. [58:02] So again run the program and here you [58:05] see that the output is name, John and [58:08] age 30. And between each object you can [58:12] see that the default space is provided. [58:14] Third is you can make use of the custom [58:17] separator. So let us have a look at the [58:19] third example. Here you can see that we [58:22] have printed three different words. [58:25] Python comma is comma 1. Here we have [58:28] used the separator. So to use the [58:30] separator we need to type se equal to [58:34] then within single or double quote you [58:36] can provide your separator. We want to [58:38] separate each word with the dash. So [58:41] here we have provided dash. run the [58:43] program and see the output. So at the [58:45] bottom you can see that the python dash [58:47] is -1. So each word in place of a space [58:51] separated by the dash. Other than these [58:54] you can also provide different escape [58:56] characters based on your need. So what [58:58] are the escape characters? Escape [59:00] characters are special characters used [59:03] to format the output. So here the [59:06] default syntax are backward slash n [59:09] represents new line backward/ t [59:12] represents the tab two backward slash [59:14] represents the back slash single [59:16] backward slash with a single quote and [59:18] the double quote represents the use of [59:20] single and double quote. So these are [59:23] the standard syntax you may utilize [59:25] based on your format. So let us again go [59:27] back to VS code. Now consider the same [59:31] example. Here the data printed is Python [59:33] is one but here I want to separate it [59:36] with the tab means the space similar to [59:38] the tab will be provided between each [59:41] word. So type within separator backward/ [59:44] e again run the program. So here you can [59:47] see that the space provided python is [59:51] one. Same way suppose I want to print in [59:54] the new line. So you can use the [59:56] separator / n. So change the value to [59:58] backward SL and run the program. Here at [60:02] the bottom you can see the output Python [60:04] is one. So each word is separated by a [60:08] new line. Fourth use is you can make use [60:10] of the custom end characters also. So [60:13] for that consider the next example. Here [60:17] you can see that first we have printed [60:18] hello and then we have provided the end [60:21] character to be the comma. That means [60:23] after end of this print statement the [60:25] comma will be provided. The next [60:27] statement is world. So now between these [60:30] two end point used is the comma. Run the [60:33] program. So at the bottom you can again [60:35] see the output. Hello. Comma is provided [60:38] due to the end and the word. Now again [60:41] here you can provide any of the escape [60:43] characters based on your need or you may [60:45] change any required formatting data [60:47] based on your requirement. Next we will [60:50] understand how we can format the string [60:52] properly for the proper visualization [60:54] using some of the functionalities. So [60:57] the first use is using the f string [61:00] function. Consider the example for the [61:02] same. So say the name is stored as John [61:07] and the age is provided 25. Now I want [61:10] to print John is 25 years old. But I do [61:14] not want to provide the fixed value but [61:16] I want to print based on the variable [61:18] values because there might be a [61:20] possibility the name and the age may [61:22] change based on the user input or the [61:24] variable values. So how we can use so [61:27] first you need to type the print then to [61:29] use the fing first type f then now [61:32] within double quote you need to use your [61:35] required string and the curly brackets. [61:38] So suppose I want to print first name. [61:40] Now these refers to the variable. So to [61:43] refer the variable we will write that [61:46] variable within curly bracket. So within [61:48] curly bracket provided the variable [61:50] name. Next string that is is now I want [61:53] to provide the age. So again it is [61:55] variable. So we will type within the [61:57] curly brackets. So within curly bracket [61:59] age is provided. And the next statement [62:01] is years old. That means whatever name [62:04] we are providing and the whatever age we [62:06] are providing accordingly this printing [62:08] statement will change. So again run the [62:11] program. So here you will see the output [62:13] John is 25 years old. Now there may be a [62:16] possibility in place of John we are [62:19] providing the name to be Jane and the [62:21] age to be 35. So now printing statement [62:24] will remain same but based on the [62:26] variable name and age will change [62:29] automatically based on the inputs. So [62:31] again run the program and here you can [62:34] see the output JN is 35 years old. So [62:37] this is the beauty of using fstring [62:39] functionality to provide proper [62:41] formatting to your output. Second method [62:43] is using format method. So consider its [62:47] example again. Here you can see that [62:49] first variable name one is Bob. Second [62:52] variable name two is Jen. Now there is [62:55] another possibility that you do not want [62:57] to provide the variable's name in [62:58] between but first you provide the syntax [63:01] for the same and then at the end you [63:03] want to define the variable names. Let [63:05] us take the example. Suppose I want to [63:07] print hello Bob I am Jen. So as you can [63:11] see the Bob is stored in the name one [63:13] and the Jane is stored in the name two. [63:16] So we will write print within double [63:18] quotes hello. We want to provide the [63:21] name that is Bob. But it is in this [63:24] variable. So we will provide only curly [63:26] brackets. Comma I am Jen. Again Jen is [63:29] stored in the name two variable. So we [63:31] will only provide the curly bracket. We [63:34] will complete our string with the double [63:35] quotes. Now in place of curly brackets, [63:38] we need to provide which value will go [63:41] in curly bracket. But we need to provide [63:43] these values in the same sequence as the [63:46] curly brackets provided. Means as I want [63:49] to type first Bob in the first curly [63:51] bracket. So we will type dot format and [63:54] within round bracket we will provide the [63:57] number of variables based on the curly [63:59] brackets available. So first we want to [64:02] provide the Bob. So here we will write [64:04] dot format name 1 comma for the second [64:07] curly bracket we will provide the name [64:09] two. So here in the first curly bracket [64:12] Bob will be passed and in the second [64:15] curly bracket name two that is the gen [64:17] will be passed. So again run the [64:19] program. So you can see that the output [64:21] is hello Bob I am Jen. But now suppose [64:24] if you have changed dot format first you [64:27] have provided name two and then you have [64:29] provided name one. So now in the first [64:32] curly bracket name two will go and in [64:35] the second curly bracket name one will [64:37] go. So now run the program. So you can [64:39] see the output hello gen. So we need to [64:43] keep in mind in which curly bracket we [64:45] need to provide which variable. So [64:47] accordingly we need to manage the [64:49] sequence. So these are the different [64:51] methods available to format your [64:53] outputs. Now same way as we have [64:56] understood there are different escape [64:57] characters are available between the [64:59] strings. You can also use different [65:02] escape characters. Suppose I want to [65:04] print Python world. But here I want to [65:07] separate Python and world with the new [65:11] line. So for that just we will provide [65:13] escape character over here. Backward sl. [65:16] So this backward/n will provide the new [65:18] line between these two words. So run the [65:21] program. So at the bottom you can say [65:23] that the python and world both are [65:25] separated by the new line. If you want [65:27] to provide the space as much as tab then [65:30] type / t again run the program. So now [65:34] here you can see that the space is [65:36] provided that much of tab. So I hope you [65:39] got the idea how to use this output [65:41] functionality using the print function [65:43] in Python programming. Hello everyone in [65:47] this video we will understand Python [65:49] input. So Python uses the input function [65:52] to take input from the user. To [65:55] understand the syntax of input function [65:57] go to the VS code. So the syntax of the [66:00] Python input is first you need to type [66:03] the input and then within bracket you [66:06] will provide the prompt. So what is the [66:08] meaning of prompt? Prompt is a optional [66:10] text that is displayed to the user [66:13] before taking the input. So suppose I am [66:15] typing enter your name. So that means on [66:19] the screen user will be displayed that [66:21] enter your name and after this user will [66:24] be asked to enter the name on the [66:27] screen. Now this is the method how we [66:29] will take the input but where this input [66:31] will be stored. So this always returns [66:33] the input as a string. We need to store [66:36] the answer by default into the string [66:38] data type. So suppose we want to store [66:41] it into the variable name. So type name [66:44] equal to input enter your name. So [66:47] whatever the entry user will be entering [66:49] that will be stored into the name [66:51] variable and then after we want to print [66:54] let us say if hello and then name of [66:56] that person. So within curly bracket we [66:59] will type name. So here we have used the [67:01] string formatter to print the data. Now [67:04] run the program. So as you can see on [67:07] the screen you will be asked to enter [67:09] your name. Enter your required name. So [67:12] I'm entering John and hit enter. So here [67:15] you can see that whatever the entry user [67:17] has entered that value will be passed [67:20] into the variable name and this name [67:22] will be passed into our print function. [67:24] So we got the output hello John here. [67:27] Keep in mind that the default data type [67:29] of the input function is the string. So [67:32] always the output will be given in the [67:34] string data type. But it is not always [67:36] required that we want the string output. [67:38] There may be a necessity that we want to [67:41] have some numbers and we want to perform [67:42] some of the mathematical operations. For [67:45] this we need to use the concept of type [67:47] conversion. We already know how to [67:50] convert the data type from one data type [67:52] to another data type. Let us take the [67:54] another input age equal to input and the [67:59] prompt provided will be enter your age. [68:02] Now here the data type of the age will [68:04] be string. To check the data type write [68:07] print type and variable name age. Now we [68:11] want to convert these data type string [68:14] to the integer data type. So for that we [68:16] will use the explicit type casting. So [68:19] we will type again we want to store it [68:21] into the same variable. So age equal to [68:24] first required data type int and within [68:27] bracket again age. So this statement [68:30] will convert string data type to the [68:32] integer data type. Again let us check [68:34] the data type. So print type and the [68:37] variable age. Now run the program. First [68:40] enter the name. Name being the zone and [68:43] the age let us say 30 and hit enter. So [68:47] here you can see that the first data [68:49] type is string and using the explicit [68:51] type casting we have converted into the [68:53] int data type. It is not always [68:56] necessary to separately type cast the [68:59] required data type into the different [69:00] line. You may perform the same task in [69:02] the same line also. So that means [69:05] suppose I want to convert these user [69:08] entered age into the integer data type. [69:10] We may reduce these lines and directly [69:13] type age equal to we want to convert it [69:16] into the int data type and then within [69:18] round bracket we will provide all the [69:20] data. So directly it will convert the [69:23] string data type into the integer data [69:25] type and it will be stored into the age [69:28] variable. So this is the methodology to [69:30] take the single user input but there may [69:33] be a chances that we want to take [69:35] multiple inputs also. So for that we [69:38] need to use split method also. So the [69:41] method name is split and by default this [69:44] method splits the data from white space [69:48] means in your statement whenever you are [69:51] providing the space from that part it is [69:54] splitting the data. To understand it, [69:56] let us take the example. Before that, [69:58] first comment all the previous data so [70:01] that we can focus on our next part. Now [70:04] I want to take the input for two [70:06] variable XY equal to. We want to take [70:09] the input. So type input. Here we will [70:12] specifically say enter two numbers [70:14] separated by space. And to separate the [70:18] data after the input method, we will use [70:21] the method split. So we will type [70:23] dotsplit and the round brackets. [70:25] Whenever I am saying the term method [70:28] that means we need to provide the round [70:29] brackets at the end of that name. Now we [70:32] want to print both the values. So type [70:34] print within round bracket we will use [70:37] the formatted string. So if first value [70:40] being the x and the second value being [70:43] the y. Now run the program. Here we have [70:46] been asked to provide the two numbers. [70:48] So first number being the 10 space 20. [70:51] Once we hit the enter, this 10 and 20 [70:54] gets separated from the space and 10 [70:57] value will be stored into the x and 20 [70:59] value will be stored into the y. So hit [71:02] enter and here you can see that the [71:04] first is 10 and the second is 20. Now we [71:07] already know the default data type of [71:10] the input method is the string. So [71:12] suppose if we perform addition on this [71:15] variable then they will be treated as [71:17] the string. So let us type x + y and [71:21] again run the program. Enter the values [71:24] 10 20. So you will get the output 10 20. [71:28] As these are the string these two [71:30] elements get combined but these two are [71:32] not added as Python is treating it as [71:35] the different string or the text. Now to [71:38] perform the summation we need to convert [71:40] these values into the integer data type. [71:43] Now to perform the summation let us [71:45] convert x and y into the integer. So x = [71:49] intx and y = int y. Now we will print [71:53] the sum. So let us type print sum is [71:56] within double quote. We will separate it [71:59] with the variables with the comma. Now [72:01] run the program again. Enter the two [72:04] numbers. Let us type 10 space 20. And [72:07] now you will get the output sum is 30. [72:10] So using this input function you may [72:13] take single value as a input or you can [72:15] also take multiple values as a input [72:17] based on your requirement. So this input [72:20] output functionality is too much helpful [72:22] to make your programs interactive while [72:24] you are working with the realtime [72:26] problems. Now with the completion of all [72:29] these concepts you are ready to move [72:31] with the next step of the Python [72:33] programming that is the operators and [72:34] the expressions in Python programming. [72:37] So we will start our discussion onto [72:39] that topics from our further session [72:41] onwards. So stay connected with me in [72:43] the further sessions. Till then thank [72:45] you. Hello everyone. In this video we [72:48] will start our discussion regarding [72:50] Python operators and expressions. [72:52] Operators and expressions are [72:54] fundamental components of Python [72:56] programming. Operators perform [72:58] operations on variables and values while [73:01] expressions combine variables, operators [73:04] and literal to produce result. From this [73:07] video onwards, we will provide a [73:09] comprehensive explanation of Python [73:11] operators, data types and how they are [73:14] used in expressions with examples. So [73:17] what is operator? An operator is a [73:19] symbol or keyword used to perform [73:22] specific operations on one or more [73:24] variables or values. There are various [73:27] types of operators are available. From [73:29] that we will start our discussion with [73:30] the first one that is arithmetic [73:33] operators. You may have already [73:35] performed these arithmetic operations as [73:38] a part of mathematics. So these are [73:40] addition, subtraction, multiplication, [73:43] division. Now some of the new ones are [73:45] floor division, modulus and the [73:47] exponents. So in the first section as [73:50] you can see here the different symbols [73:53] that we will be using are provided that [73:55] are known as the operators. In the first [73:57] column we will see plus, minus, [73:59] multiplication. For the division we will [74:02] use forward slash whereas for the floor [74:04] division we will use two forward slash. [74:07] The modulus which will give us the [74:09] remainder for that we will use the [74:11] symbol percentage. And to get the power [74:13] or the exponents we will use the 2 * [74:16] star symbol to perform that operation. [74:19] So in the example also you can see [74:21] addition, subtraction, multiplication [74:23] and division provides the respective [74:26] answer. But in division keep in mind [74:28] that it will provide the float value. [74:30] The meaning of floor division is that we [74:32] will only get the quotient value without [74:34] the decimal. So if we perform 10 / 3 the [74:38] exact answer will be 3.333. But [74:41] performing the floor division we will [74:43] get only the integer number that is 3. [74:46] Modulus will give us the remainder. So [74:48] 10 modulo 3 that is equal to 1. that one [74:51] represents the remainder and the [74:53] exponent that is 2 start 3 means 2 to 3. [74:58] So that will give us the output 8. So [75:00] let us understand all these arithmetic [75:02] operators in the program. For that go to [75:05] the VHS code. We have already created a [75:08] file for your reference. So as you can [75:10] see we have provided the fixed value num [75:13] one being the 10 num 2 3. Then we have [75:16] printed both the numbers first number [75:18] and the second number. Now first if you [75:20] want to perform the addition so we have [75:22] stored it into the addition variable. So [75:25] num 1 + num 2 that will give us the [75:27] summation 10 + 3 that will be 13. So [75:31] here our output will be 13. Next [75:34] subtraction. So num 1 minus num 2 will [75:37] give us the output 7. Third [75:40] multiplication. So num 1 into num2 [75:43] stored into multiplication variable. And [75:45] here we will get the output 30. Next is [75:48] the division. Keep in mind that division [75:50] will give us the output in float data [75:53] type. So here 10 / 3 will give us the [75:55] output 3.333. [75:58] Next is the floor division. So we will [76:00] write num 1 2 * forward/ num 2 and it [76:04] will give us the quotient value in [76:06] integer. So 10 / 3 it will give us the [76:09] output three. Next is the modulus or [76:12] remainder. So for that we will write [76:14] modulus equal to num 1 modulo num two. [76:17] So 10 modulo 3 will give us the [76:19] remainder and the remainder value being [76:21] the one. Now last arithmetic operator is [76:24] exponent and for the exponent we will [76:26] use num 1* star num 2 that means num 1 [76:31] to num 2. So here we will get 10 to 3. [76:34] So our output will be 1,000. So I hope [76:37] you got the idea how these different [76:39] symbols we need to use to perform some [76:41] of the arithmetic operations. Now run [76:44] the program. So here you can see that [76:46] first number 10 second number three and [76:49] accordingly we have performed the [76:50] operation addition, subtraction, [76:52] multiplication, division, floor [76:54] division, modulus and the exponents. [76:57] These arithmetic operations will be [76:59] helpful to you while performing some of [77:01] the mathematical operations based on [77:02] your requirement. Next we will [77:05] understand the logical operator. So for [77:07] that again go back to this slide. So [77:09] these logical operators are used to [77:12] combine conditional statements. So as [77:14] you can see in the example we will be [77:16] using the three different operators [77:18] first and then or and the not. So these [77:22] are the inbuilt keyword that we will be [77:24] using in Python to perform the logical [77:26] operations. As you can see in the [77:28] example these and or and not example [77:31] works based on the conditional [77:33] statements. As we know the end operator [77:36] represents that if any of the condition [77:39] is false then we will get the output [77:41] false. If both the conditions are true [77:44] then and then only we will get our [77:45] output to be true. Second in or operator [77:49] if any of the condition is true then we [77:52] will get the output true. If both [77:54] conditions are false then and then only [77:56] we will get our output false. And the [77:58] last one is the note operator. This [78:01] operator converts our output if it is [78:03] false then converts it into the true and [78:06] if our output is true then converts it [78:08] into the false. And here keep in mind [78:11] that the logical operators will always [78:13] provide output in the boolean data type. [78:16] So again we will go back to VS code to [78:18] understand these logical operators. As [78:21] you can see in the program we have [78:22] provided the fixed values. First is [78:24] true, second one is the false. We have [78:27] printed both the values to be condition [78:29] one, condition two. Here for the [78:32] reference we have assumed that condition [78:34] one is true whereas condition two is [78:36] false. Now in place of true or false in [78:39] a real example we will be having any [78:41] specific condition based on that we will [78:43] get the output. First is the logical and [78:46] so as you can see we have stored the [78:49] answer in variable result underscore end [78:51] and [clears throat] here we have used [78:52] the operator and so condition one and is [78:55] the inbuilt keyword and condition two. [78:58] So in our case condition one is true and [79:01] condition two is false. So true and [79:04] false as it is the end operator one of [79:06] the condition is false that means we [79:08] will get our output to be the false. So [79:11] here our output will be false. Second is [79:15] or operator. So condition one or [79:18] condition two. So true or false. So one [79:21] of the condition is true that's why we [79:23] will get our output to be true. Third [79:26] one is logical note. And here as you can [79:28] see first one is not condition one. [79:31] condition one is true and we have used [79:33] the not operator that means here our [79:36] output will be false and in the second [79:38] condition again not condition two means [79:41] our output will be true. So these [79:44] logical operators will be helpful to get [79:46] the output based on the combination of [79:49] different conditions as per our [79:51] requirement. So I hope you got the idea [79:53] how these logical operators might be [79:55] helpful to perform some of the [79:57] mathematical operations or the [79:59] conditional check to fulfill our [80:01] requirement. In this video we understood [80:04] assignment and logical operators in [80:06] detail. The remaining operators we will [80:08] understand in our further sessions. [80:11] Hello everyone. In this video we will [80:13] continue our discussion on operators and [80:15] we will continue with the comparison or [80:18] relational operators. So these operators [80:20] are used to compare values and return a [80:23] boolean result. With the use of these [80:25] operators, we will always get our output [80:27] in boolean data type. It may be either [80:30] true or false. On the first column, you [80:32] will find that there are specific [80:34] symbols you need to use to use [80:36] comparison operators. First is equal to, [80:40] second is not equal to. So you need to [80:42] use exclamatory mark and then equal to. [80:45] Remaining four are same as the [80:46] mathematical operators. So greater than, [80:48] less than, greater than equal to and [80:50] less than equal to. So if provided [80:53] values satisfy the given operator [80:55] condition then we will get the output [80:57] true else we will get the output false. [80:59] You may refer the examples provided here [81:01] to understand the output. We will go to [81:04] the VS code to again understand the use [81:06] of comparison operators. As you can see [81:09] in the example we have provided the [81:11] fixed values number 1 15 number two 10. [81:14] We have printed both the numbers first [81:16] and second number. Now first operator is [81:19] the equal to operator. As you can see we [81:21] have used the operator num one equal [81:24] equal num two. So here you can see that [81:26] 15 equal equal 10 and the answer is no. [81:30] So here we will get the output false. [81:33] Second operator is not equal to. So here [81:35] we have written num 1 exclamatory mark [81:38] equal to num two and 15 is not equal to [81:41] 10. So yes. So we will get the output [81:43] true. Next is the greater than operator. [81:46] So here num one is greater than num two. [81:48] Yes. So we will get the output true. [81:51] Fourth is num one less than num two. So [81:53] the answer is no. So we will get the [81:55] output false. Now the next one is [81:58] greater than or equal to. So num 1 is [82:00] greater than or equal to num two. Yes. [82:03] Num 1 is greater than num two. So here [82:05] we will get the output true. And the [82:07] last one is less than or equal to. So [82:10] here num 1 is less than num two. The [82:12] answer is no. So we will get the output [82:14] false. So as you can see based on the [82:17] provided values these operator checks if [82:20] both the values are same or it may be a [82:22] greater than less than or not equal to. [82:25] So if we want to perform any checks on [82:27] the numbers then at that point these [82:29] comparison operators are useful to get [82:32] the output. Let us run the program. So [82:35] in the output you can see that num 1 [82:37] equal to num two the answer is false. [82:40] Num not equal to num two answer is true. [82:42] num one greater than num two again [82:44] answer is true. So same way you can [82:46] check the answer as we have discussed [82:49] accordingly we will get the output. So [82:51] here output depends purely on the [82:53] provided values and based on that Python [82:56] will give the output. Next we will [82:59] understand the assignment operator. So [83:01] for that again go back to the slide. [83:04] These assignment operators are used to [83:06] assign values to the variables and there [83:08] are different methodologies to provide [83:10] the assignment. The first operator is [83:13] equal to. So it will directly assign the [83:15] value if we have written x equal to 5. [83:17] So these five value will be assigned to [83:20] x. Other than that we can also perform [83:23] arithmetic operations combining with the [83:25] assignment operators. So on the first [83:28] column again you can see we have [83:29] provided the operators combining with [83:31] the arithmetic operators. So first plus [83:34] equal to minus equal to multiply equal [83:36] to divide equal to floor divide and [83:39] assign exponent equal to and the modulo [83:42] equal to. So what is the meaning of this [83:44] symbol that is provided in the [83:46] description. So plus equal to refers to [83:48] that first we will add and then assign [83:51] the value. So as you can see in the [83:53] example we have written x += 3. So the [83:57] meaning of that is x = x + 3. Same way [84:01] if we have mentioned x -= 2 then the [84:03] meaning of that is x = x -2. Same way [84:07] you can understand the meaning of [84:09] remaining operators. So to understand [84:12] the output of these operators again let [84:14] us go back to the VS code in the [84:16] reference program you can see that first [84:18] we have provided the fixed value using [84:20] the assignment operator equal to. So num [84:22] equal to 10. So this is the initial [84:25] value. Now again we are using the [84:27] assignment operator and we have provided [84:29] num equal to 15. So now the new assigned [84:32] value to num variable is the 15. Next is [84:36] num plus equal to 5. So plus equal to 5 [84:40] means first we will add and then assign [84:42] the value. So the meaning of num plus [84:45] equal to 5 is num equal to num + 5. The [84:49] updated value of the num is 15. So 15 [84:52] +al 5 we will get the output 20. Next is [84:56] num minus= 3. So now our output will be [85:00] 20 - 3. So it is 17. Next is num mult*ly [85:05] equal to 2. So our output will be here [85:08] 17 into 2 that means 34. Next is divide [85:12] and assign. So num divide equal to 4. [85:15] That means 34ide by 4. So we will get [85:17] our output 8.5. Next is floor divide and [85:21] assign. So now floor divide equal to 2. [85:24] That means 8.5ide by 2. But here we will [85:28] get our output in the integer value [85:30] only. So our output will be four. Next [85:34] is modulus and assign. So num modulo [85:36] equal to 3 means four modulo 3. So here [85:40] our remainder will be 1. So our output [85:42] will be 1. And the last is exponent and [85:45] assign. So one exponent two. that means [85:49] 1 to2 and here our output will be 1 to2 [85:52] that is equal to 1. So again we will get [85:54] the output 1. So using these assignment [85:57] operators you can assign fixed value to [86:00] the variables also or you may perform [86:02] arithmetic operations with the [86:04] assignment also. To check the output run [86:07] the program and at the bottom you can [86:09] check the output. The initial value is [86:11] 10. New assigned value is 15. And [86:14] accordingly the discussed output you can [86:16] see on the screen. Here you can observe [86:19] that the divide and assign will provide [86:21] the output 8.5 and that is the float [86:24] value. And then after we have performed [86:27] any operation on this float value and [86:30] all the values we have received in the [86:32] float data type. So the answers are not [86:34] 4 1 and 1 but the answers are in float [86:36] data type that means 4.0 1.0 0 and 1.0. [86:41] So while getting the output again we [86:43] need to keep in mind that the implicit [86:46] type casting works here based on that we [86:48] are getting our output in the float data [86:50] type. Now next we will discuss about the [86:53] identity operator. [86:55] These identity operators are used to [86:57] check if two objects have the same [86:59] memory location. So the first operator [87:02] is is operator. The is operator checks [87:05] if two variable reference the same [87:08] object in the memory. That means if we [87:10] have created the two variables X and Y, [87:13] let us say these two are the objects. So [87:15] if these two variable are referencing to [87:17] the same memory location, then we will [87:19] get the output true. Else we will get [87:21] the output false. The second is is not [87:24] operator and it checks if two variable [87:27] reference the different objects or not. [87:30] If they refer to the different objects [87:33] then our output will be true else our [87:35] output will be false. So you can see [87:38] over here again these identity operators [87:40] will give us the output in boolean data [87:43] type only. That means it will be either [87:45] true or false. To understand these [87:47] identity operators again go back to VS [87:49] code. [87:51] In the program you can see that we have [87:54] provided the fixed variable x= 10 y= 10. [87:58] So these two variables are referring to [88:00] the value 10. The next is variable zed [88:03] that represents the list 1 2 3 and again [88:06] variable W refers to the list 1 2 3. So [88:10] here you can see that we have used the [88:13] collection type data that is the list [88:15] and so this Z and W variable refers to [88:18] the list. The collection type variable [88:21] refers to the object and keep in mind [88:23] that this collection type data though [88:26] values are being same every time created [88:29] new in the memory location. So Z and W [88:33] refers to the different memory location [88:35] though they are having the same value. [88:37] So after fixing these four values we [88:39] have printed all these four values and [88:41] next now first we have used the is [88:44] operator. So you can see that we have [88:46] used X is Y. So, Python will check if X [88:50] and Y refers to the same memory [88:52] location. So, here X and Y referring to [88:54] the variable. So, yes, these two are [88:57] referring to the same value only. So, [88:59] here we will get the output true. Next, [89:02] we have checked if Z is W. So, Z and W [89:06] are referring to the same values. But [89:08] these two are being the collection type. [89:10] So, the memory location of both these [89:12] will be different. So, we will get our [89:14] output to be false. Again keep in mind [89:17] that these identity operators are [89:20] checking the memory location of the [89:22] variables if they are same or not. Next [89:24] is is not operator. So first we have [89:27] checked X is not Y. But we have already [89:30] seen X and Y are referring to the same [89:33] value in the same memory location. So in [89:35] such case we will get our output false. [89:38] And the next is Z is not W. So yes Z is [89:41] not W. Both are referring to the [89:43] different locations. So here we will get [89:45] our output true. Now to check the output [89:48] again run the program. So in the [89:50] terminal you can check that X and Y are [89:53] referring to the same value or the same [89:55] memory location. So we are getting the [89:57] output true. Whereas Z and W are [89:59] referring to the different memory [90:00] location. So we are getting the output [90:02] false. So if anytime in our program if [90:05] you want to check that the variables [90:07] that we have created are referring to [90:09] the same memory location or not then we [90:11] may use these identity operators. [90:14] Go through all these different data [90:16] types again and practice them in the [90:18] separate files so that you may be [90:20] familiar with the output that you are [90:22] getting based on the different values. [90:25] Looking forward to see you all in our [90:27] further session. Till then thank you. [90:30] Hello everyone. In this video we will [90:32] understand operators precedence in [90:34] Python programming. In Python operator [90:36] precedence determines the order in which [90:39] operations are evaluated in expression. [90:41] Understanding the precedence helps avoid [90:44] logical errors and write efficient [90:46] bug-free code. Let us first see [90:48] operators precedence table. Here you can [90:50] see that the multiple operators are [90:53] provided in the column from highest to [90:55] lowest order. That means if we have [90:58] written any expression combining all [91:00] these different variables, values and [91:03] operators then first precedence will be [91:05] given to the parenthesis then to the [91:08] exponent and same way in the downside [91:10] you will find that from highest to [91:12] lowest order multiple operators are [91:15] provided. So while executing any [91:17] expressions Python is keeping in mind [91:19] these operators precedence to get the [91:22] output. Let us understand this [91:24] precedence with the example. So we will [91:26] go to the first example. Here you can [91:28] see that the expression is provided and [91:31] answer is stored into the result [91:33] variable. So here result equal to 5 + 10 [91:36] into 2 greater than 25 and 15 / 3 equal [91:41] 5. So here multiple operators are [91:43] combined in combination with the values. [91:46] So to get the output for this expression [91:48] we must need to follow the operator's [91:51] precedence based on the table. First we [91:53] need to perform multiplication operation [91:56] and the division operations. So first [91:58] step will be the multiplication. So 10 [92:01] into 2 equal to 20. Second will be [92:04] division and for that 15 / 3 equal to 5. [92:08] So in place of 10 into 2 we will get 20. [92:11] In place of 15 / 3 we will get 5. Next [92:15] operation will be the addition [92:16] operation. So 5 + 20 equal to 25. After [92:20] addition operation, now the remaining [92:22] operators are greater than and and equal [92:25] equal operator. So now first evaluate [92:28] the greater than operator. So for the [92:30] comparison 25 greater than 25 evaluates [92:33] to false. Same way five equal equal 5 [92:36] evaluates to true. Now the final [92:39] expression remaining is operator and for [92:42] that the final values available are [92:44] false and true. So this will evaluate to [92:48] false. So finally these many steps are [92:51] followed by the Python and we will get [92:53] the output to be false. To understand [92:55] Python operator precedence clearly let [92:58] us take the second example. So in [92:59] example two again you will see multiple [93:02] operators are provided. Looking at this [93:04] expression we can see that the [93:05] parentheses are provided in this [93:07] expression. So first we will execute the [93:10] parenthesis. The execution of [93:12] parenthesis will be 5 + 10 = 15 and 3 + [93:16] 2 = 5. After that we will perform the [93:19] multiplication operation. So 15 into 2 = [93:22] 30. Next division operation. So that is [93:25] 15 / 5 = 3. Next again we will perform [93:29] comparison operation. So that is 30 [93:31] greater than 25 evaluates to true. And [93:34] next is equality check that is 3= 3 and [93:38] it is evaluating to true. last operation [93:41] remaining that is the logical or [93:42] operation and the values are true or [93:45] true and this is evaluating to true. So [93:48] at the end we will get our output to be [93:51] true. So I hope you got the idea how [93:53] this operator precedence table is useful [93:56] to execute a different expressions based [93:58] on the operators provided. Now to check [94:00] the output of both these example, let us [94:03] go to the VS code. And here we have [94:05] provided the same example, example one [94:08] and the example two. So for that let us [94:11] run the program. So as you can see for [94:14] the first example we are getting the [94:15] output false and for the second example [94:18] we are getting the output true. So with [94:20] these two example I hope you got the [94:22] idea how these operators precedence [94:24] table will be useful to you to solve [94:27] different expressions. I suggest you to [94:30] practice more expressions and check the [94:32] results to build your confidence. With [94:34] the end of this video, I would like to [94:36] congratulate all and making it this far. [94:38] Learning Python or any programming [94:40] language is like building a foundation [94:42] for endless possibilities. By [94:44] understanding operators and expressions, [94:46] you have unlocked one of the most [94:48] critical tools in your programming [94:49] journey. Remember, every complex [94:52] programming starts with mastering the [94:54] basics. And you are doing just that. [94:56] Today you have taken another step closer [94:58] to becoming a confident Python [94:59] programmer. The path may seem [95:01] challenging at times, but each concept [95:04] you conquer build your confidence and [95:06] opens doors to innovation and [95:08] creativity. Keep pushing forward, keep [95:10] practicing, and most importantly believe [95:12] in your ability to learn and grow. Your [95:15] efforts today are saping you into a [95:16] problem solver of tomorrow. Stay [95:19] curious, keep coding and I will see you [95:21] in the next video where we will dive [95:22] deeper into Python. Until then, take [95:24] pride in your progress and never stop [95:26] exploring. You have got this. [95:30] Hello everyone. In this video, I will [95:32] teach you how to learn Python using Jet [95:35] GPT. To use Jet GPT, we need to provide [95:37] a prompt or information in a clear way [95:40] to get the AI output. Prompt in Jet GPT [95:43] is the input or question provided by a [95:45] user to initiate a conversion or request [95:48] information. It act as a starting point [95:51] for generating a response. The quality [95:53] and clarity of a prompt directly [95:55] influence the relevance and accuracy of [95:57] the AI's output. Prompts are important [96:00] because they are guide the AI on what [96:02] the user needs, ensuring concise and [96:04] useful response. A well structured [96:07] prompt minimizes ambiguity and helps [96:09] avoid incomplete or incorrect answers. [96:12] For example, explain Python data types [96:15] with examples. This will provide better [96:17] result than tell me about the data [96:19] types. So here JVT gets confused. You [96:22] are talking about the data types of [96:24] which language. So it is better if we [96:25] provide the clear information to the Jet [96:28] GPT. By refining prompts, user can [96:31] unlock Jet GP's full potential making it [96:33] a powerful tool for learning, problem [96:35] solving and creative task. Clear prompts [96:38] save time, enhance productivity and make [96:40] interactions more engaging and [96:42] effectively. For your reference, some of [96:44] the prompts are provided over here to [96:46] get the output from the chat GPT. So [96:49] let's go to the chat GPD and provide [96:51] some important prompts to get the [96:53] output. [96:55] Suppose I want to learn the numeric data [96:57] type from CGP in Python. So for that we [97:00] will provide the information such that [97:03] explain Python's numeric data types with [97:07] examples and hit enter. [97:15] So here you will get the output with [97:17] proper information and some of the [97:19] examples also. [97:37] Here you can see that Python has [97:39] provided some of the important [97:41] information regarding the numeric data [97:43] types and also the examples are provided [97:45] over here. Next, if you want to learn [97:48] arithmetic operators in Python, so you [97:50] may ask explain arithmetic operators [97:53] with example and hit Enter. [98:25] Here [98:34] that GBT has provided all the arithmetic [98:37] operators with the examples. And if you [98:39] look at the prompt here, we have not [98:41] specified the programming language. But [98:43] from the previous prompt, CHP has [98:46] automatically detected that we are [98:47] talking about the Python language. So [98:50] you can understand that each prompt may [98:52] be relevant to the previous prompt also. [98:54] So you will get the output accordingly. [98:57] Same way you can understand all the [98:59] different operators using this jet GBT [99:01] also. Next suppose we want to understand [99:04] what are the strings in Python. So you [99:06] may ask the same to the J GBT. So we may [99:09] provide the prompt what are strings in [99:12] Python and hit enter. [99:48] So here so much information is provided [99:50] by the JG GBT with regard to the string. [99:53] Let us again take one more prompt. [99:55] Suppose we want to understand the basic [99:57] input and output in Python. So we will [99:59] provide the information explain basic [100:02] input and output with one example in [100:06] brief in Python. [100:26] So here you can see that the chat GPT [100:28] has provided the concise information [100:30] regarding the input and output that we [100:32] have already discussed. Using the input [100:34] function we may get the input from the [100:36] user and using the print function we can [100:39] get the output on the screen. I hope [100:41] these with these examples you understood [100:43] how to make use of Jet GPT to learn more [100:46] about the Python topics. So the next [100:49] time you are reviewing topics like [100:50] commands, input or output or operators, [100:53] try using Jet GPD. Just type your [100:55] question and let it guide you. Remember, [100:58] learning is a journey and every question [101:00] you ask brings you one step closer to [101:02] mastery. I will see you in the next [101:04] session with more Python concepts. Until [101:06] then, make Jet GPD your learning partner [101:08] and strengthen your skills. Hello [101:11] everyone, welcome to module two of our [101:13] learning journey. This is control flow [101:15] and loops. In this module, we will dive [101:18] into one of the most exciting and [101:20] essential aspects of programming. [101:22] Control flow and loops form the backbone [101:24] of decision-m and repetitive task in any [101:27] program. This module will equip you with [101:29] the tools to make your program smarter [101:31] and more efficient. We will learn how to [101:34] guide the flow of execution in your code [101:36] using conditional statements like if, [101:38] else, and al if. And we will explore [101:41] loops such as for and while that allow [101:44] us to handle repetitive tasks [101:45] seamlessly. By the end of this module, [101:48] you will not only understand the [101:50] concepts but also be able to apply them [101:52] to solve real world problems including [101:55] practical examples in mechanical [101:56] engineering, data analysis, and [101:58] automation. So let's get started. And as [102:01] always, feel free to experiment along [102:03] the way. Remember, this is where your [102:06] programs truly start to think for [102:08] themselves. Let's unlock the power of [102:10] control and automation together. Let us [102:13] start with the conditional statements. [102:15] Conditional statements allow a program [102:17] to make decision based on conditions. [102:19] They control the flow of execution [102:21] depending on whether condition evaluates [102:24] to be true or false. So there are [102:26] different types of conditional [102:28] statements. From that the first one is [102:30] the if statement. Now to use the if [102:32] statement in Python programming we need [102:34] to use a proper syntax. So first we will [102:37] write if in a lower case and then we [102:40] will provide the condition using some of [102:43] the conditional operators. Then colon [102:46] symbol is important. After that we will [102:48] provide the enter and by providing the [102:51] indent we will write the code that we [102:53] want to execute if the condition is [102:55] true. So if the condition is true then [102:58] and then only the code inside the if [103:00] condition will get executed. Second [103:02] statement is the if else statement. So [103:05] as you can see the if condition will [103:08] remain same but now again we will also [103:11] provide the else condition. Here keep in [103:13] mind that the indentation provision is [103:15] most important. So in else case as you [103:18] can see we will write the else in lower [103:20] case. Then we will use the colon symbol. [103:23] Here we will not provide any condition [103:26] and this else block only gets executed [103:28] once the condition is false. So we may [103:32] use only if statement also and if needed [103:34] we can use the combination of if else [103:37] statement also. Now the third possible [103:40] if else statement is with the use of l [103:42] if statement. So in programming there [103:45] may be a possibility that in place of [103:47] one condition we want to check for more [103:50] than one conditions also. So to check [103:52] four more than one conditions we will [103:54] use if l if else case. So as you can see [103:57] first we will provide the if condition. [104:00] So if first condition is not satisfied [104:03] we want to check for the second [104:04] condition then we will write al if l if [104:07] in the lower case then condition two and [104:10] again if the condition two is satisfied [104:13] then the code will gets executed inside [104:16] this if statement. In place of second [104:18] condition, if you want to again check [104:20] the third condition also, then again we [104:23] will also write al if then we will [104:25] provide the condition three. So again if [104:27] condition three is valid then the code [104:30] executes inside that condition three or [104:33] at last if any of the condition is not [104:36] executed then the else case gets [104:38] executed and this code is only executed [104:42] if all the conditions are false. So this [104:45] if al if case is useful we want to check [104:48] for more than one conditions [104:50] simultaneously. [104:52] Now the last case is the nested if else [104:54] statement. Nested if else statement [104:57] means we will use if statement inside [105:00] another if or we may use if else inside [105:03] another else. So if you want to check [105:05] one condition inside another condition, [105:08] second condition inside third condition. [105:10] So to check multiple combinations or [105:13] multiple conditions depending on the [105:15] another condition these nested if else [105:18] statements are useful. So here as you [105:20] can see if condition one is true then [105:23] and then only interpreter will go inside [105:26] condition two. Now again if condition is [105:29] true then and then only the code [105:31] executes inside the condition true. If [105:34] the condition two is false then the else [105:37] block gets executed. Same way you can [105:40] see that the else case is provided for [105:43] the outer if and if condition one is [105:46] false then and then only the outer else [105:49] gets executed. Inside else case again we [105:52] have provided the condition three. So if [105:55] that condition three is true then and [105:58] then only the code inside if gets [106:01] executed. Else condition three is false [106:04] then the final else block gets executed. [106:07] So as you can see multiple if else [106:10] combinations can be used inside if else [106:13] based on the requirement. So this case [106:15] is known as the nested if else [106:18] statement. Here the key point to [106:20] remember is Python uses indentation to [106:23] define the blocks of code under each [106:25] condition. You can also combine multiple [106:28] conditions using and or and not [106:31] operators also. Let us understand all [106:33] these different statements using the [106:36] actual code. So for that go to the VS [106:38] code. Here as you can see first example [106:41] is provided for the if case. Here we [106:44] have mentioned if the condition provided [106:47] is five greater than three. So if this [106:50] condition is true then the block gets [106:52] executed and we will get the output five [106:55] is greater than three. If this condition [106:58] is false so you can see that here else [107:00] block is not provided. So we will not [107:02] get any specific output. Second is if [107:05] else example. Here you can see that we [107:08] have provided both the block. If five [107:10] greater than 10 then we want to print [107:12] five is greater than 10. If this [107:14] condition is false then compiler will go [107:17] into the else block. And here if we will [107:19] get the output 5 is not greater than 10. [107:22] So in our case 5 is greater than 10. [107:25] This condition is false. So else block [107:27] gets executed and we will get the output [107:30] five is not greater than 10. So this [107:34] specific statement will be our answer. [107:37] Third is if l if else statement. So here [107:40] you can see that we have stored value [107:43] age to be the 20. Now if condition is [107:46] provided age is less than 18. We will [107:49] get output you are minor. But if you [107:51] want to check for the second condition l [107:53] if age equal equal 18 then we want to [107:56] print you just become an adult. And if [107:59] none of these conditions are true then [108:01] compiler will go to the else block and [108:03] here it gets printed you are an adult. [108:06] So here our variable age is equal to 20. [108:09] So let us check first 20 is less than 18 [108:12] condition is false. So we will move to [108:15] al if here 20 equal equal 18. Again this [108:19] condition is false. So we will go to the [108:21] else block. And here we will get the [108:24] output you are an adult. At last nested [108:27] if else statement here as you can see we [108:30] have provided marks equal to 72. Now we [108:33] have provided first condition if marks [108:35] greater than equal to 75 then it will [108:37] print you achieved distinction. If that [108:40] condition is false then we have provided [108:42] the second condition. L if marks greater [108:44] than equal to 60 then it will print you [108:47] passed with the first class. Within this [108:50] statement if we want to also check if [108:52] the marks are greater than 70 or not. So [108:55] inside al if statement we have used [108:57] again if else block. This is known as [108:59] the nested if else statement. So if [109:02] marks greater than 70 then it will [109:04] execute your score is above 70. Else it [109:08] will print your score is between 60 and [109:11] 70. Now suppose if and l if both [109:14] conditions are false then interpreter [109:16] will go to the else block and here it [109:18] will print you did not achieve first [109:21] class. So let us again check with the [109:22] variable marks are the 72. So let us [109:25] check first condition. 72 greater than [109:27] equal to 75 false. We will go to second [109:30] step. 72 greater than equal to 60. True. [109:34] So it will print you passed with the [109:36] first class. Again inside that it will [109:39] check mark greater than 70. So 72 [109:42] greater than 70. True. So again it will [109:44] print your score is above 70. If is [109:48] executed. So else block will not get [109:50] executed. Same way ell if statement is [109:53] satisfied. So else block will not get [109:55] executed. So our output will be first [109:58] you passed with the first class second [110:01] and your score is above 70. So I hope [110:04] you got the idea how this syntax will be [110:07] helpful to get the output based on the [110:09] condition. So let us run the program. [110:13] So here as you can see that based on the [110:15] conditions we will get the output five [110:17] is greater than three five is not [110:19] greater than 10 and same way multiple [110:21] outputs are provided over here. So I [110:24] hope you got the idea how these if else [110:27] statements are useful to get the output [110:29] based on the different condition. [110:31] Practice these if else statement to get [110:33] more familiarity with this syntax. Keep [110:35] in mind indentations are most important. [110:38] If you will not provide the indentation [110:40] then you will get the error. So let us [110:42] say suppose at the first if five greater [110:46] than three is mentioned and I am [110:47] removing the indentation with the print. [110:50] So here directly you can see that the [110:52] error is highlighted with the red line [110:54] and it is mentioning that expected [110:57] indented block. So it is much more [110:59] important to follow the proper [111:01] indentation process to get the output. [111:04] In our next session, we will try some of [111:07] the another important examples also with [111:09] the if statement considering the [111:11] mechanical engineering examples. [111:14] Hello everyone. Today as we continue our [111:17] journey of learning programming and [111:19] logical thinking, I want to bring [111:21] something exciting and practical to this [111:23] table. Our next practice will focus on [111:25] writing if program. But this time we [111:28] will integrate mechanical engineering [111:29] concepts into our coding exercise. So [111:32] let's directly jump to the practical [111:34] examples related to the mechanical [111:36] engineering. Go to the VS code. Here as [111:40] you can see we are having the first [111:41] example. Machine requires regular [111:44] lubrication if it operates continuously [111:47] for more than 8 hours a day. Now we need [111:49] to write a program that takes the [111:51] operating hours of a machine as a input [111:53] and checks if it requires lubrication or [111:57] not. So as you can see in the statement [112:00] we need to take the input for the Rs. As [112:03] we take the input we know that the input [112:06] will be by default in the string data [112:08] type. So we need to convert it into the [112:10] numeric data type. It may be a int float [112:12] based on requirement we need to change [112:14] the data type then we put the condition [112:17] if ours is more than 8 means Rs greater [112:22] than 8 then we will write it requires [112:24] the lubrication. else we will write we [112:27] do not require the lubrication. So for [112:30] that let us check the code. As you can [112:32] see we have provided the one variable [112:35] operating hours equal to we have [112:37] converted the data into the float data [112:39] type. Input enter the number of hours [112:42] the machine operates daily. Then using [112:45] the if else conditions we have printed [112:48] some data. So if operating hours greater [112:51] than 8 colon we will print the machine [112:54] requires regular lubrication. Else we [112:56] will print the machine does not require [112:59] lubrication. So let us run the code. [113:03] We will enter the number of hours. Let's [113:05] say the number of hours being the seven. [113:08] Press enter. So here you can see the [113:10] output. The machine does not require [113:12] lubrication as the value entered is 7. 7 [113:16] greater than 8. Condition is false. So [113:18] we will go to else case and here it will [113:21] print machine does not require [113:23] lubrication. Let us again run the code. [113:26] Now pass the value greater than 8 being [113:30] 10 and hit enter. So now you can see 10 [113:33] greater than 8 condition is true. So it [113:35] will print the machine requires regular [113:38] lubrication. Based on the conditional [113:40] check outputs are required. So in such [113:42] case these if else statements will be [113:44] very much helpful. Let us go to the next [113:47] example. Here you can see that the [113:49] example provided is the mechanical part [113:52] is considered safe for use if its stress [113:55] level is less than or equal to 200 [113:58] megapascal. We need to write a program [114:00] based on the stress value as input and [114:02] output will be whether the part is safe [114:05] or unsafe. So same way here first we [114:09] need to assign the stress value using [114:11] the input. we will convert it into the [114:14] required data type. Then based on the if [114:16] else condition, if our condition is the [114:19] stress less than equal to 200, then we [114:22] will print component is safe. Else we [114:26] will print the component is unsafe. [114:28] Again check the solution here. You can [114:30] see that we have defined the variable [114:32] stress value equal to float. Input being [114:36] the enter the stress value for the [114:37] mechanical part value should be in mega [114:40] pascal based on the condition. If stress [114:42] value less than equal to 200 colon then [114:45] it will print the mechanical part is [114:47] safe. Else it will print the mechanical [114:50] part is unsafe. Again let us run the [114:52] code. Now enter the value to be 150. Hit [114:56] enter. The output will be mechanical [114:59] part is safe for use. Same way run the [115:02] program again. Now input the value 300. [115:07] Enter. So now the output will be the [115:09] mechanical part is unsafe for use. So [115:12] based on the input value we will get [115:14] either condition being the true or [115:16] either condition being the false. The [115:18] different outputs will be printed on the [115:20] screen. Now again go to the third [115:22] example. The cooling system operates at [115:25] a different efficiency levels based on [115:27] the ambient temperature. Here the [115:29] different conditions are provided. If [115:31] the temperature is below 20°C it will [115:34] print high efficiency. If it is between [115:37] 20 and 40° C, so we need to print [115:41] moderate efficiency and above 40° C, we [115:44] need to print the low efficiency. [115:46] [clears throat] [115:47] So we need to write a program to take [115:49] the ambient temperature as a input and [115:51] output will be the systems efficiency [115:54] level based on the conditions. So here [115:56] as you can see we need to have more than [115:59] one condition in our program. So in such [116:01] case we need to use the concept of if, [116:03] element, else. So again let us check the [116:06] program. So here as you can see first we [116:10] have provided input being the ambient [116:12] temperature. Temperature value is [116:14] converted into the float based on the [116:16] input. Now we have provided the multiple [116:18] conditions. If our variable where the [116:21] temperature is stored that is ambient [116:23] temperature is less than 20. So it will [116:26] print the cooling system is operating at [116:28] a high efficiency. Al if as we are [116:31] having the second condition so the [116:33] condition used is al if so here the [116:36] value provided is if the value is [116:38] between 20 and 40 so we have mentioned [116:41] 20 less than equal to our variable that [116:44] is ambient temperature again less than [116:46] equal to 40. So if our value is between [116:50] 20 and 40 so it will print cooling [116:53] system is operating at moderate [116:54] efficiency. else. If both conditions are [116:58] not satisfied then we will go to the [117:00] else case and it will print the cooling [117:02] system is operating at low efficiency. [117:04] So again keep in mind at each and every [117:06] place indentations are important, colons [117:09] are important without these always we [117:11] will get the error. So let us run the [117:13] program to check the output. First let [117:16] us input ambient temperature to be 15. [117:19] Hit enter. So now once we enter the [117:22] value 15 15 15 will go and get stored [117:25] into the ambient temperature the 15 less [117:28] than 20 condition is true. So it will [117:30] directly execute the data inside that [117:32] condition and it will print it is having [117:35] the higher efficiency. Once any of the [117:37] condition is satisfied the remaining [117:39] cases will not get executed and we will [117:41] directly get our output. Again run the [117:44] program to check for the second case. [117:46] Let us enter the value 26. Hit enter. So [117:49] now first case 26 is less than 20 false [117:53] then it will check for the next [117:55] condition 20 less than or equal to value [117:58] being 26 less than or equal to 40. So [118:01] this condition is true 26 is in between [118:04] 20 and 40. So based on this condition it [118:07] will give us the output that is the [118:09] moderate efficiency. Again run the code [118:12] and now enter the value being greater [118:14] than 40. Let us say the temperature [118:16] being the 50°C. So now 50 is less than [118:20] 20 condition is false. 20 less than or [118:24] equal to 50 less than or equal to 40. 50 [118:26] is not in between 20 and 40. So again [118:29] this condition is false. Finally we will [118:32] go to the else case. So in else case it [118:35] will print the cooling system is [118:37] operating at a low efficiency. So based [118:40] on our input interpreter executes the [118:42] data and we get the output based on the [118:45] conditions provided in the program. So [118:48] here keep in mind that data type [118:50] conversion is important. Then and then [118:52] only we will get our required output. [118:55] Now let us go to the last example. So [118:58] here again as you can see the statements [119:00] are provided. The statement is in a [119:02] mechanical assembly a bolt is [119:04] categorized based on its diameter. Here [119:07] it is mentioned if the diameter is [119:09] greater than 10 mm then again we need to [119:12] check. First condition is check for 10 [119:14] mm diameter. Within that we need to [119:17] again check inside that condition if it [119:19] is a high strength bolt or standard [119:22] bolt. So condition given for that is if [119:24] the value of tensile strength is greater [119:26] than equal to 800 megapascal then it is [119:29] a high strength bolt. Else it is a [119:31] standard bolt. Next, if the diameter is [119:34] less than or equal to 10 mm, then we [119:37] need to check again. In that case, we [119:39] need to check if it is a precision bolt [119:41] or a general purpose bolt that we will [119:44] decide based on the tolerance value. So, [119:46] this data is given. Now, the program [119:48] requires us to take the diameter, [119:51] tensile strength and tolerance as input [119:54] and categorizes the bolt based on the [119:57] given data. So, let us start the program [119:59] based on the given data. So first we [120:02] need to take input as a diameter. So [120:04] diameter equal to we will convert it [120:07] into the float data type. So we will [120:09] write float to take the input keyword is [120:12] input and let us mention enter the [120:15] diameter of bolt. Once we get the value [120:18] of the diameter we need to check for the [120:20] condition. So let us check for the [120:23] condition. So if diameter is greater [120:25] than 10 mm. So diameter is greater than [120:29] 10 mm. Then what we need to do? So [120:32] inside that again we will check if it's [120:35] high strength bolt or standard bolt. So [120:38] inside this we will again ask the user [120:40] to input the value for the tensile [120:42] strength of the bolt. So let us take the [120:45] value tensile strength equal to convert [120:49] the value into float. Let us take it as [120:52] a input. [120:54] Enter tensile strength of bolt and it [120:58] should be in megapascal. Now based on [121:01] the value again we need to check if the [121:04] value of tensile strength is greater [121:07] than equal to 800 then we will print it [121:11] is a high strength bolt. Else colon we [121:15] will print it is a standard bolt. Now [121:18] here first section is over. Here we have [121:21] used if inside if. So this is the case [121:24] of the nested if else statement. Now we [121:27] have completed the inside if else [121:29] statement. Now for the outer if we need [121:32] to check for the value less than or [121:34] equal to 10 mm. So the else case will [121:36] always consider for the less than equal [121:38] to 10 mm. So in else case we need to [121:41] again take the input that is the value [121:43] of tolerance. So we will store the value [121:46] of tolerance convert it into the float. [121:49] Take the input. Enter. Tolerance of the [121:52] bolt. Now we will check if tolerance [121:55] value is less than equal to 0.01 [122:00] mm. So we will print precision bolt. [122:03] Else we will print generalpurpose bolt. [122:07] So based on our input compiler will go [122:10] through these certain statements and it [122:12] will give us the output based on the [122:14] diameter, strength and the tolerance [122:16] value provided. Let us clear this to get [122:19] the proper idea. Run the program again. [122:22] We have asked to enter the diameter of [122:24] bolt. Let us say the diameter of bolt [122:26] being 8. As the value of diameter is 8, [122:30] it will check if 8 greater than 10 [122:32] condition is false. So we will directly [122:34] move to the else case. In else we have [122:37] asked to enter the value of tolerance. [122:40] So next we need to enter the value of [122:42] tolerance. So let us say the value of [122:44] tolerance being the 0.02. 02 mm and hit [122:48] enter. So here you can see that 0.02 is [122:52] less than or equal to 0.01 condition is [122:55] false. It will go to the else case in in [122:58] else block it will print the [123:00] generalpurpose bolt. Again let us run [123:03] the program to reverify the other [123:05] details. So again run the program. Enter [123:08] the diameter of bolt being the 12 mm. So [123:11] 12 is greater than 10. True. It will go [123:14] into the case of tensile strength. Now [123:16] enter the value of tensile strength. Let [123:18] us value being the th00and. Hit enter. [123:22] So again check inside that 1,000 is [123:24] greater than or equal to 100. True. Then [123:26] it will go inside the if case and it [123:28] will print high strength bolt. So based [123:31] on the entered values program will [123:33] decide which output is required. So same [123:37] way in the case of automations or in the [123:39] programming based on the input value [123:42] received from different sources we can [123:44] decide the type of content or the type [123:47] of values required for our program. You [123:50] may try for different variations also [123:52] for your practice purpose to get the [123:54] idea how we are getting the output based [123:57] on the execution of the program. So with [124:00] these combinations of examples, I hope [124:02] you got the idea how to implement the if [124:04] else statements and its logic based on [124:07] the requirement. So these examples have [124:09] not only enhanced our understanding of [124:11] conditional programmings but also so how [124:14] programming can be a powerful tool for [124:17] engineers. As we move forward, I [124:19] encourage you to continue experimenting. [124:21] Take the concepts we discussed, expand [124:24] on them and try applying them to other [124:26] areas of engineering. The more you [124:28] practice, the more confident and the [124:30] skilled you will become. Let's keep this [124:33] momentum going as we take on even more [124:35] exciting programming challenges in the [124:37] future. Looking forward to see you all [124:39] in our further sessions. Till then, [124:42] thank you. Hello everyone. Welcome to [124:44] today's session where we will dive into [124:47] the essential concept in programming [124:49] that is the loop statements or the loop [124:51] structures. If programming is like [124:54] solving puzzles, then loops are the [124:56] tools that help us repeat certain task [124:59] efficiently, saving the time and effort. [125:01] So before we begin, let me ask you a [125:04] quick question. Have you ever [125:06] encountered a situation where you need [125:08] to perform the same calculation or [125:10] operation multiple times? Whether it's [125:12] calculating the forces for different [125:14] loads, simulating processes and [125:16] mechanical systems or even or even [125:20] repeating task in daily life, we often [125:23] deal with repetitive actions. In [125:26] programming, loop statements help us to [125:28] handle such scenarios. With loops, we [125:31] can automate repetitive task with just a [125:34] few lines of code. Instead of writing [125:36] the same logic over and over, we can use [125:39] for loops or while loops to let the [125:41] computer do the heavy lifting. So in [125:44] this session we will understand how for [125:47] loops work, understand their syntax and [125:49] most importantly practice some of its [125:52] basic examples. So this for loop is used [125:55] to iterate over a sequence specific like [125:58] list, tpple, string or range. The basic [126:02] syntax of for loop involves we need to [126:05] use the keyword for then after any [126:08] variable name which refers to any of the [126:11] iterable. It might be the data of list [126:14] tuple string or any of the range. So we [126:18] will write for variable name then [126:21] keyword int then after the list tuple or [126:24] string that we want to represent or the [126:26] any iterable that we want to represent [126:28] from which we want to use the data then [126:32] provide the colon symbol then hit enter [126:34] and providing the indentation you may [126:37] provide any number of statements that [126:39] you want to repeat for that number of [126:41] series or for that number of iterations. [126:44] So here the key components are variable, [126:47] iterable and the statements. So a [126:50] variable is a placeholder that takes on [126:53] the value of each element in the [126:55] iterable. So if let us say in iterable [126:59] values available are 10, 20, 30, 40. So [127:02] the variable refers to the each [127:05] individual element one by one. So first [127:08] it refers to the 10 then 20 then 30 and [127:12] then 40. The next is iterable. It is a [127:16] sequence or collection to iterate over [127:19] and it includes the list, tpple, string [127:22] and range. And the last one is a [127:25] statement. So it is the block of code to [127:27] execute for each iteration. And this [127:30] block must be indented. That is the most [127:33] important part. To run the simple [127:36] iteration process using the for loop. Go [127:38] to the VS code. Now suppose as an [127:41] example first we want to iterate over a [127:43] list. We are having the list of [127:45] materials and it is stored in the [127:47] variable name materials equal to the [127:50] data is stored into the list. So within [127:53] square bracket we will provide our data. [127:56] The material being steel, [127:58] aluminum comm, copper. Now suppose I [128:01] want to print each individual element of [128:04] this material. So the printing of [128:06] element is the repeated task and these [128:09] repeated task can be performed using the [128:11] concept of for loop. So to use the [128:13] syntax hit enter we will write the [128:16] keyword for then we will create one [128:19] variable which will refer to the each [128:22] individual element available in the [128:24] materials section. So let us say 4x in [128:29] now from which data we want to consider [128:32] each element. So that will be from the [128:34] materials list. So we will write in [128:37] materials and then colon symbol. So here [128:40] you can see for X in materials means X [128:44] refers to each individual element [128:46] available in the materials. So X will [128:49] refer to steel then aluminum then [128:52] copper. Here our objective is to print [128:54] each element. So we will print let us [128:56] say the material [128:59] comma the variable being the X. Now run [129:02] the program. So here you can see that [129:05] the material first it will take the [129:07] value X. X refers to the first element [129:09] that is steel. So material steel. Next [129:12] it will again run the program. Then the [129:14] next material is aluminum. So material [129:16] aluminum. And the last one is the [129:18] material copper. So here X refers to [129:21] each element available and it will [129:24] continue till the end of the element [129:26] available in the list or tuple or the [129:28] given iterable. I hope you got the idea [129:31] how to use the syntax of the for loop to [129:34] iterate over the data available in our [129:36] iterable. In our next session, we will [129:39] continue with some of the important part [129:41] that is useful into the for loop that is [129:44] the range function and its syntax. Hello [129:47] everyone, in our previous session we [129:49] discussed about the for loop and its [129:51] syntax. Now we will understand the range [129:53] function that is used with the for loop [129:56] in combination to again give us the [129:58] certain data. So the range function in [130:01] Python is used to generate a sequence of [130:03] numbers and it's commonly used with the [130:06] for loop to iterate over a specific [130:08] range of numbers. So as a syntax of the [130:11] range as you can see we will first write [130:13] a range in lower case and within round [130:15] bracket we will provide the three data [130:17] based on the requirement that is the [130:19] start value stop value and the number of [130:22] step in which increment or decrement is [130:25] required. Here start refers to the [130:27] starting value of the sequence and the [130:30] default value is zero if not specified. [130:33] So if we have not specified the start [130:35] value then the default value will be [130:37] considered as the zero. Next, the stop [130:40] refers to the ending value of the [130:41] sequence and it is not included in the [130:44] range. So the last value that is the [130:47] stop value is excluded from the range. [130:50] The next is tab and it is the difference [130:53] between each number in the sequence and [130:55] the default value is one if not [130:58] specified. Now let us use the range [131:00] function in the real examples. So for [131:02] that go to the VS code. Suppose we want [131:04] to print the simple range having the [131:06] default start and step value. So for [131:08] that we will use the for loop. So for i [131:12] in we will use the range and the value [131:15] provided is the five then colon and we [131:18] want to print the numbers. So we will [131:20] type print i. So here as you can see [131:22] that we have provided only the fixed [131:24] value that means it will accept the [131:27] start and step value as the default [131:29] value and here five is being the end [131:32] value. So default value will be zero for [131:35] the start and the step default value [131:37] will be 1. So as you can see we will [131:40] start from the i. The value of i. [131:42] Initial value being the zero. So first [131:45] it will print zero then 1 then 2 3 and [131:49] four. This loop will get repeated for [131:51] five times as we have provided the end [131:54] value being the five. So let us run the [131:56] program. So here as you can see that it [131:58] will print 0 1 2 3 and four. So keep in [132:02] mind that the default value for the [132:05] start being the zero and the default [132:07] value for the step being the one. Now [132:10] suppose if our need is to specify the [132:12] start and stop value. So for that again [132:16] we will use the four i in range. Now [132:20] here we will provide the starting and [132:22] ending value. So 1 to 6 colon and again [132:26] our objective is to print I. So here you [132:30] need to keep in mind that whenever we [132:32] are providing the range with the [132:34] starting and stopping number it will [132:36] start from one it will include one but [132:39] the last ending number six is not [132:42] included. So it will start from 1 up to [132:45] the five only. So run the program. As [132:47] you can see over here it will print 1 to [132:49] five but six is not printed because the [132:52] end number is excluded from the given [132:55] range. Now let us say suppose our [132:57] requirement is to provide the start stop [133:00] and the step also. So for that we need [133:03] to again rewrite our for loop and let us [133:05] say suppose we want to provide the [133:07] starting value. So 4 I in range the [133:10] value being the 0 comma 10 [133:13] then colon and again we want to print I. [133:17] So what is the meaning of this [133:18] statement? So the starting value is [133:20] zero, ending value is 10 and the value [133:24] gets increased by two. So first it will [133:27] print zero then increment of two. So 2 [133:31] then 4 6 8. But again as we know end [133:36] number is not included in our series or [133:39] the sequence. So it will print up to the [133:41] 8 only. So again let us run the program [133:44] check the output. So here as you can see [133:46] it will start from zero then 2 4 6 and [133:50] 8. So if needed we can also specify this [133:53] step size also based on our requirement. [133:56] Up to now as you can see we are printing [133:58] our data in the forward direction only. [134:00] It means in the incremental order only [134:03] but there may be also chances that if we [134:05] want to print the data in the reverse [134:07] order or in the reverse range also. So [134:10] it is also possible. For that we will [134:12] provide the step size with the negative [134:15] value. So again let us start the for [134:17] loop. So for i in range let us say the [134:21] starting value is 10 ending value is [134:23] zero and I need the decrement of minus2. [134:26] Provide colon and I want to print I. So [134:29] it will start from 10. It will not [134:31] include zero. So 10 then decrement of [134:34] minus2. So it will print 8. 10 6 then 4 [134:38] then 2. But zero is not included. So it [134:41] will print value up to the two only. So [134:44] run the program and check the output. It [134:46] will print the data in the reverse order [134:48] that is 10 8 6 4 and two. So I hope you [134:52] got the idea how to make use of the for [134:54] loop in the combination of the range [134:56] function also. Other than this there is [134:59] also possibility that you can loop [135:01] through each character available in the [135:03] string also. So to understand that again [135:06] let us take one example. Suppose it is [135:09] being our example six. Objective is to [135:12] loop through strings. Suppose we are [135:15] having the name equal to Python. Now we [135:18] want to iterate through each character [135:20] available in the string. So for that we [135:22] will write four. Let us say the variable [135:24] name being the care in. Now the iterable [135:27] being the variable name then column. And [135:30] I want to print each character. So let [135:32] us print the value care. Again run the [135:35] program. So here as you can see that it [135:37] will execute code for each different [135:40] character and it gets printed separately [135:43] based on our program. So this for loop [135:45] is useful to iterate through some of the [135:48] iterables also that may be the list tle [135:51] then string and we can also get the [135:53] certain range of data using the range [135:56] function. So as you can see we started [135:58] by understanding the basics of for loops [136:01] which serve as one of the most powerful [136:03] tools for handling the repetitive task [136:06] in programming. We then explored the [136:08] range function which simplifies the [136:10] iteration by generating a sequence of [136:12] number in a highly flexible way. So [136:15] together these tools allow us to tackle [136:18] complex problems with precision. As you [136:21] continue to explore the loops, challenge [136:23] yourselves to write programs that [136:25] automate task and solve engineering [136:27] problems more efficiently. The more you [136:29] experiment with the tools like for loop [136:31] and range, the more confident you will [136:33] become in leveraging the programming to [136:35] simplify complex scenarios. I look [136:38] forward to seeing how you use this [136:40] concept in our future programs. Also in [136:43] our next session we will continue with [136:45] some of the mechanical engineering [136:47] related examples to use the concept of [136:49] for loop. Welcome back everyone. In this [136:53] session we are going to take some of the [136:55] practical examples using for loop in the [136:58] context of mechanical engineering. In [137:00] the field of mechanical engineering for [137:02] loops can be used to analyze stress [137:04] distribution, calculate temperature, [137:06] optimize designs and there are many [137:08] different possibilities. Let us go to [137:11] the VS code to take some of the [137:13] examples. As you can see in the first [137:15] example, we have been given the [137:17] statement. Calculate the stress [137:19] distribution across a 10 m long beam [137:21] under a uniform load of 5,000 Nton. And [137:24] it is having a constant cross-sectional [137:26] area of 50 mm². The formula is already [137:30] given to us that is stress equal to load [137:32] upon area. And we have to calculate the [137:35] stress at each meter along the beam's [137:38] length. and we need to assume a uniform [137:41] load distribution and consistent [137:42] cross-sectional area. So our objective [137:45] is to display the stress values for each [137:47] meter of the beam. So as we know here [137:50] the major parameters are the length of [137:54] beam being the 10 m load being the 5,000 [137:57] Nton. So we need to calculate the stress [138:01] at each meter of the beam. So we need to [138:04] iterate from first meter, second meter, [138:07] third, fourth and same way we need to go [138:10] up to the 10 m. So let us first create [138:12] some of the important variables. So as [138:15] you can see we have defined load being [138:18] the 5,000, area being 50 and beam length [138:22] being 10. Now to calculate the stress [138:24] along the length of the beam, we need to [138:27] start our for loop starting from 1 m up [138:30] to the 10 m. So let us continue writing [138:33] the for loop. So as you can see for i in [138:37] the range 1 to beam length + 1 that [138:41] means 1 to 11. So our loop will start [138:44] from 1 up to the 10. Within this for [138:47] loop our requirement is to calculate the [138:49] stress. So the next iteration is stress [138:52] equal to load divide by area. We have [138:55] already defined the value of load and [138:57] the area. And here it is clear that the [139:01] equation does not depend on the length. [139:03] So for each meter the value of stress [139:06] being the same because the load is [139:08] uniformly distributed. At last we are [139:11] printing the data stress at specific [139:13] meter we want to print. So we have [139:15] provided the value of I. So it will [139:17] print stress at 1 m. Then the value of [139:21] stress that is being the based on the [139:23] calculation. So 5,000 divide by 50 the [139:26] value. Then after it will print the [139:28] megapascal that is the unit. Let us run [139:31] the program to check the output. As you [139:33] can see the data is printed for each [139:36] meter that is stress at 1 m 100 [139:38] megapascal at 2 m 100 megapascal. So [139:41] same on up to 10 m we will get the same [139:44] value of the stress as the load is [139:46] uniformly distributed and it is having [139:48] the constant cross-sectional area. So [139:51] using these three lines only we are able [139:53] to get the output based on the iteration [139:55] of the for loop. Let us again consider [139:58] the second example. So the statement [140:01] given is we need to calculate [140:03] temperature change along a metal road. A [140:06] 10 m long metal road is exposed to a [140:09] constant heat source causing a uniform [140:11] temperature gradient along its length. [140:13] The initial temperature at one end given [140:16] is the 20° C and the temperature [140:19] increases by 5°C per meter. So we need [140:22] to write a program to calculate the [140:24] temperature at each meter along the [140:26] road. The formula given is temperature [140:29] equal to initial temperature plus rate [140:31] of change multiplied by the distance. So [140:34] our objective is to print the [140:36] temperature for each meter along the [140:39] road. So here as you can see again our [140:41] requirement is to start our for loop [140:43] from 1 to 10. But the data that we need [140:46] to calculate is different based on the [140:48] requirement. So first let us define the [140:51] variables. So variable being initial [140:53] temperature 20, rate of change five and [140:57] road length being the 10. Now using the [141:00] range function and the for loop we can [141:02] iterate over each meter of the given [141:04] road. So let us continue writing the for [141:06] loop. So as you can see for I in the [141:09] range 1 to road length + 1 road length [141:12] being our variable. Next we need to [141:14] calculate the temperature. So the [141:16] temperature being initial temperature [141:18] plus rate of change into now the [141:21] distance and the distance is increased [141:24] every time by using the variable I. So [141:26] we are multiplying this with the [141:27] variable I. After calculating the [141:30] temperature we will print the [141:31] temperature. So again using the print [141:33] function we have printed temperature at [141:36] specific meter. So to indicate different [141:39] meters 1 2 3 likewise we have provided [141:42] the variable I and the value of [141:45] temperature that we are getting that [141:46] will be printed along with with a [141:48] specific format to check the output. Let [141:51] us again run the program. So as you can [141:53] see the temperature at 1 m is 25°C [141:57] and the temperature increases by 5°C per [142:00] meter. So accordingly you will find the [142:02] increment in the temperature in the [142:04] output. I hope you got the idea how this [142:07] for loop is useful to iterate over a [142:09] data perform repeated calculation and [142:12] solve complex problems efficiently. As [142:15] we have seen today the power of Python [142:17] lies in its ability to automate [142:19] repetitive calculations, analyze data [142:21] and solve problems efficiently. I [142:24] encourage you to think about the [142:25] processes you work with in your projects [142:28] or studies and consider how programming [142:30] can simplify that data. The more you [142:33] practice, the more you will see Python [142:35] becoming a valuable tool in your [142:37] engineering toolkit. I hope these [142:40] examples inspired you to explore even [142:42] more applications of Python in your [142:44] field. Keep experimenting, keep learning [142:46] and see you in the next session. Welcome [142:49] back everyone. Today we are diving into [142:51] one of the most powerful tools in Python [142:54] that is while loop. Unlike the for loop [142:57] which is ideal for situations where we [142:59] know the number of iterations [143:00] beforehand. The while loop is perfect [143:03] for scenarios where the condition for [143:05] continuation isn't predefined. Something [143:08] we often encounter in mechanical [143:10] engineering task. For example, in [143:13] engineering simulations, system checks [143:15] or iterative problem solvings like [143:17] achieving a target tolerance or [143:19] convergence criteria, while loops [143:21] provide the flexibility to keep running [143:23] until the job is done. So in this [143:26] session, we will explore the syntax of [143:28] while loops, understand how they work [143:31] and tackle some of the mechanical [143:32] engineering problems using these [143:34] specific tool. So let us first [143:36] understand the syntax. To use the while [143:39] loop first we will type while in lower [143:42] case. Then after we provide the [143:44] condition and that condition output [143:46] should be the boolean. Now if that [143:48] condition is true then the block inside [143:52] the while loop gets executed. It may be [143:54] any statement from statement one to the [143:57] number of required statement. So this [143:59] block of statements gets executed as [144:02] long as the condition is true. There may [144:04] be a possibility that we may update the [144:06] condition to exit from the loop inside [144:09] the while loop content. Here keep in [144:11] mind that all statements inside the loop [144:14] must be indented and if the condition is [144:17] never updated to false then the loop [144:19] will run forever. That means it will go [144:21] into the infinite loop. So we must need [144:24] to keep a track of condition also and [144:26] the track of end condition also where [144:28] the loop will end. Now these may be [144:31] increment or decrement also or it may be [144:34] the change in value also such that the [144:36] condition becomes false. So let us [144:38] understand the syntax of while loop [144:40] using the first example. For that go to [144:43] the VS code. Here as you can see our [144:45] problem statement is we need to write a [144:47] Python program to print countdown from 5 [144:50] to 1 using a while loop. So the first [144:53] thing required is initialization. We [144:56] need to start from the five. So we will [144:58] initialize our value to be five. So it [145:01] is stored first into the variable count [145:03] equal to five. Second requires is the [145:06] end condition. For that we need to start [145:08] our while loop. So we will write while [145:10] then condition and the condition being [145:12] till what point we need to continue. We [145:15] need to continue up to one. That means [145:17] if the value of count that is our [145:19] variable is greater than zero till that [145:22] point we will perform certain task. Now [145:25] what is the requirement? we need to [145:26] print the number. So print the number we [145:29] need to print the value of count. So the [145:32] next statement we have mentioned is [145:34] using print function we have printed the [145:36] value of count using a specific content. [145:39] Now let us say we have started from the [145:42] five. So that five value go into the [145:45] while loop. So five greater than zero it [145:47] is true. Then it will print countdown [145:50] the value of count being the five. So it [145:52] will print countdown five. Next we need [145:55] to go to the other required number that [145:57] is four. That means we need to reduce [145:59] the value of count by one. Then we need [146:02] to have the value three then two then [146:05] one. That means after each operation we [146:07] need to decrease our value by 1. So [146:10] after printing statement we have [146:11] decremented our value that is the count [146:14] minus equal to 1. So now we will start [146:17] from five then 4 3 2 1. Once the value [146:21] becomes a zero again it will be stored [146:23] into the variable count equal to zero [146:26] then it will pass into the condition. So [146:28] 0 greater than zero condition is false. [146:30] As the condition is false we will come [146:32] out of the while loop. So to understand [146:35] the output let us run the program. So [146:38] here as you can see the value printed [146:40] are countdown 5 4 3 2 and 1. Now suppose [146:44] if we do not have provided these [146:47] decrement. So let us comment the same. [146:50] Now again run the program. So as you can [146:52] see the loop is started for the infinite [146:55] number of times. It will continuously [146:57] print the value five. So this program [147:01] has gone into the infinite loop. Let us [147:03] first exit from the program. Now why [147:05] this has happened? So for this again [147:08] check the program. The value of count is [147:10] five. So it will pass over here five [147:13] greater than zero. True. It will print [147:15] countdown five. Then again we will go [147:17] back. Now the value of count being five [147:20] only we have not incremented or [147:21] decremented based on requirement. So it [147:24] will continuously take the value five. [147:26] For that this condition is always going [147:28] to be true. So it will print our data [147:31] for the infinite number of times. So we [147:33] must need to take the end condition and [147:36] the requirement of change in value or [147:38] increment decrement to properly execute [147:40] our program. Let us take another example [147:43] of the while loop to get the idea how it [147:45] can be implemented in the mechanical [147:47] engineering context also. Here you can [147:49] see we need to calculate the time [147:51] required for a cooling process. So [147:54] certain data is given. A hot metal block [147:56] is left to cool in ambient air. Okay. [147:59] The block's initial temperature is 500° [148:02] C. So let us define the multiple [148:05] variables in the program and then after [148:07] accordingly we will start our [148:09] calculation. So first define the initial [148:11] temperature. So variable being initial [148:14] temperature equal to 500. Then cooling [148:19] rate [148:21] equal to 15. Then the surrounding air [148:23] temperature is given to be 30°C. So let [148:26] us represent it as ambient temperature [148:28] equal to 30. Now we need to write a [148:31] program using a while loop to calculate [148:33] the time it takes for the blog to cool [148:36] to 50°C or below. Now the initial [148:39] temperature is 500° C. We need to [148:42] calculate the time when it will cool to [148:45] 50°C or below. So let us define the [148:48] target temperature that is being the [148:50] 50°C. So variable being final [148:54] temperature and that is equal to 50. Now [148:57] to get the data of the time we need to [148:59] take one counter that will calculate the [149:01] time. So let us define time counter [149:04] variable being time equal to zero. [149:07] define the current temperature that is [149:09] equal to initial temperature. Now we [149:12] need to simulate cooling process using [149:14] the while loop. Here we do not know the [149:17] exact number of iteration but we know [149:19] the end condition. The end condition is [149:21] if the current temperature is greater [149:24] than the final temperature that is [149:26] greater than 50 then at all that time we [149:29] need to print the time required and the [149:31] temperature at that specific time. After [149:33] that we need to reduce the temperature [149:36] also based on the cooling rate and the [149:38] cooling rate provided being the 15°C per [149:41] minute. So we will reduce the current [149:43] temperature by minus5 and with this [149:46] reduction we will increase the time [149:48] being the 1 minute. So for that let us [149:51] start our while loop and this while loop [149:54] let us comment the same while loop to [149:57] simulate pulling process. Write the [150:00] while loop. So we will write while then [150:02] condition. condition being a current [150:03] temperature greater than final [150:06] temperature then symbol colon. If this [150:09] condition is true then we will print. [150:11] Let us provide the proper formatting. So [150:13] f within double quote define time. Then [150:16] we will print the actual time that is [150:18] the variable. Then it will be in [150:20] minutes. So let us define minutes. then [150:24] temperature colon again print the actual [150:27] value that is the current temperature [150:29] and it will be in degree Celsius. So [150:32] define degree Celsius. Now we need to [150:36] update the current temperature. So [150:38] current temperature will get reduced by [150:41] the cooling rate. So minus equal to [150:43] cooling rate. So this statement [150:45] indicates the reduction in temperature. [150:48] So let us comment reduce temperature. [150:51] Next we need to increment the time [150:53] counter also. So let us define time plus [150:56] equal to 1. And this indicates the [151:00] incremented [151:01] time counter. So this process will [151:04] continue till the current temperature is [151:08] being greater than final temperature. [151:10] After completion of the while loop, let [151:12] us remove the indentation and print the [151:15] final result. So comment print final [151:18] result. We need to print this same data. [151:21] So let us copy this data. Print over [151:24] here again that will indicate the final [151:26] time and the final temperature after [151:28] completion of that while loop. And let [151:30] us again provide the final printing [151:32] statement which indicates our exact [151:34] output. So formatted to the metal block [151:39] has cooled to certain temperature in [151:42] certain time. So for that let us define [151:44] the temperature degrees C in given time. [151:48] So time being the variable and unit [151:51] being the minutes. So here as you can [151:54] see we have prepared one program which [151:56] will simulate the decrement in time [151:58] based on the given data. So run the [152:01] program. So here as you can see based on [152:04] the iteration at the zerooth minute the [152:06] temperature being the 500°C [152:09] accordingly based on the cooling rate it [152:11] will reduce the temperature and to reach [152:14] the temperature of 50° C as you can see [152:17] the metal block has cooled to 50°C in 30 [152:20] minutes. I hope you got the idea how to [152:23] use the while loop to run the Python [152:25] program based on the requirement. [152:27] Remember the key to mastering while [152:29] loops lies in understanding the [152:31] condition and ensuring it eventually [152:33] stops preventing the infinite loops. So [152:36] keep practicing and you will soon find [152:38] yourself using this concept seamlessly [152:40] in your projects. I hope this session [152:43] inspires you to tackle more complex [152:46] engineering problems with the [152:47] confidence. With the end of this, see [152:49] you all in the next session. Hello [152:52] everyone. In this session, we will [152:55] discuss about the important part of [152:58] looping that is the loop control [153:00] statements. We will explore the break [153:03] and continue statements which will allow [153:05] us to fine-tune how our loops operate. [153:08] These statements give us the power to [153:10] alter the normal flow loops making them [153:12] incredibly versatile for solving the [153:14] real world problems. Also, for instance, [153:18] you might want to stop a loop early when [153:20] the specific condition is met or you may [153:23] want to skip certain iterations that [153:25] don't meet your criteria. So, by [153:28] understanding and applying these control [153:30] statements, you will be able to make [153:31] your code more efficient and better [153:33] suited to handle the complex situations. [153:36] Also, let's get started and see how [153:38] these statements can bring precision to [153:40] our loops. To understand the break and [153:43] continue statement, we will take [153:45] examples and understand this concept [153:47] with that same examples only. So go to [153:50] the VS code to experiment the same. So [153:52] here the first example is for the break [153:55] and this break statement is used to exit [153:58] the loop completely regardless of [154:00] whether the loop condition is true or [154:03] not. Once the break statement is [154:05] executed, the loop stops but the [154:08] remaining part of the program continues [154:11] with the given data. So as you can see [154:14] in the example, the for loop is given [154:16] for i in range 1 to 10. Here the [154:19] condition is given if i= 5 then break. [154:23] The loop will start from 1 then 2 3 4. [154:28] Once the value five comes it goes into [154:30] the if. So 5= 5 that is true and it will [154:34] execute the keyword break. So the break [154:38] means the loop stops and the remaining [154:41] part of the program continues. So let us [154:43] run the program here. As you can see it [154:46] will print up to 1 2 3 4 only. As the [154:49] value of five comes we come out from the [154:51] four loop and after for loop there is no [154:54] any other data given. So the program [154:56] stops. Let us change the data. Suppose [154:59] after for loop I am writing the printing [155:01] statement program over and I am changing [155:04] the condition being the if I equal equal [155:07] 6. So now our program will start from 1 [155:11] to 5 at the six break gets executed. We [155:14] come out of the for loop and the [155:16] remaining part of the program continues. [155:19] So again run the program here you can [155:21] see that the loop started from 1 to 5. [155:23] The range is given from 1 to 10. But [155:26] with the satisfaction of the specific [155:28] condition and the execution of the [155:30] break, we exit from the loop. After [155:33] exiting the loop, the program [155:34] continuous. So that's why as you can see [155:37] outside the for loop in the program, the [155:39] statement return is program over. So [155:41] this statement also gets executed in the [155:44] program. So the last statement is [155:46] program over and then program stops. So [155:49] this break statement is very much [155:51] helpful when we want to stop our [155:53] iteration based on the satisfaction of [155:55] the specific conditions. Let us go to [155:58] the continue statement. This continue [156:01] statement skips the rest of the code [156:03] inside the loop for the current [156:05] iteration and goes back to the loop [156:08] condition to continue with the next [156:09] iteration. That means in the case of [156:12] break loop we are exiting the loop but [156:15] in the case of continue statement we are [156:18] skipping one specific iteration based on [156:21] the satisfied condition only. After [156:23] skipping that iteration our loop will [156:26] continue for the remaining number of [156:28] iterations. Consider the example as you [156:30] can see for loop is started from 1 to [156:33] 10. Here if I equal equal 5 that means [156:36] continue. Continue means we will skip [156:39] the current iteration and go to the next [156:41] iteration. So in this program the loop [156:44] skips the printing of five because of [156:46] the continue statement but continues the [156:48] loop for the remaining values. Run the [156:51] program and check the output here. As [156:53] you can see it has printed 1 2 3 4 but [156:56] five is skipped and then again printed 6 [156:59] 7 8 9. So with the satisfaction of the [157:02] specific condition the continue [157:05] statement skips the current iteration [157:07] and go to the next iteration. It do not [157:10] directly stop the loop. So the break [157:12] statement is useful to stop the loop. [157:14] Whereas continue statement is useful to [157:18] skip the specific iteration based on the [157:21] requirement. So here we have explored [157:23] the powerful concepts of loop control [157:25] statement that is break and continue and [157:28] learned how they can help us to [157:30] fine-tune our loops to solve specific [157:32] problems more efficiently. These tools [157:35] are very much useful for optimizing some [157:38] of the processes and avoiding [157:40] unnecessary iterations. Also I encourage [157:43] you to practice these concepts and [157:45] experiment with applying them to various [157:47] problems in your field. with time and [157:50] practice you will see how much they can [157:52] enhance your programming skill and [157:54] problem solving abilities. So keep [157:56] exploring, keep coding and I will see [157:58] you in our next session. [158:01] Welcome back everyone. In this session [158:03] we will discuss about the nested loops. [158:06] Nested loops allow us to handle complex [158:08] and repetitive task by combining the [158:10] loops within the loops. This concept is [158:13] same as the nested if else case in which [158:16] we have already understood how to use [158:18] the if inside the if. So in the same [158:21] case we will use the loop inside the [158:24] loop. This concept is especially useful [158:27] when we are dealing with the matrix [158:29] grids all the multi-dimensional problems [158:32] related to the mechanical engineering [158:34] field. It may be helpful to solve [158:37] intricate problems in mechanical [158:39] engineering like stress distribution [158:41] across multiple sections or flow [158:44] simulations in a pipeline system. In [158:46] this session, you will understand how [158:48] nested loops work, how to structure them [158:51] effectively and how to apply them in the [158:54] programs based on the requirement. Let [158:56] us first check the syntax for the nested [158:59] loop. So as you can see suppose we want [159:02] to use the for loop inside the for loop. [159:04] So we will write first for outer [159:06] variable in outer range colon. Inside [159:10] that we will again write indented for [159:13] inner variable in inner range. Then [159:16] provide colon and within that indented [159:19] we will write our code that we want to [159:21] execute. Here the inner loop runs [159:23] completely for each iteration of the [159:26] outer loop. Let us understand this [159:28] syntax using the practical example. So [159:31] for that go to the VS code here. Suppose [159:33] we want to print the multiplication [159:35] table using the nested loop. If we are [159:38] about to represent our data in two [159:40] dimension, then the outer loop controls [159:43] the number of rows and the inner loop [159:45] controls the number of columns. As we [159:47] want to print the multiplication table, [159:49] the number of rows will be from 1 to 10. [159:52] So to keep the track of number of rows, [159:55] we will write our outer loop. So the [159:57] outer loop range will be 1 to 10. So for [160:00] that the syntax will be for i in range 1 [160:03] to 11. Here 11 gets excluded as we have [160:07] already understood in the range [160:09] function. Next suppose we want to write [160:12] the multiplication table for the numbers [160:14] 1 to 5. So to keep the track of that [160:18] numbers we are providing the number of [160:20] columns. So in the first column we want [160:23] to write the table of one. Then in [160:24] second column the table of two and same [160:27] way in the last column that is in the [160:29] fifth column the table of five. So to [160:32] keep the track of five columns we are [160:34] again writing the for loop inside the [160:36] outer for loop that is for J in range 1 [160:40] to 6. So here inner loop controls the [160:43] columns being 1 to 5. Here we want to [160:46] print the multiplication table. So we [160:49] are printing I into J. That means it [160:52] will print the product of numbers in the [160:54] same line separated by deps and the data [160:57] will be printed like 10 rows and the [161:00] five columns. After the print function [161:03] as you can see outside the inner loop [161:06] but inside the for loop we have [161:07] mentioned the print statement and it [161:10] ensures that after each iteration of the [161:12] inner loop we move to a new line. Let us [161:16] run the program to check the output. So [161:18] here as you can see in the first column [161:20] we are getting the table of one then two [161:24] then three and same way the table of [161:26] five we have printed. So to perform the [161:28] operations in the two dimension based on [161:31] the requirement these nested loop [161:33] concept is very much helpful during [161:35] using the nested loop there are some of [161:37] the key points that one must need to [161:39] remember. First important point is we [161:41] need to provide the proper indentation [161:44] for defining the loop blocks. We must [161:46] need to avoid the infinite loops by [161:49] ensuring loop conditions that eventually [161:51] becomes false. And using this loop [161:54] concept, we can also iterate over the [161:56] list, tpples, strings and dictionaries [161:59] based on the requirement. This concept [162:01] is very much useful in the field of [162:03] mechanical engineering where the [162:05] iteration through time steps in [162:07] simulation is required and it is used to [162:10] solve the differential equations also. [162:13] Thank you for continuing to this session [162:15] on the looping structures. As we know [162:18] now the loops are essential for writing [162:20] the efficient and dynamic programs. So [162:23] practice what you have learned till now [162:26] and you will soon find loops becoming an [162:28] integral part of your coding toolkit. [162:31] And again keep a note that these nested [162:33] loop concept is not only for the for [162:36] loop but you can use the same concept [162:38] for the while loop also. So you may [162:41] practice the same. So keep practicing, [162:43] keep learning and see you all in the [162:45] next session. Hello everyone and welcome [162:48] to this session. Today we are going to [162:50] explore how programming concepts like [162:52] loop and conditional statements can be [162:54] applied to solve the mechanical [162:56] engineering problems. We have already [162:58] understood how to use the loop and the [163:00] conditional statements. So let us start [163:03] our discussion by considering the first [163:05] example relevant to the mechanical [163:07] engineering field. So the data is given. [163:10] We need to inspect the hardness level of [163:12] a metal shaft at the various [163:14] cross-section. The shaft is subjected to [163:16] the different manufacturing process [163:18] which may result in varying hardness [163:20] values at the different section. The [163:22] target hardness level for the shaft is [163:24] set at 50. So let us define the [163:28] parameters. The first is target hardness [163:32] that is 50. Next, we need to evaluate [163:35] each section's hardness and classify it [163:37] as low, high or normal based on the [163:40] comparison with the target hardness. The [163:42] condition is given. If the hardness is [163:44] below the target, it is considered low. [163:46] If it is above, it is considered high. [163:49] And if it is equal to the target, then [163:51] it is considered as the normal. The [163:53] program should iterate through all the [163:56] sections and display the corresponding [163:58] classification for each section. Now let [164:01] us assume we are having the hardness [164:03] levels at the different sections. So for [164:05] the five section the hardness values are [164:08] given. As we want to store all the [164:10] hardness values and want to compare it [164:12] with the target hardness level we will [164:14] store all the similar values into the [164:16] one variable and for that we will use [164:19] the concept of list. So define hardness [164:22] levels equal to let us create a list [164:25] with the square bracket having the [164:27] values 45 55 50 48 and 60. Now to [164:32] indicate the different section let us [164:34] define the variable section being the [164:37] starting value 1. Now to iterate through [164:40] all the hardness levels and compare it [164:42] with the target hardness we need to use [164:44] the for loop. So four the value being [164:48] hardness in the variable of list type is [164:51] hardness level. So use the same variable [164:53] hardness levels. Within this we want to [164:56] print the details based on the [164:58] comparison. So if hardness is less than [165:01] target hardness we will print our data [165:04] in a specific format. So let us type [165:06] print use the function section then its [165:11] value colon hardness then again its [165:15] value then dash and for this condition [165:18] we need to type the low copy this [165:20] statement as this will be needed in our [165:23] further conditions also we need to go [165:25] for the second section. So we will [165:27] increase the value of section and if [165:29] this condition is satisfied we do not [165:31] need to check for the other conditions. [165:33] So to skip the iteration we will use the [165:36] control statement continue. Take the [165:38] next condition al if hardness greater [165:41] than the target hardness we will again [165:44] print the same statement but here we [165:46] need to print the value I again increase [165:49] the section plus equal to 1 and continue [165:52] the iteration. If none of these [165:54] condition is satisfied, we will go to [165:56] the else case and here we will print the [165:59] hardness of the section being normal and [166:02] again increase the section value to be [166:04] plus equal to one. So as you can see in [166:06] the program our program will iterate [166:09] through each value available in the [166:11] hardness levels that is 45 55 and so on [166:15] and compare it with the target hardness. [166:17] If the value is lower then it will print [166:19] the low. If the value is higher it will [166:21] print the high and if it is same then it [166:24] will print the normal. To check that run [166:26] the program. So as you can see for the [166:29] section one hardness is low for the [166:31] second section hardness is high and for [166:34] the third section hardness being the [166:35] same. So it has printed normal. So based [166:38] on the provided values we are getting [166:40] our output. So I hope you got the idea [166:43] how to combine these loop statement and [166:46] conditional statement to get the [166:47] fruitful result. We will again discuss [166:50] about the second example in our next [166:52] session. Till then thank you. Hello [166:55] everyone. In this session again we will [166:57] continue our discussion regarding to the [167:00] example related to conditional statement [167:03] and loop statement and the use of same [167:05] in the mechanical engineering problem. [167:08] So go to the next example. Here you have [167:10] been given data that we need to evaluate [167:13] the flow rate across different section [167:15] of the pipeline that transports the [167:17] water. The pipeline consists of several [167:19] sections with varying diameters and the [167:22] pressure drops. Here the goal is to [167:24] determine if the flow rate in each [167:26] section is within an acceptable range [167:29] and the acceptable range provided is [167:31] between 10 lit/s to 50 lit per second. [167:34] So some of the parameters are given for [167:36] the each section. Let us first define [167:39] all the parameters in the format of [167:42] variables. So here as you can see the [167:45] target flow rate at the lower side being [167:48] 10 the target flow rate at the maximum [167:51] acceptable range that is 50. The pipe [167:54] diameters are given in the list format [167:56] that is 0.05.18 [168:00] and 07. Same way the pressure drops are [168:03] given for the same section that is 300 [168:06] 500 400 and 350. The pipe length given [168:10] being the 100 m and the frictional [168:12] factor is given to be 0.02 and it is [168:16] assumed to be constant. Now the further [168:18] data given is we need to use the Darcy [168:21] waste base equation to calculate the [168:23] fluid velocity. Next we need to [168:25] calculate the flow rate and again we [168:27] need to print if the flow rate is within [168:31] the acceptable range or not. That means [168:33] if it is low, normal or high. Now for [168:35] the conversion purpose also we have been [168:37] given data that we will use the [168:40] conversion factor of 1,000 to convert [168:42] cubic m/s to L/ second but being in the [168:46] field of mechanical engineering we again [168:48] must need to have the knowledge of unit [168:50] conversion based on the need. It is not [168:53] necessary that in every programs the [168:55] same details will be given. Now we know [168:58] that to calculate the flow rate through [169:00] each section we need to iterate through [169:02] each section and for the iteration [169:04] purpose we are having the concept of [169:06] loops. So initially let us create for [169:10] loop and that loop will iterate through [169:13] each pipe diameter and the pressure drop [169:16] at specific section. So here the [169:18] conversion factor we have stored as a [169:20] variable and next we have started our [169:23] loop that is we will start with section [169:26] one for i in range the length being the [169:30] pipe diameters. So for these four [169:32] different diameters we have considered [169:34] the length. Now we will iterate through [169:37] each diameter and each pressure drop. So [169:39] diameter equal to pipe diameter of I. I [169:42] refers to the index position 0 1 2 based [169:46] on the length provided. Same way the [169:48] pressure drop at the same section is [169:50] considered as pressure drops of I from [169:53] the given list. Next we need to [169:56] calculate the fluid velocity and the [169:58] equation for the same is 4 LV²ide by 2 [170:02] GD. So based on that we need to make V [170:05] being the major variable and remaining [170:07] all the variables on the right hand [170:09] side. So the velocity equal to the [170:12] equation reframed as pressure drop into [170:14] diameter divide by 4 into f into pipe [170:19] length and whole ra to 1x2 that is the [170:22] 5. Next we need to calculate the flow [170:25] rate and the equation of flow rate being [170:27] the area into velocity. Velocity we have [170:30] already calculated in the previous step [170:32] and it is stored into the variable named [170:34] velocity. Then it will multiply with the [170:38] cross-sectional area. So the equation [170:40] for the same is also mentioned. Now the [170:43] answer here obtained is the me cube/s. [170:46] So we need to convert it into the lit [170:48] per second. And we may need to use the [170:51] conversion factor to convert it into the [170:53] lit per second. So we have mentioned [170:56] flow rate liter/s equal to flow rate m [170:59] cube second into conversion factor that [171:01] is 1,000. It is not mandatory to store [171:04] the conversion factor into the variable. [171:06] You may directly mention the same value [171:09] during the calculation also. Now next we [171:12] need to check based on the given data if [171:15] the flow rate is within acceptable range [171:17] or not. We must need to use the if else [171:20] statements. So if the flow rate lower [171:22] side is less than the target flow rate [171:25] then we will print the data for the [171:28] specific section as we have used the [171:30] formatting in our previous question [171:32] also. And here we will print the final [171:35] data being the low flow. Same way in al [171:38] if flow rate at the lower side is [171:40] greater than the target flow rate at the [171:43] higher level. So we need to print the [171:45] final answer to be high flow. And in the [171:48] else case if both are same. If it is in [171:51] the given range then we will print the [171:53] normal flow. And after getting any of [171:56] the answer we will go to the next [171:58] section. So to increase the section we [172:00] will consider to be the section plus [172:02] equal to 1. So using this concept our [172:05] program will go through each diameter [172:08] each pressure drop then it will [172:10] calculate the velocity flow rate and it [172:13] will check if the data is available [172:15] within the acceptable range or not. Run [172:18] the program to check the output. So as [172:20] you can see at the section one flow rate [172:22] we obtained is 2.69 lit/s and it is the [172:26] low flow. Same way you may read the [172:29] values for the multiple sections also. [172:31] And if there are more number of sections [172:33] then you can also include in the given [172:35] list. So this combination of loop and [172:38] the conditional statement is very much [172:40] helpful to come to the conclusion for [172:42] some of the complex problems and to sort [172:45] it out in a very simple way. I hope [172:48] these examples have not only enhanced [172:50] your understanding of Python programming [172:52] but also sparked ideas on how to apply [172:55] these concepts in your engineering work. [172:58] Remember the true power of programming [173:00] lies in its ability to solve problems [173:03] and save the time. I suggest you all to [173:05] keep experimenting, keep learning and [173:07] let Python simplify your engineering [173:10] challenges. See you all in the next [173:12] session. Hello everyone and welcome. [173:15] Today I want to discuss about an [173:16] incredible tool that can make learning [173:19] and mastering conditional and loop [173:21] statements lot easier that is jet GPT. [173:24] We have already used the chat GPT in our [173:26] previous module also to understand some [173:29] of the primary concepts. As we dive [173:31] deeper into the programming concepts [173:33] especially in Python understanding how [173:36] to use if statements, for loops and [173:38] while loops effectively is crucial. [173:41] These structures form the foundation of [173:43] logic and repetition in any program. But [173:46] sometimes figuring out how to structure [173:48] your conditions or loops for a specific [173:51] problem can feel difficult. This is [173:53] where Jet GPT can help. Whether you are [173:56] stuck on a syntax, need an example for a [173:59] mechanical engineering problem or want [174:01] clarification on a complex concept, Jet [174:04] GPT is essential tool. You can ask for [174:07] explanations, generate examples, debug [174:10] your code and explore advanced use cases [174:13] also. You may challenge yourself with [174:16] complex scenarios and let Jet GPT guide [174:18] you step by step in that manner also. So [174:21] JGPT is not just a source of answers, [174:24] but it's a companion in your learning [174:26] journey, helping you think logically and [174:29] apply concepts to real world problems. [174:32] So as you work through your conditional [174:34] and loop statements, don't hesitate to [174:36] reach out to chat GPT for assistance. [174:39] Let's use this powerful tool to make [174:41] your programming journey smoother, [174:43] faster, and more engaging. Go through [174:46] some of the important prompts that may [174:48] be useful to understand how to provide [174:50] the prompt to chat GPT to get the [174:52] effective answers. So these are list of [174:56] some of the important prompts that you [174:57] may use. Again you may modify this [175:00] prompt, update this prompt or provide a [175:03] new prompt based on your requirements. [175:05] Let us go to Jet GPT to understand how [175:08] Jed GPT will provide the answers to our [175:11] provided prompts. Suppose I want to [175:13] understand break and continue statement [175:16] in and its use in the context of [175:18] mechanical engineering. So we may ask [175:22] the chat GPT also explained use of break [175:27] and continue statement in mechanical [175:30] engineering context using Python. Hit [175:34] enter. So you may see that it will give [175:37] you the basic information and the idea [175:40] where you may use this specific tool. So [175:43] it has given the information about the [175:46] break statement. Then one example same [175:48] way the information about the continue [175:50] statement and the example related to the [175:53] continue statement and at last it has [175:56] provided the summary. Now suppose you [175:58] want to understand the syntax of nested [176:00] for loop using example. So again you may [176:03] provide the same prompt explain syntax [176:06] of nested let us say while loop with [176:10] example and hit enter. [176:18] So here you can see that it has provided [176:20] the syntax then example related to the [176:24] multiplication table which we have [176:26] already seen in the context of for loop. [176:29] Suppose if you want to have the [176:30] different example. So again you may ask [176:32] the chat GPT please provide different [176:35] example. So we will ask provide [176:38] different example [176:41] in context of mechanical engineering. So [176:45] here you can see that the jet GPT is [176:48] provided the scenario considering the [176:50] pressure test simulation for the [176:52] multiple walls. So here it has provided [176:55] some of the data calculations and the [176:57] explanations for the code and the [177:00] expected output also. So I hope you got [177:03] the idea how this tool is very much [177:06] helpful to gain more knowledge and more [177:08] insights in depth in important topics of [177:11] Python. So remember learning programming [177:14] is a process and sometimes we all hit a [177:17] roadblocks but with tools like Jet GPT [177:20] you have a powerful resource at your [177:22] fingertips whether it's refining your [177:25] code gaining a better understanding of [177:27] concepts or working through challenging [177:29] examples that GPT can provide the [177:32] support you need to succeed. The more [177:35] you practice and experiment the better [177:37] you will become at solving complex [177:39] problem with the knowledge you have in [177:41] Python. Thank you again and I wish you [177:43] the best of luck in the practice of [177:45] programming. Keep exploring, keep coding [177:48] and remember the Jet GPT is always here [177:51] to assist you. Hello everyone, welcome [177:54] to the module 3 that is functions and [177:57] modules. If programming is like building [177:59] a machine, functions are the individual [178:02] tools you use to assemble and operate [178:04] it. They help you to break down complex [178:06] problems into smaller, manageable [178:08] pieces, making your code efficient, [178:10] reusable, and easier to understand. We [178:13] will start by understanding what [178:14] functions are, why they are so crucial, [178:17] and how they empower us to write clean [178:19] and organized code. From simple [178:21] mathematical operations to real world [178:23] engineering problems, you will see how [178:25] functions can handle everything. After [178:28] mastering functions, we will explore [178:30] modules which are like pre-built [178:31] toolkits in Python. Imagine having an [178:34] entire library of solutions ready to use [178:37] for your task. This is what modules [178:39] offer. Whether it's performing complex [178:41] mathematical calculations, working with [178:43] data, or solving engineering problems, [178:46] Python modules make it all possible with [178:49] just a few lines of code. By the end of [178:51] this module, you will not only be able [178:53] to create your own functions, but also [178:56] utilize powerful Python modules to [178:58] tackle challenges in mechanical [179:00] engineering and beyond. So let's gear up [179:02] and get started with the functions and [179:04] modules your building blocks for [179:06] efficient and professional programming. [179:08] Let's start by imagining these. You are [179:11] working on a complex problem in [179:12] mechanical engineering. Perhaps [179:14] designing a simulation for stress [179:16] analysis or fluid flow. The problem is [179:19] large with multiple repetitive [179:21] calculations and steps. Now wouldn't it [179:23] be great if you could simplify this task [179:26] by organizing your code into smaller [179:28] reusable components? Well, that's [179:30] exactly where functions come into play. [179:33] Functions are the backbone of modular [179:35] programming in Python. A function is [179:38] essentially a block of organized, [179:39] reusable code designed to perform [179:42] specific task. Instead of writing the [179:44] same code multiple times, you can define [179:46] a function once and reuse it as needed. [179:50] This not only saves time and effort, but [179:52] also makes your program cleaner, more [179:54] readable, and easier to debug. functions [179:57] are particularly important in [179:59] engineering applications. Whether you [180:01] are performing calculations like [180:03] determining stress or strain, automating [180:05] data analysis or solving equations, [180:08] functions let you encapsulate logic into [180:11] reusable pieces. This means you can [180:14] focus on solving the bigger problem [180:16] rather than worrying about the [180:17] repetitive task. So let us discuss about [180:19] the syntax of a function. How we can [180:22] create a function in Python language. So [180:24] as you can see on the screen in Python [180:26] creating a function starts with the [180:29] defaf keyword followed by the function [180:31] name and parentheses for the inputs and [180:34] at last colon. Inside the function you [180:37] may write the logic you want to execute [180:39] but you need to keep in mind that the [180:41] indentation is important and if needed [180:43] the function can also return the value [180:46] or result. So in short input values to [180:49] function are known as the arguments or [180:51] parameters and output of function is [180:54] referred as return values. Let us take [180:56] an example to create a function and use [180:59] the same in the Python language. So for [181:01] that go to the VS code here. As you can [181:03] see we want to create one function named [181:06] gaze out and the requirement of that [181:09] function is to greet the person that is [181:11] the hello learners. So to create that [181:13] function first we will use the def that [181:16] is def then function name it is gazo [181:19] then parenthesis and in these we do not [181:23] require any argument or parameters. So [181:25] these parentheses are kept empty. Then [181:27] after colon symbol is provided and [181:30] within that whatever data is required in [181:32] the function that all will be returned [181:35] after the colon but the indentation is [181:38] mandator. So here we have mentioned [181:39] print hello learners. After creating the [181:42] function the major important part is to [181:44] call that function means to use that [181:46] function such that we will get the [181:48] output. So to call that function [181:50] removing indentation in the further [181:52] lines we need to type the function name [181:55] then after parenthesis and within [181:57] parenthesis if arguments are provided [182:00] then same type of arguments are needed [182:02] in the parenthesis and if no arguments [182:05] are provided then same way during [182:07] function calling no arguments are [182:09] needed. After calling the function if [182:11] any further code is required we may also [182:13] write. So to just get the clarity if [182:16] function is over or not after calling [182:18] the function we have mentioned the print [182:20] statement saying we are outside the [182:22] function. Now run the program to check [182:24] the output. So here as you can see due [182:26] to this g how line. Once this code of [182:29] block gets executed first function is [182:32] executed indicating hello learners. [182:34] After completion of the function, next [182:36] line will get executed and it will print [182:39] outside the function. So the same thing [182:41] you can see in the terminal also. Now [182:44] you can reuse this function as many [182:46] number of times as possible based on [182:48] requirement also. So we may say there [182:51] are so many benefits of using functions. [182:54] Some of them are we need to just write [182:56] the code once and we may use it multiple [182:58] times. There is also possibility of [183:01] breaking down large problems into [183:03] smaller for the logical part due to the [183:05] concept of the function. The programs [183:07] becomes more structured and easier to [183:10] follow. The creation of functions are [183:12] also helpful for the easy debugging of [183:14] the code. So fixing an issue in a [183:16] function automatically corrects it [183:18] wherever the function is used. Let us [183:20] again come back to the slides. Now we [183:22] will take another example also. Here you [183:25] can see that we have provided the [183:27] function name calculate rectangle area. [183:30] The def is already mentioned and within [183:32] round brackets two arguments are [183:34] provided length, width. Now the [183:37] objective is to calculate the area. So [183:39] after colon within the function we are [183:41] calculating the area area equal to [183:43] length into width. And if you want to [183:45] store this answer into the method then [183:48] we may use the return statement also. So [183:50] this return keyword is used to store the [183:53] output into the method name. So here [183:56] calculated area will be returned to the [183:59] given function and that will be our [184:01] output. Here you can see that the return [184:03] statement should be the last statement [184:05] for any of the function. Now to run that [184:07] we require the two argument that is [184:09] length equal to 5, width equal to 3. To [184:11] calculate the area we will call the [184:13] function and we want to store that [184:15] answer. So area equal to calculate [184:18] rectangle area and within parenthesis we [184:20] will provide the two arguments needed. [184:22] So here we have passed the variables L [184:25] comma W. Finally we have printed our [184:27] answer using the proper formatting [184:29] options. So here again you can see that [184:31] this function can now be utilized [184:34] anywhere in the program and it will [184:36] perform the same. So with this session I [184:38] think you got the idea what is the [184:40] function is how we may use the function. [184:43] With this session, I hope that you got [184:45] the idea how to create a specific [184:47] function and how to call that function. [184:50] We will discuss in detail about the [184:52] functions and its usage in our further [184:54] session. Hello and welcome. In this [184:56] session, we will be diving deeper into [184:58] two key components of Python functions [185:00] that are absolutely crucial to [185:02] understand arguments and return types. [185:04] So, let's start with the arguments. [185:06] Arguments are like the inputs you [185:08] provide to a function. When you create a [185:10] function, you often want it to work on a [185:12] specific data. That's where arguments [185:15] come into play. They allow you to pass [185:17] values into a function so that it can [185:19] perform its task on those values. For [185:22] example, imagine you are calculating the [185:24] area of circle. Instead of hard- coding [185:26] the radius, you can create a function [185:28] where the radius is an argument. When [185:31] you call the function, you provide the [185:33] value of the radius. The function [185:35] calculates the area based on that value. [185:37] This makes your function more flexible [185:39] and reusable. In Python, you define [185:42] arguments inside the parenthesis of a [185:44] function. You can have as many arguments [185:46] as you need. You can even set default [185:49] values. So if the user doesn't provide [185:51] an input, the function uses the default [185:53] value. So to understand the part of [185:55] arguments, let us go to the VS code [185:57] where we have already created one [185:59] program. Here you can see that we have [186:01] created one function with the keyword [186:03] dev. The function name is calculate [186:05] stress. And here to calculate the stress [186:08] two arguments are required that is force [186:11] and area. So as you can see the first [186:13] argument provided is force and the [186:15] second argument is area. And here you [186:18] can see that the argument area is [186:20] provided with the default values. So if [186:22] user do not provide any other value then [186:25] the default value for the area will be [186:27] considered to be 50. After providing the [186:29] arguments colon symbol is provided and [186:31] within that the required operation is [186:33] performed. So within these parentheses [186:36] the arguments are provided and in future [186:39] while calling the function we will [186:41] provide the value of force and the area [186:43] based on requirement. Now let's move on [186:45] to the return type. Let us again go back [186:47] to the slide. Now when a function [186:49] complete its task, it often needs to [186:51] send the result back to the part of the [186:53] program that called it. This is done [186:55] using the return statement. So think of [186:57] it as the output of the function. For [186:59] instance, consider the first example. [187:02] Let's consider the area of circle that [187:04] we want to calculate. After calculating [187:06] the area, function can return the value [187:09] so that you can use it elsewhere in your [187:11] program. Maybe you can print it, save it [187:13] to a file or use it for the another [187:15] calculation. So here is why arguments [187:18] and the return types are so important. [187:20] The arguments allow you to make your [187:22] function dynamic. Instead of working [187:24] with fixed values, your function can [187:26] handle variety of inputs. Written types [187:28] allow your function to send results back [187:30] to the main program making them highly [187:32] versatile and enabling complex workflows [187:34] for better programming. So if we [187:36] summarize these arguments are the inputs [187:39] that you give to the function and the [187:41] return types are the outputs you get [187:43] from the function. Let us again go back [187:45] to our program. So here you can see that [187:48] after colon we have calculated the [187:50] stress that is equal to force divide by [187:52] area and we want to return the value of [187:54] stress to this function. So as you can [187:57] see the keyword required is written and [187:59] then after we will provide the value [188:01] that we want to return. In our case we [188:03] want to return the stress. So we have [188:05] mentioned stress. Now to call that [188:07] function we need to provide the two [188:08] arguments. In our case we have provided [188:11] the fixed area that is 50. So we need to [188:13] provide the value of force. So for that [188:15] we have defined the value of force that [188:17] is equal to,000. Now we need to store [188:20] the value of stress that has been [188:21] calculated using the formula. So we have [188:24] stored it into the variable named stress [188:26] with default area equal to we are [188:28] calling the function calculate stress [188:31] and as you can see the default [188:33] cross-sectional area is also provided. [188:35] So we need to just provide one argument [188:37] that is the force. So calculate stress [188:39] within bracket we have provided the [188:41] argument to be force. So the value of [188:43] force that is 1,000 will pass into this [188:45] function. Area will be considered to be [188:47] the 50. Now the stress will be [188:50] calculated that is force divide by area [188:52] and the answer for that will be returned [188:54] back to that function. This function [188:56] stores the answer into the variable [188:58] named stress with default area. Then [189:00] after we have printed the answer in a [189:03] specific format. Same way you can see [189:05] that if we want to change the area then [189:07] we have stored it into the variable let [189:09] us say custom area that is equal to 75. [189:11] Again we are calculating the stress but [189:13] here we want to change the default value [189:15] of area. So that calculate stress [189:18] function is called by passing the two [189:20] argument. First one is force and second [189:23] one is custom area. So based on these [189:25] two provided values again function [189:27] calculates the stress and the value of [189:29] stress is returned back to our function [189:31] and this value is stored into the [189:33] variable named stress with custom area. [189:36] Finally after calculating the stress [189:38] again the answer is printed with a [189:39] specific format. Let us run the program [189:42] to check the output. So here you can see [189:44] that the stress with default area being [189:46] 20 megapascal and stress with custom [189:48] area being 13.33 mega pascal. So I hope [189:52] you got the idea how to make use of [189:54] these functions and the use of arguments [189:56] and the return parameters to get the [189:58] output based on the requirement. To [190:00] understand it properly again let us [190:02] consider the second example. So here you [190:05] can see that we want to check for the [190:07] safety and based on the stress value and [190:10] the safety limit provided we want to [190:12] check if the part is safe with the [190:15] stress level or part is unsafe with the [190:17] stress level. So for that we have [190:19] defined the check safety function. It is [190:21] having the two arguments stress safe [190:24] limit. Within that function we have [190:26] calculated if stress is less than equal [190:28] to safe limit we will print the part is [190:31] safe. Else we will print the part is [190:33] unsafe. So for that we have considered [190:35] the two inputs that is trace equal to [190:37] 180 and the safe limit being the 200. [190:39] Now to check if the part is safe or not [190:42] we need to provide these two inputs into [190:44] the function and to call that function [190:47] we can simply write the name of function [190:49] that is check safety. Within that we [190:51] will provide the two arguments. First [190:53] being the stress and second being the [190:55] safe limit. Here keep in mind that you [190:58] need to provide the input in the same [191:01] sequence as we have defined in the [191:03] function syntax. So first we have [191:05] provided stress so we need to provide [191:07] the value of stress only. Second we have [191:09] provided the value of safe limit. So we [191:11] need to provide the value of safe limit [191:12] only. So the sequence in which we are [191:14] providing the input is also important to [191:17] get the proper output from the function. [191:19] Now here we can see that 180 is less [191:21] than or equal to 200. So the answer is [191:24] true. So we will get the output. The [191:25] part is safe with a stress level of 180 [191:28] megapascal. So again run the program to [191:31] check the output. So here you can see [191:33] that the part is safe with a stress [191:34] level of 180 megapascal. So using these [191:37] case of functions you can convert your [191:39] bigger program into a smaller sections [191:41] and using that functions you can easily [191:44] manage the different operations of your [191:46] program. I hope with this session you [191:48] got the idea how to create a function, [191:51] how to provide arguments and get the [191:53] return type based on the requirement. [191:55] Looking forward to see you all in our [191:57] further session. Till then thank you. [191:59] Hello everyone and welcome back. Today [192:01] we will discuss about very versatile [192:03] concept in Python functions that is [192:05] arbitrary argument. There are times when [192:07] you might not know in advance how many [192:09] arguments a function will need to handle [192:12] it. That's where arbitrary arguments [192:14] come into play. They allow your [192:15] functions to accept a variable number of [192:18] inputs making them adaptable and [192:20] powerful in real world scenarios. In [192:22] this session, we will explore how to use [192:25] arbitrary arguments, their syntax, and [192:27] how they can be applied to handle [192:29] dynamic data inputs effectively. So for [192:31] that, let's get started with the syntax [192:34] of arbitrary positional arguments. So [192:37] here as you can see star args that [192:40] syntax that part collects all positional [192:43] arguments into a tuple. You can iterate [192:45] through arcs to access individual [192:48] values. So to define arbitrary [192:50] positional arguments we need to write [192:52] star args within the parenthesis of the [192:55] function. So if we consider the syntax [192:57] as you can see on the screen first we [192:59] will provide def then function name and [193:02] within parenthesis we will provide star [193:04] args. Now to iterate through all the [193:06] possible arguments you may use the for [193:08] loop. So as you can see for arg in args [193:12] that is for each element in args we can [193:15] perform different operations based on [193:17] the requirement. If you want to print [193:18] the argument you can simply print the [193:20] argument. Other than this you may [193:22] perform any mathematical operations or [193:24] the required operations based on your [193:26] need within this function. Now to call [193:28] this function we may need to write [193:30] function name. Then within bracket we [193:33] may pass any number of arguments. That [193:35] means we may pass one argument, two [193:37] argument, three argument or any variable [193:40] number of argument. So that is the major [193:43] advantage of using arbitrary positional [193:45] arguments. To understand its use, let us [193:48] go to the VS code and run one program. [193:50] So here you can see that we have created [193:53] one specific function with name [193:55] calculate total load. Within that we [193:57] have provided the variable argument that [194:00] is star loads. So it represents that we [194:03] may provide the any number of load based [194:05] on the requirement. Within that function [194:07] our objective is to find sum of loads. [194:10] So we have stored its answer into the [194:12] variable total load equal to inbuilt [194:15] function used is sum and within its [194:18] argument we have provided all the loads [194:20] that we have passed into the argument of [194:22] function we have created. After [194:24] calculating the total load, we have [194:26] printed the final answer with the [194:27] specific format. Now to call that [194:29] function, as you can see, we have [194:32] provided two different options. First, [194:34] calculate total load function is called [194:36] with the three argument. Again, we have [194:38] also called calculate total load [194:40] function with the four argument. So you [194:43] may also pass two, three, four or any [194:45] number of arguments based on your need. [194:47] So that is the major advantage of using [194:50] these arbitrary arguments. Let us run [194:52] the program to check the output. So here [194:54] as you can see in the first case total [194:57] applied load and in the second case [194:58] total applied load we are getting based [195:00] on the provided value. So I hope you got [195:02] the idea how these arbitrary arguments [195:05] are useful while creating the functions. [195:07] Let us again go back to the [195:09] presentation. Now we will understand the [195:11] syntax for the arbitrary keywords [195:13] argument and as you can see for that we [195:16] need to provide two times star symbol [195:18] and then after the argument name. So [195:21] here we have provided the keyword [195:23] arguments. So the syntax for the same is [195:25] first we will provide def then function [195:27] name and within parenthesis we will [195:30] provide star variable or the arbitrary [195:33] argument name. Now these keyword [195:35] argument refers to the key value pair [195:38] only. So for that we may again iterate [195:40] through each element using the for loop. [195:42] So for key comma value in keyword asks [195:45] dot items. items is the inbuilt function [195:48] that will iterate through each element [195:50] of key and value and we may print the [195:52] key and value based on the required [195:54] format. The advantage we have is that [195:56] while calling the function we will [195:58] provide the function name and within [196:00] bracket we may provide any number of key [196:02] value pair based on our need. So we may [196:04] pass 1 2 3 4 or maximum any number of [196:08] pairs based on requirement while [196:10] creating these keyword arguments. Again [196:13] to understand this syntax let us go back [196:15] to VS code. We have created one function [196:17] that is def display component [196:19] properties. We have provided the [196:21] arbitrary keyword arguments. Within this [196:23] function we have printed component [196:25] properties. We want to print its key and [196:28] value based on the input data. So for [196:30] key comm, value in kox dot items. Within [196:33] that we want to print key and value. Now [196:36] how we can access this combination. So [196:38] for that as you can see we have called [196:40] this function display component [196:42] properties. We are providing the [196:44] multiple key value combinations that is [196:47] name gear material steel diameter 200 mm [196:50] and the weight being the 15 kg. Same way [196:53] suppose if you want to change the key [196:55] value combination then again it is [196:56] possible. So in second function you can [196:58] see that we have provided name soft [197:01] length 1.5 m diameter 50 mm and the [197:04] material to be the aluminum. Now how it [197:07] will give us the output to check that [197:09] let us run the program. So here you can [197:11] see that the key is name and the value [197:14] is gear. Then same way material and [197:17] steel diameter is 200 mm and weight is [197:20] 15 kg. So there are multiple key value [197:24] combinations are provided using the [197:26] equal to operator and all are separated [197:28] by the comma and any number of argument [197:30] any type of argument if you provide that [197:33] will be executed based on the created [197:35] function. So this keyword argument is [197:38] also very much helpful to collect all [197:40] keyword arguments into a dictionary. You [197:42] can iterate through kbox dot items to [197:45] access key value pairs. So let us go [197:47] back to presentation to again understand [197:49] it in detail. So here you can see that K [197:52] works that is keyword arguments collect [197:55] all keyword arguments into a dictionary [197:58] and you can iterate through each item [198:00] using the inbuilt function that is items [198:03] with the parenthesis to access key value [198:06] pairs. We have already seen in the [198:08] example that how to utilize this concept [198:10] to execute the arbitrary keyword [198:12] argument. So why to use these arbitrary [198:14] arguments? So you can see that you don't [198:17] have to you don't have to specify the [198:19] number of loads when defining the [198:20] function. That means you are having the [198:23] flexibility to provide the number of [198:25] loads based on your requirement and the [198:26] program will give you output dynamically [198:29] based on your inputs. It can also handle [198:31] both small and large data sets without [198:34] any modification. So with this [198:36] discussion of arbitrary positional [198:38] arguments and the arbitrary keyword [198:40] arguments, I hope you got the idea how [198:42] to take advantage of these arbitrary [198:44] positional and keyword arguments during [198:47] creation of functions and utilize it [198:49] with the maximum possible outcomes. [198:52] Looking forward to see you all in our [198:54] further sessions. Till then thank you. [198:56] Hello everyone. Today we are diving into [198:58] an essential concept in Python [198:59] programming that is variable scope. This [199:02] is critical topic that determines how [199:04] and where variables can be accessed or [199:06] modified in your code. Whether you are [199:08] writing a simple programs or complex [199:11] systems, understanding variable scope [199:13] will help you avoid errors and write [199:15] clean, efficient and predictable code. [199:17] So let's get started with the variable [199:19] scope. So the variable scope refers to [199:22] the region in a program where a variable [199:24] is accessible. In Python, variables are [199:27] not all treated equally. They depend on [199:29] where and how they are defined. Knowing [199:32] the scope help us understand where a [199:34] variable exist and where it do not [199:36] exist. Python defines four types of [199:38] variable scope. Local scope, enclosing [199:41] scope, global scope and built-in scope. [199:44] These scopes are collectively known as [199:46] legb rule that is leb [199:49] rule which stands for local enclosing [199:52] global and built-in scope. So let us [199:55] have a brief discussion about the logic [199:58] of different scope. So the local scope. [200:00] So if the variable declared inside a [200:03] function belong to the local scope, they [200:05] can only be accessed within that [200:07] function only. Once the function [200:09] execution is complete, these variables [200:11] are automatically discarded. When [200:13] variables defined in an enclosing [200:16] function that is function inside another [200:18] function, these are in the enclosing [200:21] scope. These variables are accessible to [200:23] the inner function but they are not [200:26] global. If variables are declared [200:28] outside any function or block they all [200:30] are belong to the global scope and these [200:33] variables are accessible throughout the [200:35] program unless they are shadowed by the [200:37] local variables and Python has a set of [200:40] built-in functions and keywords such as [200:42] the len range that we are using in our [200:45] for loop also which are always available [200:47] unless overridden. Now to understand how [200:50] these variables can be accessed and [200:52] where they can be accessed, let us go to [200:54] the VS code with one program. We will [200:56] understand the area of different scopes. [200:59] So here in the program as you can see [201:02] one variable is defined global factor [201:04] equal to 1.5 and this variable is [201:06] defined outside the function but inside [201:09] the program. So this variable can be [201:11] accessed throughout the program from [201:14] first line to the last line. So this [201:16] variable is known as the global [201:18] variable. Next we have created the [201:20] calculate torque function. It is having [201:22] two arguments and within these one [201:24] variable is created pi equal to 3.1415. [201:27] So this variable is declared inside the [201:29] function. So it is known as the local [201:31] variable and this variable can be [201:34] accessed up to the limit of this [201:37] calculator function only and this one is [201:39] limited up to this specific area that is [201:42] from line number four to 18. But after [201:45] line number 18 this pi named variable [201:48] gets automatically discarded. Now within [201:51] this calculate torque function you are [201:52] also seeing another function is created [201:55] that is define adjust torque. So within [201:57] this function one variable is created [202:00] adjustment factor. So adjustment factor [202:03] variable is created inside the function. [202:05] The location of these variable is [202:08] function inside a function. So this [202:10] variable is known as the enclosing [202:12] variable and the scope of this variable [202:14] is up to these defined function only [202:17] that is adjustment torque. Next again [202:19] torque variable is created. Adjusted [202:22] torque variable is created. All these [202:24] variables are created within these [202:26] defined calculate torque function only. [202:28] So the scope of that function being the [202:30] local they are limited up to these [202:32] defined function only. After that you [202:35] will see the print function. So we are [202:37] every time printing our required data [202:39] using this print function. So this print [202:42] is inbuilt function and this can be [202:44] accessed anywhere in any of the program [202:47] based on the requirement. So here you [202:49] can see that we have used global [202:51] variable, local variable, enclosing [202:54] variable and built-in functions also to [202:56] show you how these variables can be [202:58] accessed within that scope. So in [203:01] current section all the variables are [203:02] managed very well and utilized very [203:04] well. So we are not getting any error. [203:06] So let us run the program. Here we need [203:08] to input the applied force. So let us [203:11] say that is 1,000. We will provide the [203:13] radius in meter that is being 20. So the [203:16] adjusted torque is calculated based on [203:18] the provided value. So we are not [203:20] getting any error. But now suppose if we [203:22] are accessing these variable outside the [203:25] scope then we will 100% get the error. [203:27] So to understand that let us see this pi [203:30] variable is having local scope up to [203:32] this defined function only. Now suppose [203:35] if I am accessing after the line number [203:37] 18 I want to print variable pi. So here [203:41] you can see that it is representing the [203:43] error that is pi is not defined and it [203:46] is due to the reason that after [203:48] completion of these function [203:50] automatically pi variable gets [203:52] discarded. So knowing the variable scope [203:55] help us to understand where this [203:56] variable exist and where we can access [203:59] this variable based on the need. So I [204:01] hope you got the idea how the different [204:03] variables that we can provide into the [204:06] program and how we can make use of that [204:08] variables based on our requirement. [204:10] Hello everyone and welcome to today's [204:12] session. In this lecture we will explore [204:14] global variables in Python. A [204:16] fundamental concept that plays a crucial [204:18] role in programming when dealing with [204:20] the data across multiple functions. By [204:22] the end of this session, you will have a [204:24] solid understanding of what global [204:26] variables are, when to use them, and how [204:29] they function within Python scope [204:30] hierarchy. A global variable is a [204:32] variable that is defined outside of any [204:35] function, block or class. Unlike local [204:38] variables which are confined to the [204:39] function, they are defined in global [204:41] variables. They can be accessed and [204:43] modified by any part of the program [204:46] provided they met certain conditions. [204:48] They are often used when we want to [204:50] share data across multiple functions [204:52] without passing it explicitly as [204:54] arguments. For example, in real world [204:57] applications, a global variable might [204:59] store a configuration setting or a [205:01] constant value that is relevant [205:03] throughout the program. So imagine you [205:05] are working on a mechanical simulation. [205:07] You have a constant safety factor that [205:09] needs to be applied across different [205:11] calculations such as stress analysis, [205:14] torque calculations, and thermal [205:15] performance evolutions. Instead of [205:17] defining the safety factor in every [205:20] single function, you can declare it as a [205:22] global variable and access it anywhere [205:25] in the program. Here key points to [205:27] remember about global variables are [205:29] global variables are accessible anywhere [205:31] in the program. They simplify code by [205:34] reducing the need for repetitive [205:35] arguments but they should be used [205:38] sparingly. Too many global variables can [205:40] make your code harder to debug and [205:42] maintain. If you need to modify global [205:44] variable inside a function, you must [205:47] explicitly declare it using the global [205:49] keyword. So these global variables are [205:52] incredibly useful when a value is truly [205:55] universal across all the functions. And [205:57] passing the value repeatedly as an [205:59] argument would make the program [206:01] unnecessarily verbose. To understand the [206:03] use of global keyword, let us go to the [206:06] VS code. In this program, as you can [206:08] see, we have provided global variable [206:10] safety factor equal to 1.5. Now we want [206:13] to use this safety factor variable [206:15] inside both the function. So it is [206:17] better that we define the safety factor [206:19] to be the global and for that within [206:22] function we will mention global then [206:24] variable name. We are using these now [206:27] variable torque equal to force into [206:29] radius into safety factor. Due to use of [206:31] these keyword as you can see we are [206:34] reducing the number of arguments [206:35] required during this calculation also. [206:38] Same way in the calculate stress [206:39] function we are providing the global [206:41] safety factor so it can be accessed [206:44] easily as a global variable. Now to run [206:46] this program we require different [206:48] arguments that is force radius load and [206:51] area during the call of function as you [206:54] can see we are just passing two [206:55] arguments force and radius and due to [206:57] the declaration of the global variable [206:59] safety factor can be accessed easily [207:02] throughout the program. So it will [207:04] access the value of safety factor to be [207:06] the 1.5 in both the cases and it will [207:08] give us the output for the calculation [207:10] of torque and stress. Let us run the [207:12] program to check the output. So here you [207:14] can see that we are getting the value of [207:16] torque as well as stress. So I hope you [207:19] got the idea how to use this global [207:21] keyword to access the global variable [207:23] throughout the program. But the one [207:25] important thing to keep in mind is [207:27] overusing the global keyword can lead to [207:29] the unexpected bugs or confusing [207:31] dependencies. So the key is to strike a [207:34] balance. Use global variables for [207:36] constants or configuration data but [207:39] avoid them for intermediate calculations [207:41] or temporary values. So in conclusion, [207:44] global variables are like shared tools [207:47] in workshop. They are convenient and [207:48] powerful, but they should be handled [207:50] carefully to maintain order and [207:53] efficiency in your code. With this [207:55] knowledge, you are now better equipped [207:56] to write Python programs that are [207:58] efficient and easy to manage. So make [208:01] use of all the concepts that we have [208:03] utilized till now that is the defining [208:05] functions, scope of variables and use of [208:08] global keyword and it will help you to [208:11] prepare more solid and understandable [208:13] code. We will dive deeper into the [208:15] Python programming in our further [208:17] session with the concept of modules. [208:19] Hello everyone and welcome to today's [208:21] session on Python modules. Today we will [208:23] dive deep into one of Python's most [208:26] powerful features that is modules. [208:28] Whether you are working on a simple [208:30] script or a large project, modules are [208:32] essential for creating organized, [208:34] efficient and reusable code. By the end [208:37] of this session, you will understand [208:38] what modules are, how to use them, and [208:41] why they are critical in Python [208:43] programming. A module in a Python is [208:45] simply a file that contains a Python [208:48] code and it can include functions, [208:50] variables, classes, and even runnable [208:53] code. Modules allow us to break down [208:55] complex programs into a smaller [208:57] manageable pieces making the code more [209:00] readable and reusable and essentially [209:02] they enable us to write a modular code. [209:05] So why do we use modules? So once a [209:08] module is created it can be used across [209:10] multiple programs. Code can be divided [209:12] into logical parts improving readability [209:15] and maintainability. In large teams, the [209:17] different members can work on a separate [209:19] modules and the Python comes with a rich [209:22] library of built-in modules and you can [209:24] also install that third party modules [209:27] based on your need. So there are [209:28] different types of Python modules are [209:30] available. First one is built-in [209:32] modules. So they are pre-installed with [209:34] Python such as the math, random and OS [209:37] module. Other than that there are many [209:39] different modules are available. Second [209:41] is userdefined modules. So these modules [209:44] you can create yourself to organize your [209:47] code. And the third is third party [209:49] modules. So these modules created by [209:51] others which can be installed using pip [209:54] such as numpy, pandas and met.ly. These [209:57] are some of the examples. But other than [209:59] this there are so many different third [210:01] party modules are available. So in a [210:03] simple terms userdefined modules are [210:05] nothing but the py file generated by us [210:08] and we can access the data of these [210:11] files in other different programs also [210:13] based on requirement. How to access that [210:16] data we will understand in detail in our [210:18] further sessions. Let us just have a [210:20] discussion how to use these built-in [210:22] modules in our programs. For that let us [210:24] go to the VS code. Here we want to use [210:27] the math module available into the [210:30] system and for that to access any of the [210:33] inbuilt module we need to use the import [210:35] statement. First we will write the [210:37] import then after module name. So we [210:40] want to access the math module. So we [210:42] have mentioned import math. Now we want [210:44] to access the variable available pi [210:46] within the math module. So for that we [210:49] can access the pi variable using the dot [210:52] operator. So to calculate the area as [210:54] you can see we have mentioned math.py [210:57] so it will access the pi variable [210:59] available within math module and [211:01] accordingly the equation is provided and [211:04] the answer is printed based on the [211:05] required format. So using the first [211:08] statement import math you can see that [211:10] it will import the entire math module [211:13] and you can access any of the properties [211:15] available within the math module. Second [211:17] approach to import the module is we can [211:20] import a specific functions from the [211:22] math module. So suppose we want to [211:24] access variable pi and the square root [211:26] function from the math module. So we may [211:28] write from math import pi, square root [211:32] that is sqrt which is inbuilt syntax. [211:35] Same way you may also import multiple [211:38] variables or functions based on your [211:40] need and that all should be separated by [211:42] comma. So from this second statement, it [211:45] will only import the given variables and [211:48] the functions in the import statement. [211:50] The third option to import the math [211:52] module is you can import the math module [211:55] with the allies also. That means you can [211:57] import the math module with the [211:59] different name. So suppose I want to [212:01] import math module as name M then we may [212:05] write import math as M. So now we may [212:09] access all the elements of math module [212:11] by using the name m. So let us say I [212:14] want to find the square root of number [212:16] 36. So we may directly print the answer [212:19] print. Now to access the square root [212:21] function it is available within the math [212:23] class. We have imported the math as m. [212:26] So we may write m dot square root and [212:29] within parenthesis we will provide the [212:31] value that is 36. Now to print specific [212:35] format let us write square root is m dot [212:38] square roo<unk>36. So now we can access [212:41] all the properties of math module using [212:43] these m allies. Another possible [212:45] approach to import the built-in module [212:47] is we can import all functions from the [212:50] given module and for that we may write [212:52] from math import star. So this star [212:55] refers to all the functions available [212:58] within the math module. So these are [213:00] different approaches available to import [213:02] the built-in module based on our [213:04] requirement and you may use the same [213:06] approach to import userdefined modules [213:09] as well as third party modules. Let us [213:11] run the program to check the output. So [213:13] here you can see that based on the data [213:15] provided we are getting the different [213:17] output. So this means the syntax we have [213:20] provided over here is 100% valid and we [213:22] may use these different import [213:24] approaches to use the built-in modules. [213:26] There are so many different in-built [213:28] modules are available and we will [213:30] discuss about some of the important [213:32] built-in modules and their use in our [213:34] further session. Hello everyone and [213:36] welcome back to our Python for [213:37] mechanical engineering series. Today we [213:39] are diving into an exciting and [213:41] practical topic that is built-in Python [213:44] modules relevant to our mechanical [213:46] engineering. Python is renovant for its [213:48] extensive library of built-in modules [213:51] that can save us time and efforts by [213:53] providing pre-written readytouse [213:55] functions for various task. As a [213:57] mechanical engineer, you might find [213:59] yourself dealing with task like [214:00] numerical analysis, plotting data, [214:03] handling complex mathematical [214:04] calculations, and even working with the [214:06] data files. Instead of writing [214:08] everything from scratch, Python's [214:10] built-in modules empower us to focus on [214:12] solving engineering problems more [214:14] efficiently. In this session, we will [214:17] discuss the most useful built-in modules [214:19] for mechanical engineers, their [214:20] applications, and where these may be [214:23] useful based on our need. So, let's [214:25] explore the tools Python provide to [214:27] enhance our productivity and problem [214:29] solving capabilities in the world of [214:31] mechanical engineering. So, here the [214:34] list of built-in Python modules are [214:36] available that may be relevant to the [214:38] mechanical engineering. First is the [214:40] math module. So it provides access to [214:42] mathematical functions like [214:44] trigonometric calculations, logarithmic, [214:46] square roots and constants like pi and [214:49] many more. So this is useful while [214:52] calculating stress, strains or flow [214:54] rates using the different formulas. [214:56] Second is statistics and it provides [214:59] functions to perform statistical [215:01] operations like mean, median, variance [215:03] and standard deviation and it is useful [215:06] in analyzing experimental data or [215:08] quality control metrics. Third is random [215:11] module and it generates random numbers [215:14] for the simulation purpose. So it may be [215:16] useful while simulating material failure [215:18] probabilities or testing designs under [215:20] random conditions. Fourth is the time [215:23] module and it provides functions to work [215:25] with time related operations and it is [215:28] useful for tracking performance or [215:29] runtime analysis of different processes. [215:32] Next is OS module and it enables us to [215:36] interact with the operating system. So [215:38] it may be helpful for automatic file [215:40] handling for analysis of reports or [215:42] simulation of the files. Next is the CSV [215:45] module and it simplifies reading from [215:48] and writing to the CSV file. So it is [215:50] useful for processing experimental data [215:53] or importing data sets for the further [215:55] analysis. Next is JSON and it handles [215:58] JSON data for configuration and the [216:00] structured data and it is useful for [216:03] exchanging configuration settings for [216:05] the simulation purpose. The next is ITL [216:08] tools and it provides tools for working [216:10] with the iterators and generating [216:12] combinations or permutations and it is [216:14] useful while analyzing multiple design [216:17] scenarios or parameter combinations. [216:19] Next is collections and it provides [216:22] specialized container data types such as [216:24] DQ and counter and many more and it is [216:27] useful while optimizing inventory [216:29] management in manufacturing or counting [216:31] defect occurrences. We are just listing [216:34] here some of the important usage only [216:36] but there may be many different usage [216:39] you may find based on your need. The [216:42] next module is CIS module and it can [216:44] access system specific parameters and [216:47] the functions and it is useful for [216:49] handling command line arguments for [216:51] custom simulation inputs. The next [216:53] modules are the third party modules that [216:55] are technically external but often [216:58] considered essential for the programming [217:00] purpose. So the numpy module performs [217:02] advanced mathematical and array [217:04] operation and they are useful for [217:06] handling large numerical computations in [217:08] finite element analysis. The next is [217:11] metplot lib and it creates 2D plots and [217:14] visualization and it is useful for [217:16] visualizing stress distribution thermal [217:19] profiles or the dynamic responses. The [217:21] next one is scypi and it provides [217:24] advanced scientific and engineering [217:26] mathematical tools and it is useful for [217:28] solving differential equations, [217:30] optimization problems and for your [217:32] transformations also. And the last one [217:34] is the pandas library and it is useful [217:37] for handling tabular data for analysis [217:39] and manipulation and it is useful for [217:42] managing large data sets from [217:44] experiments or simulations. So these are [217:46] the different Python modules that may be [217:48] very much helpful for creating the [217:50] mechanical engineering programs. In the [217:52] upcoming sessions, we will explore some [217:54] of these modules in depth with practical [217:56] examples tailored for mechanical [217:58] engineers. We will demonstrate how they [218:00] can be applied to solve real world [218:02] engineering problems, automate task and [218:04] simplify complex computations. Remember [218:07] these modules are your tools. The better [218:10] you understand their capabilities, the [218:12] more efficiently you can work in your [218:14] field. So let's work with these and make [218:16] Python a powerful companion in your [218:18] mechanical engineering journey. Hello [218:20] everyone. In this session we will [218:22] discuss how to make use of userdefined [218:24] modules with the example. Let us go to [218:26] the VS code to understand it with the [218:29] example. Here you can see that we have [218:31] created one file mechanics.py [218:34] and it is having three data two [218:36] functions and one variable. First [218:38] function is def calculate stress which [218:40] will calculate the stress and return the [218:42] value based on the two input force and [218:45] area. Second is calculate torque. It is [218:47] having again two arguments and it will [218:49] return the final calculated torque to [218:51] the defined function. And the third is [218:54] density is provided that is material [218:56] density equal to 7850. [218:59] So here you can see that these generated [219:01] file mechanics. py is the userdefined [219:04] module and we may use this module in t [219:07] we may use this module in the another [219:09] file also based on our need but for that [219:12] the import statement is required as we [219:14] have discussed in our previous sessions. [219:16] So let us say we want to access this [219:18] mechanics module into this other file [219:21] that is main. Now to access this module [219:24] the foremost important statement is [219:26] import statement. If we want to import [219:29] all the properties of the mechanics [219:31] module, we will write import then module [219:34] name. And in our case, the module name [219:36] is mechanics. Then after we have [219:38] provided the two variables force and [219:40] area. Now to calculate the stress, we [219:42] can call the function available within [219:45] the calculate stress. We can call the [219:47] function available within mechanics [219:49] module that is the calculate stress. And [219:51] for that we will write mechanics dot [219:54] calculate stress. And we need to provide [219:56] the two argument and it will calculate [219:59] the stress based on the given data and [220:01] the answer is stored into the variable [220:03] named stress. And then after the answer [220:05] is printed based on the required format. [220:08] Second, as we have already seen we can [220:11] import the specific functionality of [220:13] these module also. So for that we will [220:16] write from mechanics import calculate [220:19] torque. So it will only import the [220:21] property calculate torque and remaining [220:23] will remain hidden. So after importing [220:25] the same we may calculate the torque by [220:27] providing the different data. The third [220:29] option is we can import the mechanics [220:32] module with the different allies also [220:34] and for that we may write import [220:36] mechanics as mac. So to access now [220:38] different properties we may use this [220:40] name mac. So suppose we want to access [220:43] the density. So density equal to mac dot [220:46] it is having the variable material [220:48] density. And the last one is we may [220:51] import all the properties of the [220:53] mechanics module. And for that we may [220:55] write from mechanics import start. So it [220:58] will import all the functions and [221:00] variables from this mechanics module. [221:02] And again we may use any of the [221:04] functionalities based on our [221:06] requirement. So I hope you got the idea. [221:08] The methodology will remain same. It may [221:10] be the built-in module, userdefined [221:12] module or the third party module. We [221:15] need to first import the required module [221:17] and then based on the import statement [221:19] we may access the different variables or [221:22] the properties of the imported module. [221:24] Let us run the program to just check the [221:26] output. So here you can see that we are [221:28] getting the output based on the required [221:31] input. So I hope you got the idea how we [221:33] may use the userdefined function and we [221:36] may import that function into the [221:38] different programs also. So we may reuse [221:40] the code based on our requirement. In [221:42] our next session we will discuss about [221:44] how to use JG GPT for function design [221:47] and the code modularity. So looking [221:49] forward to see you all in our further [221:51] session. Till then thank you. Hello [221:53] everyone and welcome to another session [221:55] where we will explore the exciting [221:57] applications of Python in programming. [221:59] So today we are going to focus on two [222:02] critical aspect of writing effective and [222:04] maintainable code that is function [222:06] design and code modularity. These are [222:08] essential concept not just for Python [222:10] but for programming in general [222:13] especially when working on a large scale [222:15] projects or solving complex problems. [222:17] Have you ever faced a situation where [222:19] your code became too long, difficult to [222:21] understand or hard to debug? Or maybe [222:24] you have struggled to reuse a piece of [222:26] code across multiple projects. Well, [222:28] this is where functions and modularity [222:31] come into play. Functions allow us to [222:33] break down large program into a smaller [222:36] reusable block of code making it easier [222:38] to read, debug and maintain. And about [222:40] this we have already discussed in detail [222:42] in our previous session. On the other [222:44] hand, code modularity help us to [222:46] organize our programs into a logical [222:49] independent parts making collaborations [222:51] and scaling up projects with much more [222:54] manageable way. And here is the best [222:56] part. The chat GPT can assist you in [222:58] understanding these concepts, designing [223:01] functions and creating a modular code [223:03] for any domain including mechanical [223:05] engineering, data science or even [223:07] automation. Whether you are looking to [223:09] improve your existing code, create [223:11] efficient algorithms or just explore [223:13] best practices, Jet GBT is here to guide [223:16] you when working with Python. Clear [223:18] prompts can help us uncover various [223:20] features of the language including [223:22] functions, variable scope, modular [223:24] programming and applications in specific [223:26] domain like mechanical engineering. [223:28] Today we will discuss well-crafted [223:30] prompts and example that not only [223:32] clarify the concepts but also connect [223:35] them to the real world scenarios. So [223:37] here are some of the important prompts [223:39] that you may use to understand some of [223:41] the important topics of the Python using [223:44] Jet GPT. You may use these prompts also [223:47] or you may create your own prompts also [223:49] based on your need. Now to understand [223:51] this prompt and its outcome. Let us go [223:53] to the chat GPT. Suppose we want to [223:56] understand how to create a function [223:58] without a return type. So for that uh we [224:00] may provide the prompt how to create a [224:03] function without a return type and as we [224:07] want to understand it with the language [224:09] Python. So we will provide using Python [224:13] and hit enter. [224:24] So here you can see that the chat GPT [224:26] has provided one simple outcome that [224:28] shows how to create a function without a [224:31] return type and one example is also [224:33] provided. It has also provided how to [224:36] call the same function. Next, suppose we [224:39] want to understand the variable scope [224:40] with the example. So we may provide the [224:42] prompt explain variable scope with [224:46] examples including all possible types. [225:07] Here you can see that Jet GPT is [225:10] providing different examples for the [225:12] different scope of variables and you may [225:14] explore all different scope using the [225:17] same example that has been given by the [225:19] state GPT. [225:26] So with the list of example it has also [225:28] provided some of the important key [225:29] points that define the difference [225:31] between the different scope of [225:33] variables. Suppose we want to understand [225:36] the scope of these variables in context [225:38] of mechanical engineering. So we may [225:40] also provide the prompt explain variable [225:43] scope in context of mechanical [225:46] engineering and its use. [226:04] So here you can see that now the JGPT is [226:07] providing the examples in context of the [226:09] mechanical engineering so that you may [226:11] relate it more in a clear manner. [226:43] With this, I hope that with Jet GPD's [226:46] assistance, you can master each of these [226:48] aspect by practicing with a specific and [226:50] actionable prompts. This way, you will [226:53] not only learn the theoretical concepts, [226:55] but also gain hands-on experience with [226:57] practical coding challenges. [227:00] Hello everyone and welcome to module [227:02] four where we'll be diving into [227:04] numerical computations with NumPy. [227:07] Numpai is one of the most widely used [227:09] libraries in Python and it is [227:11] fundamental tool for any engineer, data [227:13] scientist or researcher working with the [227:15] large data sets, mathematical [227:17] computations or scientific applications. [227:20] Whether you are solving engineering [227:21] problems, analyzing experimental data or [227:24] doing any kind of numerical computation, [227:26] NumPy is the go-to solution for handling [227:28] arrays and matrix efficiently. In this [227:31] module, we will understand the core [227:33] concepts of numpy including its array [227:35] structure and data types. We will also [227:37] learn how to perform element wise [227:39] operations and linear algebraic [227:41] operations using numpy function. We will [227:43] explore the random module in numpy to [227:46] generate random data for simulation and [227:48] testing. We will also perform matrix [227:50] manipulations and tackle problems in [227:52] mechanical engineering like stress train [227:54] analysis and fluid dynamics all with the [227:57] power of numpy. By the end of this [227:59] module, you will not only be able to [228:01] perform complex numerical operations but [228:03] also enhance the performance and [228:05] efficiency of your code with the help of [228:07] NumPy. So all right, let's get started [228:09] with the first steps of working with [228:11] NumPy. Before we jump into core [228:13] functionality, we need to ensure that [228:15] NumPy is installed. We may need to be [228:17] able to check its version and learn how [228:19] to use it in our programs. To begin, we [228:22] need to install NumPy on our system. If [228:25] you haven't installed it already, you [228:27] can use the Python package manager pip. [228:29] You can install numpy by running the [228:32] command in your terminal or command [228:34] prompt that is pip install numpy. To [228:37] understand this, let us open terminal or [228:39] command prompt. In my case, I am opening [228:41] the terminal. Now to install numpy, you [228:44] need to type pip install numpy and hit [228:47] enter. In my case, as you can see, I [228:50] have already installed numpy. So it is [228:52] showing that there is a new release of [228:54] keep available. In your case if numpy is [228:57] not installed then it will start [228:59] installing the numpy. But make sure you [229:01] are having the working internet [229:02] connection to install this numpy [229:04] library. To upgrade numpy you need to [229:06] type pip install d-upgrade [229:10] pip and as you can see it will start [229:12] updating the new numpy version available [229:15] in the system. After installation of the [229:17] numpy you can check the numpy version [229:20] also. For that you need to type python- [229:23] c then import numpy and print numpy-c [229:29] version_. [229:30] So this is the line of code you need to [229:32] write to check the numpy version also. [229:34] And after installing the numpy version, [229:36] we need to import it into our python [229:39] program. And the standard way of [229:40] importing numpy is as we need to type as [229:43] a first line import numpy or you can use [229:47] numpy other name also as an alias. For [229:50] that you need to type import numpy as [229:52] other name that you want. In my case I [229:55] have mentioned import numpy as np. So by [229:58] using np as an alias we can refer to [230:01] numpy functions in much shorter way [230:03] which keeps our code clean and readable. [230:06] One of the main features of numpy is its [230:08] array functionality. We can create [230:11] one-dimensional array using np dot array [230:14] function. So to use that function the [230:16] first statement needed is the import [230:17] statement. So we have mentioned import [230:19] numpy as np. To create a one-dimensional [230:22] array we need to store it into one [230:24] variable. Let us say the variable is [230:26] array equal to np dot array. Within [230:30] round bracket we will provide our 1D [230:32] array. So we need to indicate 1D array [230:34] using the list function using the square [230:37] brackets. So within square brackets in [230:39] the array argument we have mentioned 1 2 [230:41] 3 4 5. So here we are creating a 1D [230:45] array with the values 1 2 3 4 and 5. [230:48] Numpy arrays are much more efficient [230:50] than Python list especially when you are [230:52] dealing with the large data sets or need [230:54] to perform complex mathematical [230:56] operation. And finally to print that [230:58] array you can type print and within that [231:00] you can pass the variable in which the [231:02] array is stored. Let's combine [231:04] everything we have learned so far into [231:06] our simple program. We will install [231:08] numpy, import it, create 1D array and [231:11] perform basic operation. So let us go to [231:13] VS code to execute the program. So here [231:16] just for the clarity purpose one file is [231:19] created and the steps are mentioned that [231:21] we need to follow. The first statement [231:23] is we need to import numpy. So for that [231:26] we need to type import numpy as np. [231:30] Second to create a one-dimensional array [231:32] we need to store it into one variable [231:34] that is array equal to the function we [231:37] will use that is array which is [231:39] available within np. So we will write np [231:42] dot array and within its round bracket [231:45] we will provide our elements that are [231:47] required to create a array. So we will [231:49] type array equal to np dot array within [231:53] square bracket 10 20 30 40 and [231:55] [clears throat] 50. So using this line [231:57] we can create a 1D array with the values [232:00] 10 to 15. Now then we perform a simple [232:03] mathematical operation by multiplying [232:05] each element of the array by two and [232:08] this operation is applied to each [232:10] element element wise which is a key [232:12] feature of by arrays. So for that we [232:15] have mentioned result equal to array [232:18] multiplied by two. So the entire result [232:21] will be stored into the variable result [232:24] and the each element of array will be [232:26] multiplied by two. Finally we print both [232:29] the original and modified [232:31] [clears throat] arrays. So in first we [232:33] have printed the array and in second we [232:35] have printed the result. Let us run the [232:37] program to check if we are getting the [232:38] output or not. So here as the output you [232:41] can see that the original array is 10 to [232:43] 50 and the modified array is 2200. As we [232:46] have multiplied the array by two. So [232:49] each element of that array also gets [232:51] multiplied by two. Here [clears throat] [232:52] keep in mind that this import statement [232:54] is most important if we are removing [232:57] that import statement. Then we are not [232:59] able to access all the functionalities [233:01] of that numpy library. This is just a [233:04] basic introduction to working with numpy [233:06] arrays. As we move forward we will [233:08] explore more advanced features and [233:10] operations such as slicing, resetting [233:12] arrays and perform complex mathematical [233:15] operations. With this foundational [233:17] knowledge, you can now start integrating [233:19] numpy into your engineering problems. So [233:21] let's move ahead and learn how to [233:23] perform more complex numerical [233:25] operations with numpy using the concept [233:27] of arrays in our further session. Hello [233:30] and welcome everyone. In this session, [233:32] let's explore different ways to create [233:34] arrays in numpy. These methods are [233:36] incredibly useful when initializing [233:38] arrays for computation in engineering, [233:41] data analysis or simulation. There are [233:43] different functions are available to [233:45] create array in numpy and the first one [233:47] is zeros within which we need to pass [233:50] two arguments save data type required [233:54] for the zeros and again the data type [233:56] provided that may be the optional. So if [233:59] you needed you may provide the data type [234:01] here save defines that we will provide [234:03] the tpple defining the dimensions of the [234:06] array and d type will be the data type [234:08] and the default value for that will be [234:10] the float. Second is the arrange [234:12] function and it generates a 1D array of [234:15] evenly spaced values within a specified [234:18] range and it's similar to Python's range [234:20] function but it returns a nump by array. [234:23] So the syntax for the same is also [234:25] provided here as an argument. We will [234:27] provide the starting value. If we have [234:29] not provided then the default value will [234:31] be zero. Then we need to provide the [234:33] stopping value and it is excluded from [234:35] the calculation. Third, we need to [234:37] provide the step size and the default [234:39] value for that is one. And the last one [234:41] will be the data type of the elements. [234:44] The next one is the random module [234:46] available within the numpy and that [234:48] random module is having the function [234:50] rand which creates an array of random [234:53] numbers between 0 and 1 and the numbers [234:56] are driven from a uniform distribution. [234:58] So as you can see in the random function [235:00] we need to provide the multiple values [235:02] that is d1 comma d2 up to dn which [235:05] represents the dimensions of the array. [235:08] The last one is the empty function which [235:10] is again available within the numpy [235:12] module and it creates an uninitialized [235:15] array of specified s. The values in the [235:18] array are random and depend on the state [235:20] of the memory. So here again same refers [235:23] to the tpple defining the array and d [235:26] type refers to the data type of the [235:27] array elements and the default value for [235:29] that is plot. So let us go to the VS [235:32] code to access all these different [235:34] functions and create different required [235:36] arrays. Here one file is created the [235:39] import statement is mentioned that is [235:41] import numpy as np. Now the first step [235:44] is we will create a 3x4 array filled [235:46] with the zeros. So to create a 3x4 array [235:49] filled with the zeros we are having the [235:52] zeros function in the numpy and within [235:55] which we will provide the 3a 4 in the [235:58] tuple format. So here as you can see we [236:01] have stored one array into the np.zeros [236:04] and within its argument provided the 3a [236:07] 4. So 3a 4 in the round bracket refers [236:10] to the tuple and it will create 3x4 [236:13] array. Next one message is printed and [236:15] our objective is to print that final [236:18] array. So let us run the program. So [236:20] here you can see that the 3x4 array is [236:22] created and it is having the default [236:24] value float. So all the values are [236:26] mentioned in the format of 0. Second [236:29] suppose we want to create an array from [236:31] 0 to 20 with a step size of five. So for [236:34] that we may use the arrange function and [236:37] as you can see we will use the function [236:39] np do.range. We'll start from zero [236:42] ending point or the stopping point will [236:44] be 20 comma step size will be five and d [236:48] type if you want to assign we can assign [236:50] d type equal to any relatable data type [236:53] from the starting and stopping value. So [236:55] here it is mentioned d type equal to [236:58] flo. Next again whatever the array may [237:00] be created that will be printed using [237:02] the print function. Let us again run the [237:04] program. So here you can see that the [237:06] array with nparrange created and it is [237:09] having the step of five. So the value is [237:13] 0.5 point 10 and 15 point. One point you [237:17] can keep in mind is the ending value [237:19] that is 20 is excluded from the given [237:22] range. Third suppose we want to create a [237:24] 2x3 array with random values. So for [237:27] that you need to use the random module [237:29] available within the numpy library. So [237:32] again we are storing the answer into the [237:34] array random equal to np dot random [237:37] within random module random function is [237:40] available. So rand 2 3. So 2a 3 refers [237:44] to the tpple and it will represent the [237:47] 2x3 array. Again whatever random array [237:51] may be created it will be printed on the [237:53] screen using this print function. Let us [237:55] run the program. Here you can see that [237:57] the 2x3 array is created and it is [238:00] having the value in the range of 0 to 1. [238:03] Last suppose we want to create a 2x2 [238:05] empty array. So for that we need to use [238:07] the functionality empty and for that we [238:10] will store the answer array empty equal [238:13] to np empty. Again we will provide the [238:16] dimension in the format of tpple 2x2. [238:19] Then whatever empty array may be created [238:22] that will be printed on the screen. But [238:25] here keep in mind that this function [238:27] creates an uninitialized array of the [238:29] specified steps of the specified shape [238:32] and to represent that shape we may use [238:35] the res functionality also that we will [238:37] understand in our further session. The [238:40] values in this array are random and that [238:42] depends on the state of the memory. So [238:44] let us run the program and check it. So [238:46] here you can see that one empty array is [238:48] created with the value 0 5 10 and 15. [238:52] Here again take a note that the values [238:54] in these array are arbitrary and [238:56] uninitialized. So if we see these zeros [239:00] function can be used when you need an [239:02] array initialized with zeros. One can [239:04] use arrange for creating a range of [239:07] numbers in a specific pattern. This [239:09] random function is useful for generating [239:11] arrays filled with the random values. [239:13] And this empty function used for [239:14] creating arrays quickly when you don't [239:17] need any initialization. So these array [239:19] creation methods form the foundation for [239:21] working with numerical data in Python. [239:24] Start experiment with them to suit your [239:26] specific needs. I hope you find this [239:28] content very informative and you are [239:31] practicing very well to have the idea [239:33] about these different functionality and [239:35] how to utilize them based on require. So [239:37] looking forward to see you all in our [239:39] further session. Till then thank you. [239:41] Welcome back everyone. Today we are [239:43] diving into one of the most important [239:45] and powerful aspect of numpy that is [239:48] creation of n-dimensional arrays and it [239:50] is also known as ND arrays. These arrays [239:53] are the backbone of numerical [239:55] computations in Python particularly for [239:58] engineering scientific research and data [240:00] analysis. We will explore how to create [240:02] these arrays from scratch. How to [240:04] transform basic Python list into [240:06] multi-dimensional arrays and even how to [240:09] build complex arrays using specialized [240:12] numpy functions like zeros, arrange and [240:15] random. And these functions we have [240:17] already seen for the one-dimensional [240:19] array. By the end of this session, you [240:21] will have a clear understanding of how [240:23] to manipulate ND arrays for your [240:26] computational need. So let's get [240:27] started. Numpy allows the creation of [240:31] n-dimensional arrays where n can be 1, [240:34] two or even. These arrays are more [240:36] efficient than python list because they [240:39] store data in a contigious block of [240:41] memory allowing for the faster [240:43] computation. Now to understand that let [240:45] us go to the VS code. Suppose our first [240:47] objective is to create a one-dimensional [240:50] array. So to use the numpy functionality [240:52] the first important statement is the [240:54] import. We have mentioned import numpy [240:56] as np. Now to create a two-dimensional [240:59] array we will store that array into one [241:01] variable and then we will use the [241:04] functionality np array and within that [241:07] now using proper square brackets we need [241:09] to define the two dimensional array. So [241:11] as you can see we have defined one [241:14] variable array_2d using np dot array. [241:18] Now within outer square brackets two [241:21] again square brackets are provided [241:23] separated by comma. So here each square [241:26] bracket within outer square bracket [241:28] refers to the one-dimensional array. So [241:31] the value 1 2 3 refers to the one [241:33] dimension and the value 4 5 6 refers to [241:36] the second dimension. So let us run the [241:38] program to check the output how this 2D [241:41] array can be accessed. So here you can [241:43] see that the two-dimensional array is [241:45] created that is 1 2 3 4 5 6 and it is a [241:48] 2x3 array. Next to create a [241:51] three-dimensional array as you can see [241:53] within outer square bracket one inner [241:55] square bracket within outer square [241:57] bracket inner square brackets are [241:59] provided to refer to the two dimension. [242:01] Same way to refer to the three dimension [242:03] we need to provide outer bracket for the [242:05] one dimensional inner square bracket for [242:08] the two dimension and within that again [242:10] we will consider another square bracket [242:12] to refer to the three dimension. So to [242:15] understand that let us check this [242:16] content using np dot array within outer [242:20] square bracket inner square brackets are [242:22] provided that are highlighted with the [242:24] blue and within that again two parts are [242:27] separated with these yellow square [242:29] bracket and the values are 1 2 3 4. So [242:33] these inner square brackets refers to [242:34] the three dimension. Same way comma [242:37] separated again inner square brackets [242:40] are provided. So within blue square [242:42] bracket, yellow square brackets are [242:44] referred to the three-dimensional data. [242:46] While accessing the multiple dimensional [242:48] data, keep in mind you need to have the [242:50] clear idea about setting the square [242:53] brackets. Let us run the program to [242:54] check the output. So here you can see [242:56] that the three-dimensional array is [242:58] created with the values 1 2 3 4 5 6 7 8. [243:02] You can also create a n-dimensional [243:04] array directly from a nested list in a [243:07] Python. Each level of nesting [243:09] corresponds to a dimension of the array. [243:11] So suppose we want to create a list of [243:14] list for three-dimensional array. So let [243:16] us again check the content. We have [243:18] stored it into the list of list. Now [243:20] within outer square bracket two inner [243:23] square brackets are provided and within [243:25] pink square brackets these blue square [243:28] brackets are separated. So within three [243:31] square bracket we are having the value. [243:33] So it represents the three-dimensional [243:35] data. So this list of list is nothing [243:38] but the concept of three-dimensional [243:39] array only. And to convert that list of [243:42] list to a three-dimensional array again [243:44] we need to use the functionality np dot [243:47] array. So at the bottom as you can see [243:50] we have created three-dimensional array [243:52] by converting that list of list and for [243:54] that np dot array within argument we [243:58] have passed this variable list of list. [244:00] So it will create the array from this [244:02] list of list concept. Let us run the [244:04] program to check the output. So here you [244:06] can see that we are getting the [244:08] three-dimensional data by using the [244:10] concept of list of list. As we have [244:13] already discussed in our previous [244:14] session, the numpy provides built-in [244:16] functions to create the one-dimensional [244:19] array. So as well as it provides the [244:21] functionality for the multi-dimensional [244:23] arrays. Let us assume that we want to [244:25] create a 3x3x3 [244:27] array filled with the zeros. So for that [244:29] we can use the functionality zeros and [244:32] for that we can store the answer let us [244:34] say array_0 the function is np do. dot [244:38] zeros then within bracket we will [244:40] provide the dimensions in the tpple [244:42] format. So that is 3a 3a 3. So it [244:45] represents three 3 + 3 + 3 array and [244:48] then we have printed that array. Let us [244:51] run the program. So here you can see [244:53] that 3 + 3 array is created having the [244:56] default values zero and the default data [244:58] type being the float. Next suppose we [245:01] want to create a 3 + 4 + 2 matrix with [245:04] the numbers from 1 to 24 and for that [245:07] [clears throat] we may use the inbuilt [245:08] functionality a range and it generates [245:11] evenly spaced values and can be resaved [245:14] into n dimensional arrays also. So let [245:16] us again go to that code and as you can [245:19] see we have stored the answer into the [245:22] array arrange. The function is nparange [245:25] 1, 25. nparrange creates the [245:29] one-dimensional array from value 1 to 24 [245:32] and it will be the equally spaced data. [245:35] Now suppose we want to convert that [245:37] one-dimensional array and we want to [245:39] reshape it to the another array. Then we [245:42] need to use the inbuilt functionality [245:43] res. So again we have called different [245:45] function dot resave and within argument [245:48] we will provide the dimension of the [245:51] array. So the dimension required is 3a 4 [245:54] 2. So it will create a array having the [245:56] value 1 to 24 and that value will be [245:59] resaved into the required 3 + 4 + 2 [246:03] array. Let us run the program to check [246:05] the output. So here you can see that at [246:07] the bottom you will find the output. So [246:09] it is having the data 3 + 4 + 2 [246:14] elements. So this is our 3 + 4 + 2 [246:17] array. So using this reset function one [246:20] can easily change the shape of the [246:22] matrix based on the requirement. Now [246:24] last suppose we want to create a 2 + 3 + [246:27] 4 array with the random values. So for [246:29] that we need to use the inbuilt random [246:32] module available in numpy and we will [246:34] use the function rend. So it will create [246:36] an array with the random values between [246:39] zero and one. So let us check this code. [246:42] So here as you can see np.tr random dot [246:45] rand we need to provide the dimension of [246:47] the array in the tuple. So that is 2a 3 [246:51] 4. So this random function will [246:53] automatically create this dimensional [246:56] array having the random values. Let us [246:58] run the program to check the output. So [247:01] here you can see that 2 + 3 + 4 array is [247:07] created using these random values. So [247:10] using this concept I think you know that [247:12] numpai's ability to create and [247:14] manipulate and arrays is a gamecher for [247:17] scientific computing. You can create [247:19] arrays from Python list or use functions [247:22] like zeros, arrange and rand for better [247:25] efficiency and flexibility. [247:27] Understanding ND arrays is crucial for [247:29] solving complex engineering problems [247:31] such as simulating physical systems or [247:34] analyzing large data sets. So start [247:37] experimenting with these methods. You [247:39] will see how powerful and versatile [247:41] NumPy can be for your project. We will [247:44] dive deeper into the foundational aspect [247:46] of the numpy in our further session. [247:48] Welcome back everyone. Today we are [247:50] diving into a foundational aspect of [247:52] numpy that [clears throat] significantly [247:54] impacts how efficiently we perform [247:56] numerical computations. This topic is [247:58] data types in numpy. So numpy arrays are [248:01] highly optimized because they allow us [248:03] to specify the data type of the array [248:05] elements. Understanding and selecting [248:07] the right data type is crucial for [248:10] optimizing memory usage, improving [248:12] performance and ensuring accurate [248:14] calculation. In this session, we will [248:16] explore the different data types [248:17] available in numpy. how to explicitly [248:20] define them and how they differ from [248:23] standard Python data type. Numpy [248:25] supports a wide range of data types [248:26] which can be broadly classified as [248:29] mentioned over here. The first is [248:31] integer types and it represents the [248:33] whole numbers and common types are int [248:36] 8A 16A 32A 64. So these represents the 8 [248:41] 16 32 and 64 bits. Floating types [248:45] represents the decimal number and the [248:47] common types for them are float 16, [248:49] float 32, float 64 and float 128. And [248:53] here the precision var based on the [248:55] bits. The complex type is used to [248:58] represent the numbers with real and [249:00] imaginary part and it is having the type [249:03] complex 64 and complex 128. Boolean [249:06] represents the true or false and it is [249:09] having the type bull. The next is string [249:12] type and it is used to store the text [249:15] data and it is having the type string [249:17] str or uni code. Object type allows [249:20] storing the Python objects such as list [249:23] or dictionaries in the numpy array and [249:25] it is having the type object. In other [249:27] data types the unsigned integers [249:30] represents the non- negative whole [249:32] numbers and it is having the type u int [249:34] 8 and the u int 16. It is also used for [249:37] the date and time data also. Now to [249:40] understand about all the data types how [249:41] we can assign this data type to the [249:44] arrays. For that let us go to the VS [249:46] code. Here as you can see first we have [249:48] created an array as we want to check its [249:50] data type. So array 1 is created using [249:52] the array function. Next we will print [249:55] the array one and we want to print its [249:57] data type. So we will write variable [249:59] name dot dype. So dt type is the keyword [250:02] that we need to use. And here the [250:04] default output for the same will be the [250:06] int 64. As an example, some of the [250:09] arrays are provided with the different [250:11] types of elements. So as we can see the [250:14] first is integer array. Next is float [250:16] array. Third is complex array. And [250:18] within complex array you can see that we [250:20] will mention np dot array. Within round [250:22] bracket and within that square bracket [250:24] we will provide our imaginary number or [250:27] the complex number. So that number is [250:29] being 1 + 2 j 2 + 3 j and 3 + 4 j. Here [250:33] as you can see based on the provided [250:35] data the data type is defined by the [250:37] numpy system. We can also create the [250:40] numpy arrays with the defined data type [250:42] also. For that we need to make the minor [250:44] changes in the syntax. So as you can see [250:46] we are storing the answer in the array [250:48] one using the np array functionality and [250:50] it is having the three different [250:53] elements. Now the default value we know [250:54] that it is having the data type int 64. [250:58] So we can change this by providing the [251:00] comma then d type equal to required data [251:03] type. In such case this is the int 3. So [251:07] here based on provided data type the [251:10] variable is assigned the type. It may be [251:12] int 8, un 16, plot 32 and the complex [251:16] 64. As you can see here the provided [251:18] data types are different and we may [251:20] change the required data type also based [251:22] on the requirement using some of the [251:24] inbuilt function. Here as you can see we [251:27] have provided one integer array having [251:29] the array element and the default data [251:31] type will be integer. Now suppose we [251:33] want to change its data type to the [251:35] float. So for that we will store it into [251:37] the another array that is float array [251:40] equal to the array which we want to [251:42] convert that is integer array. Then the [251:45] function name is type. So dot as type [251:48] and within bracket we will provide our [251:49] required data type. In our case suppose [251:52] we require the float. After converting [251:54] it, let us print both integer as well as [251:57] float array to get the difference. So [251:59] from this program, I hope you got the [252:01] idea that what are the data types for [252:03] the array and how we can modify the data [252:06] type based on the required condition. [252:08] Let us run the program to check the [252:10] output. So first you can see that the [252:12] default data type is integer 64. Then [252:15] for the second again data type is the [252:17] int 64. For the next float to 64. Then [252:21] for the complex array it is complex 128. [252:24] At the bottom as we see other than [252:27] provided data type we can also convert [252:29] the data type of the array and for that [252:32] required syntax is already provided. Int [252:34] array is having the data type integer 64 [252:37] using this a type function. The data [252:40] type of the integer array is converted [252:42] to the float. So as we are printing both [252:44] the data you can see that the first one [252:46] is int 64 data type second one is the [252:49] float 64 data type. So as you can see [252:52] numpy supports a wide range of data [252:54] types for better control over memory and [252:56] performance. Next specifying data types [252:58] explicitly can optimize your code for [253:01] specific application. Dype attributes [253:03] help you to inspect or verify the data [253:06] type of any numpy array. Data types like [253:09] plot 64 or int 32 are ideal for [253:12] numerical computation while complex 64 [253:15] is helpful for handling complex numbers [253:17] in physics or engineering. So start [253:19] exploring the flexibility of numpy data [253:22] types in your projects and you will see [253:24] the significant impact it can have on [253:26] performance and accuracy. So looking [253:28] forward to see you all in our further [253:30] session. Till then thank you. Hello [253:32] everyone. Today we are going to discuss [253:34] numpy array attributes and it is an [253:36] essential concept when working with [253:38] numpy. These attributes provide valuable [253:41] information about the structure and [253:43] properties of a numpy array. It help us [253:45] understanding [clears throat] [253:46] how the array is stored, its dimensions, [253:49] size, shape, data type and more. By [253:52] mastering these attributes, you can gain [253:54] better control over your arrays and [253:56] ensure efficient data processing in [253:58] Python. We will start by exploring the [254:00] general attributes and then dive deeper [254:02] into these specific ones like endem [254:05] size, shape, d type and others. So let's [254:08] start with the numpy array attributes. [254:10] So numpy array come with various [254:12] built-in attributes that provide [254:14] metadata about the array and these [254:16] attributes include first one n that is [254:19] it provides the number of dimensions of [254:22] the array. The next is size and it [254:24] provides the total number of elements in [254:26] the array. Now the shape is a tpple [254:29] representing the dimensions of the [254:31] array. If we want to check the data type [254:33] of the elements in the array, we may use [254:35] the d type. Item size attribute provide [254:37] the size of each array element in bytes [254:40] and the data attributes provide the [254:42] memory address of the array data. So [254:44] let's take a quick look at these [254:46] attributes with the example. As you can [254:48] see in the example, we have imported the [254:50] numpy and one array is provided and it [254:53] is the two-dimensional array with the [254:55] elements 1 2 3 and 4 5 6. So as you can [254:58] see we have printed different attributes [255:01] of the array that is first we have [255:03] printed the array then dimensions of the [255:05] array using array dot the name of [255:08] attribute that is the end dim then size [255:11] of the array next save next data type [255:14] and same way item size and the data all [255:16] these attributes are used in this [255:19] example. So to check the output for the [255:21] same let us run the program and we will [255:23] first check output for the first [255:25] elements only. So you can say the array [255:27] is printed that is 1 2 3 4 5 6. Number [255:30] of dimensions is 2. The size means [255:33] number of elements are six. The shape of [255:35] the array is 2 + 3. So it is represented [255:39] in the format of tpple that is 2, 3. [255:41] Data type of the elements is int 64. The [255:45] size of the array is 8 bytes and the [255:47] memory address for the same is also [255:49] provided using this data attribute. So [255:51] using these attributes one can get the [255:53] details about the metadata of any of the [255:56] array. Next as you can see suppose we [255:59] want to again check use of nim attribute [256:02] for the different types of dimensions. [256:04] Here three arrays are created 1D 2D and [256:07] 3D and for all these arrays if one want [256:10] to check if these are exactly the [256:12] one-dimensional two-dimensional or [256:13] three-dimensional arrays we may use the [256:15] ndim attribute. So array_1D [256:18] end. Same way for all these arrays this [256:21] same attribute is used. Next suppose we [256:24] want to check the size again [256:26] independently the size attribute is used [256:28] in this example. Further to check the [256:31] shape one array is created that is the [256:33] three-dimensional array and the array [256:35] dots save attribute is used. In the next [256:38] example as you can see one array is [256:40] created with the array 1 2 3 and the [256:42] data type provided is the float 64. And [256:45] next we will check the data type of the [256:47] array using this dt type attribute. Next [256:50] again one array is created and for this [256:53] data type provided is int 32 and the [256:55] attribute used is the array dot item [256:58] size. And the last one again for the [257:00] created array the data attribute is used [257:02] to get its location. Let us again run [257:05] the program to check the output. So here [257:07] as you can see for this 1D 2D and 3D [257:10] array the dimensions provided are 1 2 [257:13] and 3. For the next example, the total [257:16] number of elements present are six. So [257:18] array dots size is giving answer to be [257:20] six. Next array shape is provided for [257:23] this given example. And here as you can [257:25] see it is the 2 + 2 + 2 array. So the [257:29] shape provided is 2a 2a 2 in the format [257:32] of double. For the next we have already [257:34] provided the data type to be float 64. [257:36] So once we are printing the value of d [257:38] type it will be by default float 64. For [257:41] the next array, we have provided the [257:42] three elements and the size of the array [257:45] is printed using this array dot item [257:47] size and the size of this array is four [257:50] bytes. And at last, using this data [257:52] attribute, one can print the address of [257:54] this array and it is printed on the [257:56] screen as you can see. So understanding [257:58] these numpy attributes gives you [258:00] complete control over your data and its [258:02] properties. from analyzing the arrays [258:04] dimension to inspecting the memory [258:06] layout. These attributes are [258:08] indispensable for efficient programming [258:10] in Python. So now it's time to put these [258:12] attributes into practice and use them to [258:14] analyze and manipulate data effectively [258:17] in your own project. Hello everyone. In [258:19] today's session, we are going to explore [258:21] NumPy array indexing and it is one of [258:23] the core concepts in NumPy that allows [258:26] you to access, manipulate and modify [258:28] elements in arrays. Indexing is [258:30] essential for performing any kind of [258:32] data manipulation be it for analysis or [258:35] for computation. We will begin by [258:37] understanding how to access and modify [258:39] air elements using indexing. We will [258:41] also cover negative indexing, 2D and 3D [258:44] indexing and how to access specific rows [258:47] or columns of multi-dimensional arrays. [258:50] By the end of this session, you will [258:52] have a solid foundation to handle the [258:53] array data. So indexing is used to [258:56] access specific elements of array and [258:59] index for each array starts from zero [259:01] for the first element 1 for the second [259:04] and so on. So as you can see suppose [259:06] here one array is provided having the [259:08] element 1 3 5 7 9. So the index element [259:12] of the first element will be zero second [259:14] element will be 1 and so on it will [259:17] increase by one. There is also a concept [259:19] of negative indexing. That means you may [259:22] refer to the last elements of the array [259:24] also. But for that the last element [259:26] position will be referred as the minus1. [259:28] Then further moving from right to left [259:31] it will increase negative indexing will [259:33] be minus2, minus3 and so on. So the [259:36] first element can be referred as the [259:38] zeroth element also or minus 5th element [259:41] also. So this concept is applicable to [259:44] 1D, 2D, 3D or any multi-dimensional [259:46] array. So to understand that let us go [259:49] to the VS code for the program. [259:51] One-dimensional array is provided having [259:53] elements 10 20 30 40 and 50. Next we [259:56] have accessed the element of array and [259:58] for that we need to write the variable [260:00] name in which array is stored. So that [260:03] is array and within square bracket we [260:05] will provide the index of the element [260:07] that we want to access. So we know that [260:10] the starting index element is zeroth [260:12] element and same way further we will get [260:14] the further elements. So as you can see [260:17] in the print statement it is printed [260:19] first element and array of zero mean [260:21] array within square bracket zero. So it [260:23] will represent the first element and [260:25] that will be 10. Next it is printed [260:27] array of two. So array of two refers to [260:30] the third element. So the 10 is zero [260:33] then first then 2 3 and four. These many [260:36] index positions are available. So array [260:39] of two will print the 30. Same way one [260:42] can use the concept of negative indexing [260:44] also. And for that as you can see it is [260:47] mentioned array of minus1 that means it [260:49] will represent the last element. So it [260:52] will be 15. Next again second last [260:54] element that is minus2 and it will be [260:56] considered as 40. Next you can see one [260:59] can modify the elements of the array [261:01] also by referring to that specific [261:03] element. So here it is mentioned array [261:06] of 1 equal to 100 and array of three [261:08] equal to 200. So in given array array of [261:12] one refers to the second element. So 0 1 [261:16] 2 3 and 4. So in place of 20 we will get [261:20] the new value being 100. Same way the [261:22] second and third element will be 40. So [261:25] the array of three represents to the [261:27] 200. So 40 will be replaced by 200. Next [261:31] modified array is printed and four [261:33] modified array. We can see that the [261:35] first element will be 10. Next element [261:38] will be 100 then 30 then again 200 then [261:42] 50. So this will be the modified array. [261:45] Next again the array is modified using [261:47] the negative indexing. So array of [261:49] minus1 equal to 40 and array of minus3 [261:52] equal to 20. So again if we consider the [261:55] modified array the negative indexing [261:57] start from right to left. So first will [261:59] be -1 -2 -3 -4 and minus 5. So the new [262:04] array will be 10a 20 [262:09] 40. [262:11] So this will be the new modified array [262:13] based on the assigned negative indexing. [262:16] Next two-dimensional array is created [262:18] and for two-dimensional array one need [262:20] to keep in mind that each blue square [262:22] bracket represents to the [262:24] one-dimensional array and it is [262:26] representing the number of rows [262:27] available in that array and within each [262:30] row present of each element represents [262:33] to the number of columns. So here these [262:36] first blue bracket refers to the zeroth [262:39] element of array. Then second is the [262:41] first element and next is the third [262:43] element. Now from zeroth element if you [262:46] want to access one then at zeroth [262:48] element one-dimensional array is [262:50] available and within this array we want [262:53] to access element one. So that means we [262:55] need to type array of first row element [262:58] that is 0 comma next column element that [263:01] is again 0. So 0 comma 0 refers to first [263:05] square bracket and its first element. So [263:08] as you can see here it is mentioned that [263:11] array of 1, 2. First is the zero then [263:14] one and then two. So the one refers to 4 [263:17] 5 6 within one we need to access the [263:20] element available at the two index [263:22] position. So that will be six. So it [263:25] will print the six. So as we can see [263:27] this first is referred as the 0 comma 0 [263:30] then 0a 1 then 0a 2. Next can be [263:34] referred as 1 comma 0 1 comma 1 and 1 [263:37] comma 2. And the last refers to 2 comma [263:41] 0 2a 1 and 2a 2. Next array with the [263:46] position 0 comma 0. So the first array [263:49] and its first element that will be 1. [263:51] Now suppose we want to access the [263:53] specific row only then we may write [263:55] array of zero but we will not provide [263:58] the second element. So it will consider [263:59] the row available at the zerooth [264:01] position. So row available at the zero [264:04] position will be 1 comma 2 3. Suppose 11 [264:08] 1 2 access specific column only. So for [264:11] that we need to write array of colon [264:14] symbol and comma 1. Colon symbol at the [264:17] first refers to the access of all rows [264:20] available within that array. But within [264:23] that row second column needs to be [264:25] printed. So it will print all the [264:27] elements available in the column [264:30] available at first index position. So [264:32] element available at the second columns [264:34] being 2, 5 and 8. So it will print 2, 5 [264:39] and 8. Next three-dimensional array is [264:42] created and within that if you want to [264:45] access the index position then one must [264:48] need to keep in mind that this outer [264:50] blue square bracket refers to the zeroth [264:52] element. Then second refers to the [264:54] element available at the first index [264:56] position. Within that there are two [264:59] index position. Zero refers to the 1A 2 [265:02] and 1 refers to the 3A 4. Within these [265:05] element available at the zeroth position [265:07] that is 1 comma 2. The element position [265:09] will be 0 and 1. So as you can see the [265:13] statement printed is array with index [265:15] position 0a 1, 0. So the zeroth index [265:19] position will be this blue square [265:21] bracket and within that there are two [265:24] square brackets. So this one is the [265:25] zeroth index position and second one is [265:27] the one index position. So within zeroth [265:31] position we need to access the one index [265:34] position that is 3a 4 and within these [265:36] we need to access again element [265:38] available at the zerooth position. So [265:40] there are two elements the position of [265:42] first is zero the position of second is [265:45] four. So it will print the three. So the [265:47] output for this case will be three. The [265:50] next array of 1 comma 0a 1. So first we [265:54] need to consider the index position [265:56] available at 1 for the outer bracket. [265:59] Within this we need to access the zeroth [266:01] element that is 5a 6 and within this the [266:05] first element needs to be accessed that [266:07] is six. So here we will get the output [266:10] six. Let us run the program to check the [266:12] output. So as you can see the first [266:14] element will be 10. Third element will [266:16] be 30. Last element will be 50. Second [266:19] last element will be 40. Next, based on [266:22] the modified values, the modified array [266:24] is 10, 100, 30, 240. [266:27] And in the second case, the element will [266:29] be 10, 100, 20, 240. So here for the [266:33] second element, we have already updated [266:35] the value from the first process. So the [266:37] updated value considered will be the [266:40] 100. And for the same element available [266:43] at the third index position the value [266:45] updated to be 200. So it will be [266:47] considered as the 200. The remaining [266:50] process will remain same. So the output [266:52] will be 10 20 240. Next the element [266:57] available at the given positions are 6 [267:00] and 1. Element at the first row are 1 2 [267:03] 3 and element available at the second [267:05] column that are the 2 5 8. Next, based [267:08] on the indexing position that we [267:10] understood, the element at the given [267:12] position will be three and six. So, by [267:15] mastering indexing, you can efficiently [267:17] access, modify, and manipulate array [267:19] data in both simple and [267:21] multi-dimensional arrays. Whether it's [267:23] slicing a 2D array or working with a [267:25] complex 3D arrays, indexing is a [267:28] powerful tool for all your data [267:30] manipulation tasks in NumPy. Keep [267:32] practicing to gain confidence in these [267:34] concepts and soon you will be able to [267:36] work seamlessly with any kind of data [267:38] structure in Python. Welcome back [267:40] everyone. Today we are diving into numpy [267:43] array slicing. It is a vital concept [267:45] that allow us to access and manipulate [267:48] specific portion of the array. Whether [267:50] you are working with one-dimensional or [267:52] multi-dimensional arrays, slicing gives [267:54] you the flexibility to extract and [267:56] modify data [clears throat] efficiently. [267:58] We will cover the syntax of slicing. We [268:00] will demonstrate how to slice 1D and 2D [268:03] arrays and explore the use of start, [268:05] stop and step parameters and even learn [268:08] how to reverse the arrays. So let's get [268:10] started and see how slicing can simplify [268:13] working with data in Python. So the [268:15] syntax of numpy array slicing is [268:17] whichever array we want to slice we will [268:20] use its variable name. So let us say the [268:22] variable name is array. Then after [268:24] within square bracket we may provide the [268:26] three values start colon stop colon [268:30] step. Start refers to the starting index [268:33] and it is inclusive during slicing. Stop [268:36] refers to the ending index and it is [268:38] exclusive from the slicing and step [268:41] refers to the interval between the [268:43] different indexes that we have selected. [268:45] And all these values are optional. Based [268:48] on the selected option we will get the [268:50] output. To understand the concept of [268:52] slicing and use of this syntax, let us [268:54] go to the VS code in the example. Here [268:57] you can see that we have provided the [268:58] list of data for the different number of [269:00] arrays and with the different type of [269:02] values. So first one array is provided. [269:05] This is the one-dimensional array. So [269:07] here we have sliced the data and you can [269:10] see that we have mentioned array within [269:12] square bracket 1 col 5. So it is [269:16] representing the start and stop value. [269:18] The starting value is the first index [269:21] element and the ending value is the [269:23] fifth index element. But keep a note [269:26] that the five is exclusive. So it will [269:29] slice the data from first index position [269:31] to the fourth index position. Now we [269:34] already know the starting index position [269:36] will be zero. So we need to start from [269:38] the one. So we will start from the 20. [269:41] So it will print 20 30 40. So the index [269:45] position will be 1 2 3. we will go up to [269:48] four. So again we will get the value 50. [269:51] So it will slice the array with the [269:53] values 20 30 40 and 50. In the next [269:56] option you can see that here only [269:59] mentioned array within square bracket [270:01] colon 4. So before colon it is [270:04] represented as the start and after colon [270:06] it is represented as the end. So it will [270:08] slice the array from beginning to the [270:11] index three. As four is exclusive we [270:14] will consider the index position to [270:16] three. So we will get the output again [270:19] starting from the beginning that is 10 [270:21] 20 30 and 40. So the index position will [270:25] be 0 1 2 and 3. In the next statement [270:29] you can see that the slicing considered [270:31] to be array of 2 colon empty. So it [270:34] means it will slice the array from index [270:37] 2 till the end. So [clears throat] if we [270:39] start slicing it will start from index [270:41] position two. So that will be 0 1 2. [270:45] That means it will start from 30 and it [270:47] will continue up to end that is 40, 50 [270:50] and 60. So this will be the sliced array [270:53] we will get from the given data. Now we [270:56] may also modify the array elements using [270:58] slicing. So for that as you can see [271:00] again one-dimensional array is provided. [271:03] And here the next statement mentioned is [271:06] array of 1 colon then after no value is [271:09] provided that means it will modify [271:11] elements starting from index one and the [271:14] value of each element that will be equal [271:16] to 100. So after this statement if we [271:19] modify the array so the modified array [271:22] will be zero index position will remain [271:24] five and for all the remaining index [271:26] position it will be value 100. Next, we [271:29] may also modify the array element using [271:31] stop parameter also. So here you can see [271:34] that we have provided the slicing [271:36] parameter to be array within square [271:38] bracket colon 3. So the starting index [271:41] element will be the zero and the ending [271:43] index element will be the three. So [271:45] [clears throat] we'll start from 0 1 2 [271:48] and 3 and the value updated for all [271:50] these element that will be equal to 50. [271:53] So if we print the modified array so it [271:55] will be starting from zeroth index [271:58] position 50 50 that is up to the index [272:02] position 0 1 and 2 and for the remaining [272:05] position the value will remain as it is [272:07] that is 20 and 25. Next we may also [272:10] provide the start and stop parameters. [272:12] So here you can see that array with the [272:14] index position 1 to 4 that is from index [272:17] position 1 up to three all the elements [272:20] will be modified with the values 200 300 [272:24] and 400. So if we print the modified [272:26] array we will get the output zero [272:28] element will be 10 then 200 300 and 400 [272:33] that will be index position 1 2 and [272:35] three and the last value will remain [272:37] same that will be 50. In these we may [272:39] also include the step parameter also. So [272:42] in the next example you can see that the [272:44] slicing data provided are array within [272:47] square bracket colon col 2 that means [272:50] starting and ending element will be the [272:52] zerooth element up to the last element [272:55] and the step considered to be the two [272:57] that means at the interval of two the [272:59] value of each element will be 100. So if [273:02] we print the modified array we will get [273:04] the array. The first element will be 100 [273:07] then two then again 100 then four then [273:10] again 100 then six then again 100 and [273:13] last eight. So it will modify every [273:16] second element. In this slicing we may [273:18] also use the concept of negative slicing [273:20] also. So here one-dimensional array is [273:22] provided and here it is mentioned array [273:25] within square bracket minus3 colon no [273:28] value that means up to the end and we [273:30] know that the negative indexing starts [273:32] from -1 -2 -3 so we will consider the [273:36] element from the minus3 and then after [273:39] from minus3 up to the last element all [273:42] the elements will be sliced and we will [273:43] get it printed. So if we print the final [273:46] answer it will be 40 50 and 60. Using [273:50] this slicing concept we may also reverse [273:52] the numpy array. And for that as you can [273:55] see we may write the syntax array. As we [273:58] want to reverse each element of this [274:00] array we will type within square bracket [274:03] colon that means starting to the ending [274:05] condition and the minus one that means [274:07] it will reverse each element of the [274:10] array. So if we print the modified [274:12] array, it will print 5 4 3 2 and 1. This [274:17] concept is also applicable for the [274:19] two-dimensional array also. Here in the [274:21] example, next two-dimensional array is [274:24] provided and it is the 3 + 3 array. So [274:28] here as you can see the syntax provided [274:30] is array within square bracket 0 2, 1 3. [274:35] So before comma element represents the [274:38] row and after comma element represents [274:40] the column. So 0 comma 2 refers to that [274:43] are available at the index position 0 [274:45] and 1 that will be 1 2 3 and 4 5 6 and [274:48] within these row we need to consider the [274:50] column 1 comma 3 that means it will [274:53] consider element available at the 1 and [274:56] two index position. So element available [274:59] at the 1 and two index position will be [275:01] 2 3 and 5 6. So if we print this [275:04] modified array we will get element to be [275:07] 2 3 and 5 6. Next as you can see we have [275:12] printed array cola 2. That means we will [275:15] consider all the elements of row that [275:18] are available at the second indexed [275:20] column. So here we can see that the [275:23] first index position will be zero then [275:25] one then two. So the element available [275:28] at the second index column that are the [275:30] three 6 and 9th. So it will print three [275:34] 6 and 9. At the last statement as you [275:37] can see again the slicing data provided [275:39] is array of 1 comma column. That means [275:43] we will consider all the columns [275:45] available in the first indexed row. So [275:48] the row available at the first index [275:50] position is 4 5 6. So we will get the [275:54] output 4 5 6. Now let us run the program [275:58] to compare the output. So we will run [276:00] the program and here you can see that we [276:03] are getting the output same way as we [276:06] have mentioned in the comment also you [276:08] may compare these output and understand [276:10] the logic that we have discussed to get [276:12] this output. So I hope you got the idea [276:15] how this slicing may be useful to [276:17] collect certain elements from the array [276:19] based on requirement from the specific [276:21] index position. So, this slicing is an [276:23] incredible powerful tool that allows you [276:25] to efficiently extract, modify, and [276:28] manipulate parts of an array. Whether [276:30] you are working with a simple [276:31] one-dimensional array or complex [276:33] multi-dimensional arrays, slicing gives [276:35] you a granular control over your data. [276:37] So, practice these concepts and you will [276:39] be well equipped to handle any slicing [276:41] challenges in Python. Looking forward to [276:44] see you all in our further session. Till [276:46] then, thank you. Welcome everyone. In [276:48] this session, we will focus on numpy [276:50] arithmetic array operation. Arithmetic [276:53] operations are at the core of numerical [276:55] computing, enabling you to perform [276:57] element wise calculations, scale arrays [276:59] or apply mathematical transformations [277:02] seamlessly. Numpy provides a [277:04] comprehensive set of arithmetic [277:05] operations making array based [277:07] computations efficient and [277:09] straightforward. So let's begin by [277:10] exploring the different arithmetic [277:12] operations available in numpy. Addition [277:15] operator adds the corresponding elements [277:17] of the array. Subtraction subtracts the [277:20] corresponding elements of the arrays. [277:22] Multiplication multiplies the [277:24] corresponding elements of the arrays. So [277:26] same way you can see that these are the [277:28] different basic arithmetic operators [277:30] available that we have already [277:32] understood in the case of simple data [277:34] types. These same operators are [277:36] applicable for the array operations [277:38] also. Let us consider one example to [277:40] understand the output of these different [277:42] operators. So go to the VS code here as [277:45] you can see that two arrays are provided [277:47] array 1 and array 2 and we will perform [277:50] different arithmetic operations on this [277:52] array. So first we are performing the [277:55] addition on the both array. So result [277:58] equal to array 1 + array 2. So it will [278:01] give us the result by adding each [278:03] individual element available at the same [278:05] index position. Means we will get 1 + 4 [278:08] will be 5, 2 + 5 will be 7 and 3 + 6 [278:12] will be 9. So here keep in mind that it [278:14] will perform the element wise operation. [278:17] It may be the addition, subtraction and [278:19] any operation. Next operation performed [278:21] is the subtraction. And here you can see [278:23] that result equal to array 2 minus array [278:26] 1. So 4 - 1 we will get 3. 5 - 2 3 and 6 [278:31] - 3 will be also three. Next operation [278:33] is multiplication. So if we perform the [278:36] multiplication element wise, we will get [278:38] the output 4 10 and 18. Next division [278:43] operation is performed. For that if we [278:45] execute we will get the output array 2 / [278:48] by array 1 that will be 4 / 1 it will be [278:52] 4.0 that is answer will be in the float. [278:55] Then 5ID by 2 will be 2.5 and 6ide by 3 [278:59] that will be 3.0. Next is the floor [279:02] division. So floor division will give us [279:04] the output of the quotient value with [279:06] the int data type. So here array 2 floor [279:09] divided with array 1. So we will get the [279:11] output 4 / 1 that will be 4. Then 5 / 2 [279:16] will be 2 and the 6 / 3 will be again 2. [279:20] So these are the quotient values for the [279:22] given division operator. Next is the [279:24] modulus operator and these operator will [279:27] give us the remainder. So if we perform [279:29] array two modulo array 1. So four divide [279:32] by 1 remainder will be zero. 5ID by two [279:35] remainder will be 1 and the 6ide by 3 [279:37] again the remainder will be zero. So [279:39] this will be our output. In the next [279:42] operation, exponentiation is performed. [279:44] So here array 1 multiply multiply two. [279:47] That means it will square each element [279:49] of array 1 based on the given data. So [279:52] our output will be 1 4 and 9. And the [279:56] last operation that is the result equal [279:58] to minus array 1. That means it will [280:01] change all the positive values of the [280:03] given array to be the negative value or [280:05] the vice versa. So we will get the [280:07] output -1 -2 - 3. Now to check the [280:12] output run the program. Now you can [280:14] compare the our discussed output with [280:16] the program generated output. And you [280:18] can see that we are getting the same [280:20] output as mentioned over here. In this [280:23] division case we are getting the output [280:25] 4 then 2.5 and here for the third case [280:29] if we divide 6ide by 3 then we will get [280:33] the value to be the 2.0. [280:36] So here it should be corrected that [280:38] means 2.0 and for remaining all cases we [280:41] are getting the same output. So with [280:43] numpy arithmetic operations you can [280:45] effortlessly perform computations on [280:47] arrays in a highly efficient element [280:50] wise manner. These operations are [280:52] foundational for numerical computing [280:54] task and are applicable across various [280:57] fields. It includes mechanical [280:59] engineering, data analysis and [281:01] scientific research. So practice these [281:03] examples to become proficient with [281:05] numpai's arithmetic capabilities. [281:07] Welcome back everyone. Today we are [281:09] diving into numpy array functions. This [281:12] plays a crucial role in making numpy [281:14] such a powerful tool for numerical [281:16] computing. Numpy offers a wide variety [281:19] of functions to work with arrays [281:21] allowing you to perform creation, [281:23] manipulation, mathematical operations [281:25] and the statistical analysis [281:27] efficiently. Understanding this commonly [281:29] used function will help you to unlock [281:31] the true potential of numpy in your [281:34] project. So let's get started. There are [281:36] different categories available for the [281:39] array functions. But we will overview [281:41] some of the most commonly used numpy [281:43] array function. The first one is the [281:44] array create functions. So these [281:46] function help you to create arrays in [281:48] various shapes, size and initial values. [281:51] The first one is nparray that converts a [281:54] python list or tuple into a numpy array. [281:57] Next np.zeros. It will create an array [281:59] filled with zeros. The next is np once. [282:03] So it will create an array filled with [282:04] ones. And the last is np. And it will [282:07] create an uninitialized array. To use [282:10] this function, let us go to VS code. [282:12] Here you can see that we have created [282:14] four arrays. Array 1 np. Within which we [282:17] have provided the list which we want to [282:20] convert it into the array. Second is [282:22] array 2, np.zeros. And within this the [282:25] dimension of array is provided in the [282:27] format of tuple. So 2a 3 refers to the 2 [282:31] + 3 array and it will be filled with [282:33] zeros. Array 3 equal to np do.1 3a 2. It [282:37] means it will create a 3 +2 array with [282:40] all the values being 1. And array 4 [282:43] equal to np 22 refers to the 2 +2 array [282:47] and it will create an uninitialized [282:50] array. We have printed all these array. [282:52] So let us run the program to check the [282:54] output. So here you can see that the [282:56] first array is created 1 2 3. The second [282:58] 2 + 3 array is created with all the [283:00] default values being 0.0 that is in the [283:03] float data type. Next once array is [283:06] created and it is having the dimension 3 [283:08] +2 and the last empty array is created [283:10] and it is the uninitialized array. Next [283:13] we will understand about the array [283:15] mathematical functions. These functions [283:17] perform element wise mathematical [283:19] operations. Some of the widely used [283:21] mathematical functions are noted over [283:23] here. The functions such as add, mean, [283:26] max, square root and power are useful [283:29] for mechanical engineering perspective [283:31] also. We will understand the use of [283:33] these functions using one example. So [283:35] let us go to VS code. Here two arrays [283:38] are provided array 1 array 2. Now first [283:41] we are trying to find the minimum [283:42] element from array 1. So we will use np [283:45] dot mean and within bracket we have [283:48] provided the required array that is [283:49] array 1. So this function will give us [283:52] the minimum element present from this [283:55] array one and that will be one. Next [283:58] function is max and within its argument [284:00] array one is provided. So it will find [284:02] the maximum element from given array and [284:04] that will be nine. Next is add function [284:07] and within its bracket two arrays are [284:09] passed that is array 1, array 2. So it [284:12] will add each element of this array and [284:14] it will be stored into the variable [284:17] result add. So the output of these [284:21] result underscore add will be element [284:23] wise addition that is 2 6 and 12. The [284:27] next function is square root. So it will [284:29] find the square root of each element of [284:31] the array 1 and we will get the output 1 [284:34] 2 and 3. Next function is the power and [284:38] here you can see that the first we have [284:40] provided the array and then after we [284:42] have provided the power for the each [284:44] element. So this will find the power of [284:47] each element present in the array two. [284:49] So it will find the square of each [284:51] element. So output will be 1 4 and 9. [284:55] Now the output for each variable is [284:58] printed using this statement. So let us [285:00] run the program to check the output. So [285:02] here you can see that the minimum [285:04] element is 1, maximum element is 9, [285:06] addition is 2, 6 and 12 and in square [285:09] root as you can see whenever we are [285:12] finding the square root the output will [285:13] be default float data type. So we will [285:16] get the answer to be 1.0 2.0 and 3.0. [285:20] Next the power value will be again 1 4 [285:22] and 9. So I hope you got the idea how to [285:25] make use of these mathematical [285:26] functions. Next we will discuss about [285:28] some of the array statistical functions. [285:31] So these functions perform statistical [285:33] analysis on array elements. So some of [285:36] the most widely used statistical [285:37] functions are mean, median, standard [285:40] deviation and variance. To understand [285:43] the use of this function again let us [285:45] consider one example. So go to VS code. [285:47] Here we have provided one array having [285:50] the element 125. Now we are finding all [285:52] necessary elements that is median. So we [285:55] will type np dot median and within its [285:58] argument we will pass the array for [286:00] which we want to find its value. Same [286:02] way we may use the functions mean, [286:05] standard deviation and variance and the [286:07] answer for all these variables are [286:09] printed using this print function. So [286:12] let us run the program to check the [286:14] output. So here you can see that the [286:16] value of median is 3.0 that is the [286:18] middle element present in the given [286:21] array when it is shorted in the [286:23] ascending order. Next is the value of [286:25] mean that is the average. So it is the [286:27] sum of total numbers divide by count of [286:30] total numbers. The next is standard [286:33] deviation and the last one is variance. [286:36] So if needed in any mathematical [286:38] computation we may find the standard [286:40] deviation and the variance value also [286:43] directly by using this inbuilt function. [286:45] So in this session we explore the most [286:47] commonly used numpy functions and [286:49] categorized by their purpose. So whether [286:51] you are creating arrays, performing [286:53] mathematical or statistical [286:55] calculations, NumPy has the tools that [286:57] you need. These functions are the [286:59] building blocks for solving complex [287:01] numerical problems efficiently. So [287:03] practice these examples to master their [287:05] use and unlock the power of numpy for [287:07] your projects. Hello everyone and [287:09] welcome back. Today we will explore a [287:11] unique feature of numpy that goes beyond [287:13] numerical operations that is the string [287:16] functions. While NumPy is primarily [287:18] known for handling arrays and numerical [287:20] computations, it also provides powerful [287:23] tools for string manipulation. These [287:25] functions are useful for processing text [287:27] data in arrays making it easy to perform [287:30] task like concatenation, case [287:32] conversion, repeatation and more all at [287:35] array level. Let's take closer look at [287:37] some common string functions provided by [287:39] the numpy and their applications. So [287:42] here is the list of commonly used string [287:44] functions in numpy and we will [287:46] understand the use of all these function [287:48] using one example. For that go to VS [287:50] code. Here one standard program is [287:53] provided for the understanding purpose. [287:55] Two arrays are provided array one and [287:57] array two and they are having the string [288:00] data type elements. Now first is the add [288:03] function and it concatenates two strings [288:05] element wise. So here you can see that [288:08] we have stored it into the variable [288:09] concatenated equal to to use this [288:12] functionality of string function. We may [288:14] type np dot care is compulsory then dot [288:19] the function name add and within its [288:21] argument we will pass the number of [288:23] arrays that we want to concatenate. So [288:25] here we have passed array 1, array 2. [288:28] Then we have printed the concatenated [288:30] array. So here we will get the output. [288:32] First hello world that will be [288:34] concatenated then numpy functions and in [288:37] third we will get the output python [288:39] python. So one array is created with the [288:41] name concatenated and it is having the [288:43] concatenated strings. Second function is [288:45] the multiply function and it repeats the [288:48] string for a specified number of times. [288:50] So we have stored it into the variable [288:52] repeated equal to np.care dot multiply [288:56] within its argument. First we will pass [288:58] the array comma separated. We will pass [289:01] the number which represents the number [289:04] of times we want to repeat the string. [289:07] So here the number is two that means [289:09] when a new array will be created it will [289:11] be stored into the repeated variable and [289:13] it is having the elements hello numpy [289:17] numpy and python python as here two is [289:20] mentioned which indicates the repetation [289:22] of each element two times. Third is [289:25] capitalize function and it capitalize [289:28] the first letter of each string in the [289:30] given array. Now to use this again same [289:33] methodology we will adapt np do. care [289:35] dot capitalize within its argument we [289:37] will pass the array. So it will [289:39] capitalize first letter of each element [289:42] of the given array. So here array one is [289:44] passed. So for that it is having first [289:47] element in lower case. So it will keep h [289:50] capital n capital and p capital. The [289:53] fourth one is the lower function and it [289:55] converts all uppercase characters in a [289:57] string to the lower case. To use this we [290:00] will only type np do.care dot lower and [290:03] within its argument we will pass the [290:05] array two. So here array 2 is passed. So [290:08] it will consider all the elements of [290:10] array 2 and any uppercase character [290:13] present in the given string will be [290:14] converted to the lower case. The fifth [290:16] one is the upper function and it [290:18] converts all lowerase characters in a [290:21] string to the upper case. So we may use [290:23] the function in the same way as the [290:25] lower. We will type np.care dot upper [290:28] and within its argument here we have [290:30] passed array one. So now this function [290:32] will convert each element string and its [290:35] character to the uppercase. Sixth [290:37] function is the join and it joins the [290:39] elements of a sequence into a single [290:41] string by using the specified character. [290:44] So here we will type np.care dot join [290:47] within its argument. First we will pass [290:49] the character with which we want to join [290:52] each character of the string. Then comma [290:55] separated we will provide the array [290:56] name. And here we have provided the [290:58] array one. So in this case it will [291:01] create a new array. And in this array [291:03] each element of the array and within [291:06] that each character of the string gets [291:08] connected by this dash symbol. So as an [291:11] output we will get hash e- l- [291:16] o as of first. Then in second we will [291:18] get n - u - m d-p. [291:23] That means it will combine each [291:25] character of the string by this provided [291:28] character. Now the last function that is [291:30] the equal function and it checks the [291:32] equality of the strings from the given [291:35] arrays. So here we have mentioned [291:37] np.care doe equal within bracket we want [291:40] to compare the elements of array 1, [291:42] array 2. So we have passed array 1, [291:44] array 2. So it will compare each element [291:48] of the given arrays at the same index [291:50] position and if these two matches then [291:52] it will return the answer true and if [291:55] does not matches then it will return the [291:57] answer false. So in our case first two [291:59] elements are not matching so we will get [292:01] the answer false. For the second also we [292:03] will get the answer false but in the [292:05] third case both elements are matching so [292:08] we will get the answer true. Now to [292:10] check the output let us run the program. [292:12] So here you can see that first strings [292:14] are concatenated of the array 1 and [292:17] array 2. So we are getting the output [292:18] hello world numpy functions and python [292:21] python. As a second case in the multiply [292:23] function the repeatation provided is for [292:26] two times. So we are getting the output [292:28] hello numpy numpy and python python. In [292:31] the third case the first letter gets [292:33] capitalized due to this function. Due to [292:35] use of lower function the each string [292:38] elements are converted to the lower case [292:41] and due to use of the uppercase function [292:43] each element of the string is converted [292:45] to the upper case. In the next join [292:48] function as you can see each character [292:50] of the strings are connected by the [292:52] provided dash and in the last case in [292:54] the equality check using the equal [292:56] function each we have compared the [292:58] elements and we are getting the output [293:00] false and true. So I hope you got the [293:02] idea how to make use of the string [293:04] functions to get the idea about the [293:07] string to perform the manipulations [293:09] operations on the string elements [293:11] available in the array. So these numpy [293:13] string functions are extremely versatile [293:15] and allow you to manipulate text data [293:17] directly within arrays. Whether it's [293:19] concatenating strings, changing case or [293:22] repeating patterns, these functions help [293:24] streamline text processing task in [293:26] Python. So you can imagine this being [293:28] particularly useful in scenarios like [293:30] formatting or cleaning data in machine [293:33] learning pipelines, processing text in [293:35] CSV or database records for preparing [293:38] labels or annotations for visualization. [293:40] So that's all for today's discussion on [293:43] numpy string functions. Try [293:44] experimenting with these functions in [293:46] your own project to see how they [293:48] simplify text manipulation task. Hello [293:50] everyone, welcome back. In this session, [293:53] we will understand use of some of the [293:55] important trigonometric function. Numpai [293:58] provides a comprehensive set of [293:59] trigonometric functions that help [294:01] compute the trigonometric ratios of [294:03] angles such as s, cosine, tangent and [294:06] their inverse. These functions are [294:08] especially useful in engineering and [294:11] scientific computations where angles and [294:13] rotations are involved. Commonly used [294:15] trigonometric functions in numpy are sin [294:19] cosine tan inverse s inverse cosine [294:22] inverse tangent and conversion to [294:24] degrees and radians. Let us consider one [294:27] example to use this function. For that [294:29] go to vs code. Here for your reference [294:32] some data is provided and the use of [294:34] these functions are also provided. But [294:36] for this import of numpy module is [294:39] important. So first we have imported the [294:41] module import numpy as np. So we may use [294:44] the numpy as alias of the np. Here angle [294:47] is defined as the 45. Now we can convert [294:50] this angle to radians also. For that we [294:52] may type angle radian equal to np dot [294:57] radians and within which we will pass [294:59] our degree angle. So it will convert our [295:02] degree angle into the radians value. [295:04] Next we may calculate the s cosine and [295:08] tangent value also. For that we may use [295:10] np dot sign and within which we will [295:13] provide the angle. And here the angle [295:15] provided is in radian. Same way we may [295:18] use the function np do. cos and np.10 [295:21] within its argument we may need to pass [295:23] the value of angle. Now the stored value [295:26] s cosine and tangent are printed using [295:28] this print function. We may also use the [295:31] inverse trigonometric functions to find [295:33] its value. So here you can see that we [295:36] have stored its values into some of the [295:38] variables. So suppose we want to find [295:40] the sign inverse for that np sign and [295:44] within its argument we will pass the [295:46] sign value and these values we have [295:49] already calculated using these above [295:51] functions. Same way we may use the arc [295:54] co and arc 10 function also which will [295:56] provide the inverse trigonometric [295:58] values. All the calculated values stored [296:01] in this variable and these values are [296:03] printed using this print functions. [296:05] Next, if we are having the angles in [296:07] radians, then we may convert these [296:09] angles into the degrees also and for [296:11] that we may use the function np do. And [296:14] within its argument, we will pass the [296:16] radian angle and same way vice versa is [296:19] also possible. So these degrees and [296:21] radians functions are useful to convert [296:23] the angle into the radians over degrees. [296:26] Now use of these functions are also [296:29] shown over here and some of the data is [296:31] printed using this print function. Now [296:33] there is also possibility that we may [296:35] convert the range of angles from degree [296:37] to radian using these functions also. So [296:40] as you can see in this example we are [296:42] having the angles in degree in the [296:44] format of array that are np dot array we [296:47] are having the angles 0 30 45 60 and 90. [296:51] We can convert these angle into the [296:53] radians using these radians function. So [296:56] np.t radians and these array of degree [296:59] angle is provided as an argument and [297:01] same way using this array concept we can [297:04] pass these angles radian variable that [297:07] is having the array of radians into the [297:09] s cosine and tangent also to calculate [297:12] element wise values from this array. So [297:15] these functions are useful for [297:16] calculating the single values as well as [297:18] the element wise values from the arrays [297:21] also. Let us run the program to check [297:22] the output. So here you can see that the [297:25] angle is 45° and using these radian [297:28] functions we have converted into the [297:30] radian. Same way the value of sin cosine [297:32] and tangent 45° is obtained using the [297:35] sin cos 10 value. But here keep a note [297:38] that within this function we will pass [297:40] the radian angle. Same way we may find [297:42] the inverse trigonometric data also. So [297:45] based on the provided radian value we [297:47] may find the values of angles also. And [297:49] this may be possible by using this ark [297:51] sign, arc co and arc tangent function. [297:54] Next we have converted the degree to [297:56] radian and radian to degree using this [297:58] degree and radian function. And you can [298:00] see the output. And same way at the [298:02] bottom we have passed the array of [298:05] degree into the radian. So we have [298:06] converted the value into the radians. [298:09] And same way we may find the values of [298:11] s, cosine and tangent also from that [298:13] each element of array. So I hope that [298:16] you got the idea how we may use these [298:18] trigonometric functions to calculate the [298:20] data based on the provided angles. It [298:22] may be a radians or it may be in the [298:24] degrees. You may use these trigonometric [298:26] functions while performing some of the [298:28] mathematical operations or the matrix [298:30] operation in which the rotations or the [298:33] angular movements are involved. Keep [298:35] practicing these functions so you may [298:37] get better idea how to utilize and where [298:40] to utilize these functions. Looking [298:42] forward to see you all in our next [298:43] session. Till then thank you. Hello [298:45] everyone. Today we are diving into numpy [298:48] matrix operation and it is an essential [298:50] part of numerical computations for [298:52] handling multi-dimensional data. We will [298:55] explore a range of matrix operations [298:57] including multiplication, transpose, [299:00] inverse, determinant and reaping. These [299:02] operations are crucial in engineering, [299:04] data science and scientific research. So [299:07] now let's understand these operations [299:08] step by step with the examples. First is [299:11] the dot function and it is useful for [299:13] performing the matrix multiplication. [299:16] This calculate the dot products between [299:18] the two matrix. The second function is [299:20] the transpose function and it will [299:22] convert the rows into columns and vice [299:25] versa. So this function flips a matrix [299:27] over its diagonal converting rows into [299:29] columns and columns into rows. The next [299:31] is inverse function and it computes the [299:34] inverse of a square matrix and this [299:36] inverse function is from the line. The [299:39] fourth function is the determinant. So [299:41] it calculates the determinant of a [299:42] square matrix and again it is available [299:44] from the clean. The next function is the [299:46] flatten and this converts the n [299:48] dimensional matrix to the [299:50] one-dimensional array. And the last [299:52] function is the reshape which changes [299:54] the shape of array without altering its [299:56] data. So we may change the type of array [299:59] by using this resave function. So let us [300:01] understand all these functions using one [300:04] program. So go to v score. Here you can [300:06] see that first we have imported the [300:08] numpy and as we want to use the function [300:10] inverse and determinant which is [300:12] available in the line and this line is [300:15] available within numpy. So we have [300:17] mentioned from numpy. Lineal import [300:20] inverse comma determinant that is inv. [300:24] We have provided the 2x2 matrix. It is [300:26] having the elements 7 5 and 2 3. First [300:30] we have printed the original matrix. Now [300:32] suppose we want to find the sum of [300:33] diagonal elements of this matrix. So for [300:36] that the function available is trace. So [300:39] we will type np trace within which we [300:41] will provide our matrix. [clears throat] [300:43] So it will find the sum of diagonal [300:45] elements of the matrix. We can find the [300:47] transpose of a matrix. For that we may [300:50] type np.transpose [300:52] and within its argument we will pass our [300:54] matrix. To find the inverse of a matrix [300:56] we will use the function inv. and within [300:59] its argument we will pass the matrix. We [301:01] may also calculate the determinant using [301:04] the same function that is det and within [301:06] its argument we will pass our required [301:08] matrix. Now any n dimensional matrix can [301:11] be converted to the one-dimensional [301:13] matrix using this flatten function and [301:15] to use this we first need to type the [301:18] array name that is matrix and dot we [301:21] will use the function that is flatten [301:23] and we will pass no arguments. Now this [301:26] flattened one-dimensional array can be [301:28] converted to the any required shape by [301:30] using the resave function. So you can [301:32] see that we have mentioned flatten dot [301:34] resave and we want to convert these [301:36] one-dimensional array to 1x4 matrix. So [301:39] we have mentioned value to be 1, four. [301:42] It represents one row and four columns. [301:45] We may find the multiplication of these [301:48] two matrices also. And for that the [301:51] function available is dot function. So [301:53] here you can see that the first matrix [301:55] is having the values 7 5 2 3. Second [301:58] matrix is defined over here having the [302:00] values 1 2 3 4. So suppose we want to [302:03] find the dotproduct between these two [302:06] matrix we will type np dot dot and [302:09] within its argument we will pass our [302:11] matrix that is matrix matrix_b. [302:14] So it will find the multiplication [302:16] between these two matrices. Let us run [302:18] the program to check the output. So here [302:20] our original matrix is 75 23. Its [302:24] diagonal elements are 7 and 3. So the [302:26] trace of matrix obtained that is the 10. [302:29] Next we have found the transpose of [302:31] matrix. And here you can see that the [302:33] elements of rows became the elements of [302:36] column. So 75 and 2 3. Next we have also [302:39] obtained the inverse of matrix. We very [302:41] well know that finding the inverse of [302:43] matrix is a timeconuming process. But [302:45] using this function you may directly [302:47] find the inverse of matrix. Next [302:49] determinant of matrix is also obtained [302:51] using the d function. We are having this [302:54] two-dimensional matrix 7523 and using [302:57] flattened function it is converted to [302:59] the one-dimensional array that is 75 23. [303:02] Next these one-dimensional array is [303:05] converted to the two-dimensional array [303:07] having the one row and four columns. So [303:09] you can see that here two square [303:11] brackets are provided and it is having [303:13] the one row having elements 7 52 3. And [303:16] finally we have also printed the [303:18] dotproduct of these two matrices. And [303:21] again we know that this process is [303:23] timeconuming to find the multiplication [303:25] of two matrix. But using this dot [303:27] function we may easily find the [303:29] multiplication between the matrices. So [303:31] I hope you got the idea how we may [303:33] perform these matrix operations by using [303:35] some of the inbuilt functions available [303:37] within numpy library. These operations [303:40] enable you to perform essential matrix [303:42] computation effortlessly with this [303:44] numpy. So practice combining these [303:46] operations in program to gain the [303:48] proficiency. Hello everyone, welcome to [303:51] today's session. In this session, we [303:53] will be applying Python's library to [303:56] solve engineering problems numerically. [303:58] In mechanical systems, analytical [304:00] solutions are often impractical for [304:02] complex geometries or dynamic [304:04] conditions. This is where numerical [304:06] methods tap in and numpy serves as our [304:08] toolkit. From solving systems of linear [304:11] equations in a structural analysis to [304:13] performing numerical integration for [304:15] thermodynamic cycles, Numpai's array [304:17] manipulation and mathematical functions [304:19] make it both powerful and efficient. By [304:22] the end of this session, you will [304:23] understand how to translate engineering [304:25] problems into a code and achieve precise [304:27] reliable solution. So let us start with [304:30] the first example. The statement given [304:32] is a mechanical component experiences a [304:34] stress in the three perpendicular [304:36] directions and the stress tensor is [304:39] provided. We need to calculate the trace [304:41] of the stress tensor. Second, we need to [304:44] find the determinant and inverse of the [304:46] stress matrix and third we need to find [304:48] the transpose of the matrix. Now to [304:51] solve this problem, first we need to [304:53] define the stress tensor. To define this [304:55] we need to create a 3 +3 array and for [304:58] that we may use the np dot array [305:00] function. So here we have defined the [305:02] stress tensor using the np dot array [305:05] function. Now to perform the first task [305:08] we need to define the variable in which [305:10] we will find the value of trace and to [305:13] find the value of trace there is inbuilt [305:15] function available in the numpy that is [305:17] the trace function. So just we need to [305:20] store that answer into one variable. So [305:23] trace value equal to np dot trace and [305:26] within its argument we will pass our [305:28] array to find the determinant and [305:30] inverse we need to specifically use the [305:33] lin elk part of the numpy. So here [305:36] determinant is stored as np. Linel dot [305:40] date that is the de which represents the [305:43] determinant within its argument we have [305:44] passed the required array. To find the [305:47] inverse of the matrix again np.el green [305:50] dot function available is inverse that [305:52] is I and V and within its argument we [305:54] will pass again our required matrix. So [305:57] answer of both get stored into the [305:58] determinant and the inverse matrix [306:00] variable. Now to find the transpose [306:02] again within numpy library transpose [306:05] function is available. We need to store [306:07] its answer into the variable. So [306:09] transpose matrix equal to np.transpose [306:13] and within its argument we have provided [306:15] our required array. So using all these [306:17] inbuilt functions we can easily perform [306:20] the required mathematical computation [306:22] based on our given problem. Finally we [306:24] are displaying all the results that we [306:26] have calculated from the above steps. So [306:28] we are printing stress tensor stress [306:31] determinant inverse of the stress tensor [306:33] and the transpose of the stress tensor. [306:35] Let us run the program to check the [306:37] output. So here you can see that first [306:39] stress tensor is provided. Now second [306:42] trace is calculated that is nothing but [306:44] the summation of the diagonal elements [306:46] that will be 470. Next determinant of [306:49] the stress tensor that is again [306:50] calculated using the inbuilt function. [306:53] Next we have obtained the inverse of the [306:55] stress tensor and finally we have also [306:57] obtained the transpose of the stress [306:59] tensor. So based on need you may again [307:01] perform further calculations also and [307:03] numpy will be the tool that may be [307:05] helping you to solve these type of [307:07] problems. Let us go to second example. [307:09] So here the statement is a mechanical [307:11] shaft is subjected to dynamic loads [307:14] causing angular displacements over time. [307:16] The angular displacement changes from 0 [307:18] to 90° and we need to calculate the s [307:21] cosine and tangent values. Then we need [307:23] to convert angle from radian to degree. [307:26] At last we need to find the inverse of [307:28] the trigonometric functions for the [307:29] selected values. So to perform this [307:32] first we need to consider the angle in [307:34] degree. So we are storing it theta [307:36] degree that is equal to 45. Next to [307:39] convert this angle into the radian we [307:41] need to use the inbuilt function that is [307:43] dot radian. So we will type angle in [307:46] radian equal to np dot radians and [307:48] within its argument this theta degree is [307:51] passed. Now to find the s cosine and [307:53] tangent values again these [307:55] functionalities are available within [307:56] numpy. So we will use the function np [307:58] dots sine np. dot cos and np.10 and [308:02] within its argument we will pass the [308:04] radian angle. So the value of sine, [308:06] cosine and tangents are stored into [308:09] these three variables and the inbuilt [308:11] functions are used. Now to perform the [308:14] second step, we may use the inbuilt [308:16] function that is degree or radian to [308:18] convert the angle vice versa. We are [308:21] converting from radian back to degree [308:23] that is np degrees and within its [308:25] argument we will pass our radian value. [308:28] After getting the degree value again we [308:30] may find the inverse trigonometric [308:32] values using the inverse trigonometric [308:34] functions that are the ark sign arc cos [308:37] and ark 10. So we are storing its answer [308:40] again into three variables that is ark [308:42] sine cos and 10 values and within these [308:45] functions we are passing our angle value [308:47] in degrees. So in the first step we have [308:50] provided the angle in degrees. Then we [308:53] have converted this angle into the [308:55] radian. Using these radian angles we [308:57] have calculated the trigonometric [308:59] values. Again this radian angle is [309:01] converted back to degree and using this [309:04] degree angle we have calculated the [309:06] inverse trigonometric values. Finally we [309:09] are displaying all the results that are [309:11] for the angle conversion and the [309:13] trigonometric and inverse trigonometric [309:16] values. So let us run the program to [309:18] check the output. So here you can see [309:20] that the angle is 45° and its [309:22] corresponding radian value is provided. [309:24] Then sin, cos and 10 angle values are [309:27] provided. Then again using the degree [309:30] values again inverse trigonometric [309:32] functions are calculated. So with the [309:35] use of trigonometric functions you may [309:37] easily perform these type of [309:39] calculations where you do not need to [309:41] write the code that how to calculate s [309:43] cosine and tan but you may use the [309:45] inbuilt functionality. Let us again [309:47] consider the third example. So here the [309:50] scenario given is a mechanical lab [309:52] stores a list of material names in [309:54] uppercase steel, aluminium, titanium. [309:57] There are certain steps that we need to [309:59] perform. First we need to convert all [310:01] material names to lower case. Then we [310:03] need to capitalize the first letter of [310:05] each material name. Then we need to [310:07] concatenate material names for the [310:09] documentation purpose. And fourth we [310:11] need to repeat material name multiple [310:13] times for the labeling purpose. So let [310:15] us first define the array of the [310:18] materials that is materials equal to np [310:20] dot array and the three materials are [310:23] provided. Now to convert the name of [310:24] these materials into the lower case in [310:27] numpy inbuilt function is available that [310:29] is the dot lower. So lowerase materials [310:32] that is equal to np dot care dot lower [310:36] and within its argument we will pass our [310:38] array. Now to capitalize first letter of [310:41] each material name there is inbuilt [310:44] function is available that is [310:45] capitalize. So to use that we will again [310:47] type np do.care dot capitalize and [310:50] within its argument we will pass our [310:52] lowerase materials array to show the [310:55] concatenation operation again we may use [310:57] the inbuilt function concat and for that [311:00] the function name available is the add. [311:02] So we will type np.care care dot add and [311:05] within its argument we have passed the [311:08] two materials from that array that is [311:10] material at zeroth index position and [311:12] material at first index position at last [311:15] for the repeatation of the material name [311:17] there is inbuilt function available [311:19] named multiply so we will use npcare [311:22] domultiply within its argument first we [311:25] have passed our required array comma 3 [311:28] represents that the each name gets [311:30] repeated three times in the given array [311:32] after performing all The required [311:34] operation we are displaying the results [311:36] based on the performed steps. So we are [311:38] printing the list of original materials [311:40] then lowerase materials then capitalized [311:43] materials then concatenated names and [311:45] finally the repeated labels. To check [311:47] the output run the program. So here [311:50] original materials are listed over here. [311:52] Then we have converted it to lowerase [311:54] materials. Then the first letter of each [311:57] material is capitalized. We have [311:58] concatenated two elements zerooth index [312:01] and the first index. So we are getting [312:03] the output steel combined with the [312:05] aluminum and this operation is performed [312:08] on the original array. So we are getting [312:10] this first capitalized letters [312:12] concatenated at this point and at last [312:14] we are getting the repeated labels. As [312:16] you can see steel is multiplied three [312:18] times, aluminum is multiplied three [312:20] times and same way the remaining [312:21] materials are multiplied by three times. [312:24] So if there is any need of string [312:26] manipulation during the array operation [312:28] also then at this point some of these [312:30] string functions are also useful to [312:32] perform these task. So I hope with these [312:35] examples you got the idea how one may [312:37] use the different array functions [312:39] available in the numpy to perform it may [312:42] be a mathematical operations or it may [312:44] be a text type operations. So keep [312:46] practicing this tool try to implement [312:49] these functionalities to have better [312:51] clarity about benefit of using numpy in [312:54] our regular task. So looking forward to [312:56] see you all in our further session. Till [312:58] then thank you. Hello everyone and [313:00] welcome back. Today we are going to talk [313:02] about how Jet GPT can be an incredibly [313:05] powerful tool for troubleshooting numpy [313:07] related issues. Whether you are a [313:09] beginner or an experienced programmer, [313:11] Jet GPT can assist you in solving [313:13] errors, optimizing code, and deepening [313:16] your understanding of numpy operation. [313:18] One of the most common challenges when [313:20] working with the numpy is facing [313:22] unexpected errors like safe mismatch, [313:24] index, out of bounds or unsupported [313:26] operant types. So instead of spending [313:29] hours searching for solution, you can [313:31] simply describe the error to chat GPT [313:33] and the Jet GPT can quickly identify [313:35] that type of error and it will guide you [313:37] to use the correct syntax with proper [313:40] informations. You may also learn the [313:42] important numpy operations using JG GPT. [313:45] For that you need to provide your [313:46] required prompts. Here are some of the [313:49] important prompts that you may provide [313:51] based on your requirement. Now suppose [313:53] you are a beginner to understand the [313:55] numpy. So you may first ask the question [313:57] that how do one can create a numpy array [314:00] with specific values. So suppose we are [314:02] providing the data how do one can create [314:07] numpy array with specific values. [314:11] So here checkp will analyze your [314:13] question and it will provide the data [314:15] based on your requirement. So here you [314:18] can see that we may use the inbuilt [314:20] arrow function, pull function, zeros, [314:23] ones, arrange function. So all these [314:25] functions we have already understood and [314:28] other than this there are list of [314:29] different functions are available. You [314:31] can also master that functions using the [314:34] Jet GPT. So here there are list of [314:36] different operations are provided. [314:39] Suppose you want to understand some of [314:41] the important mathematical functions [314:43] available in the numpy. Then also you [314:46] may provide the prompt such as explain [314:49] all important functions available in [314:52] numpy for mathematical operations with [314:56] syntax and hit enter. Here you will find [314:58] the list of different mathematical [315:00] operations that you may perform and that [315:02] are available within this numpy library. [315:05] Some of the functions we have already [315:07] understood but other than that there are [315:09] also list of different functions are [315:11] available that you may master using this [315:14] JGPT. So here you can see that there are [315:16] so many aggregation functions are also [315:18] available that are sum product [315:21] cumulative sum cumulative product then [315:23] statistical functions and so on. You [315:25] will find the list of different [315:27] operations that you may perform using [315:29] these numpy library. So I hope you got [315:32] the idea how you may master the numpy [315:35] using these chat GPT as your important [315:37] partner. There are list of functions [315:39] available that are goes on that you may [315:42] use based on the requirement. Now after [315:45] understanding all the important [315:46] functions suppose you want to have the [315:48] practical applications so also you can [315:50] ask the chat GPT about the practical [315:52] applications that is it may generate [315:54] some of the programs also. Suppose we [315:57] want to generate a numpy program to [315:59] solve a system of linear equations. So [316:01] we may provide the prompt generate numpy [316:04] program to solve a system of linear [316:07] equations. So here you can see that JPT [316:10] has provided one code providing the [316:13] array then some of its important data. [316:15] So using this concept you may get the [316:17] practical applications also and how to [316:19] solve that type of problems. To the left [316:21] hand side you will find the explanation [316:24] for that code also. Now here you will [316:26] find the option also suppose you want to [316:28] have the answer in chat instead. So you [316:30] may select this and here the entire [316:33] problem and its answers are provided in [316:35] these chat box only. So here you will [316:38] see that two linear equations are [316:40] provided and to solve these linear [316:42] equations you may use these inbuilt [316:44] functionalities available. So with this [316:46] I can say that CGP isn't just a [316:49] troubleshooting tool. It's your 24x7 [316:52] coding assistant, debugger, and tutor [316:54] for NumPy. It helps you fix errors [316:56] faster, learn concepts better, and write [316:59] cleaner and more efficient code. So the [317:01] next time you face a numpy challenge, [317:03] don't hesitate. You can just ask this to [317:06] JPT. Hello everyone. Today we are diving [317:08] into one of the most powerful and widely [317:11] used libraries in Python for data [317:13] analysis that is pandas. If you have [317:15] ever worked with data, whether it's [317:17] organizing spreadsheets, analyzing [317:19] trends, or preparing data for machine [317:21] learning, you will find pandas [317:23] incredibly useful. But what exactly is [317:27] pandas? So, pandas is a open-source [317:29] Python library designed for data [317:32] manipulation and analysis. It provides [317:34] two main data structures that is series [317:36] and data frame. And that we will [317:38] understand in detail in our further [317:40] sessions. Pandas has become an essential [317:42] tool for data scientist, analyst and [317:45] engineers worldwide. Pandas is mainly [317:48] used for data cleaning. So it is used [317:50] for fixing missing values, removing [317:53] duplicates and handling inconsistencies. [317:55] Second, it is useful for data [317:57] manipulation such as sorting, filtering, [317:59] grouping and merging data sets [318:01] effortlessly. It is also used for data [318:04] analysis to perform statistical [318:06] calculations and descriptive analytics. [318:09] It can work with metplot li and cun for [318:12] the data visualization to create the [318:14] charts and graphs. Pandas can handle [318:16] large data sets and it can help you to [318:19] work with millions of rows efficiently. [318:21] And the major important advantage is you [318:23] can read or write data from CSV, Excel, [318:27] JSON, SQL and other formats also. In [318:30] short, if you are working with data, [318:32] pandas makes everything simpler and [318:34] faster. Now you might wondering why use [318:36] pandas when python already has list and [318:39] dictionaries for storing data. So pandas [318:42] is designed for efficiency and ease of [318:45] use. Pandas is built on numpy making [318:48] data handling quicker than traditional [318:50] list. It can provide powerful functions [318:52] for filtering, grouping and transforming [318:54] data with just a few lines of code. It [318:57] is compatible with numpy, metlot lip, [319:00] seabon and scikit learn type of library [319:03] also. And in real world application, it [319:05] is used in finance, healthcare, [319:07] marketing, machine learning and many [319:09] more sectors. So instead of manually [319:12] processing data in Excel or writing long [319:14] Python loops, pandas allows us to work [319:17] more efficiently. So before we can start [319:19] using pandas, we need to install it. And [319:22] the installation process is simple. You [319:24] need to open your command prompt or [319:26] terminal and just type pip install [319:29] pandas and hit enter. But make sure [319:31] while executing this line you are having [319:34] working internet to install this pandas [319:36] library. And once installed pandas is [319:39] ready to use. Now that you have pandas [319:41] installed you need to import it into [319:43] your code. And the standard convention [319:46] to import the pandas is you need to type [319:48] as a first line import pandas as pd. And [319:52] here pd is a sword alias for pandas [319:55] which help us write cleaner code. So [319:58] pandas is must-h have library for data [320:00] analysis in python. We explored that [320:03] pandas is its importance installation [320:06] and how to import it. In the next [320:08] sessions we will dive deeper into data [320:10] manipulation and real world [320:12] applications. Thank you for joining this [320:14] session and looking forward to see you [320:15] all in the next lecture. Hello everyone. [320:18] In our previous session we introduced [320:20] pandas and learned how to install and [320:23] import it. Today we are going to focus [320:25] on one of the fundamental data structure [320:27] in pandas that is series. Pandas series [320:30] is a one-dimensional labeled array [320:32] capable of holding any data type. It may [320:35] be integers, floats, string, etc. But [320:38] keep in mind that it is useful for [320:40] one-dimensional array only. It is [320:42] similar to a column in Excel spreadsheet [320:45] or a list in Python but with additional [320:47] functionalities. Each element in series [320:50] has an index which act like a row label. [320:53] Let's create a simple pandas series to [320:55] understand how it works. For that let us [320:57] go to VS code. Here we have imported [321:00] pandas and for that the line mentioned [321:02] is import pandas as pd. Here we created [321:05] a series of numbers 10 to 50 and for [321:08] that we have stored the value into the [321:10] variable data and the function used is [321:13] pd dot series. Within its round bracket [321:16] the data array is provided. So within [321:19] square bracket data provided is 10 20 30 [321:22] 40 and 50. And then after the data is [321:24] printed. So within print argument data [321:28] is passed. Now to check the output let [321:30] us run the program. So here you can see [321:33] that the series of numbers are created [321:35] from 10 to 50 and the numbers on the [321:38] left that are 0 to 4 are default index [321:41] labels just like the row numbers in a [321:43] table. And at the bottom the data type [321:45] is provided and for this the data type [321:47] is int 64. So using this series function [321:51] one can create the simple panda series [321:54] having the default index numbers. As you [321:56] can see by default pandas assign a [321:59] numeric indexes starting from zero. [322:01] However we can customize these index [322:03] levels. To understand that let us again [322:06] go to the next program. Here you can see [322:09] that we have provided the data that is [322:12] equal to PD dot series within round [322:14] bracket elements are provided within [322:17] list that is 10 20 30. Now comma we will [322:22] write the labels for the index. So comma [322:24] index equal to within square bracket we [322:27] will provide the label for the index [322:30] which we require. So here we have [322:32] mentioned a comma b comma c. Then after [322:35] the data is printed. So now for this the [322:38] output will be the element will be 10 20 [322:41] 30 and the index number for the same [322:43] will be a b and c. After creating series [322:46] we can access the values also by using [322:49] labels or by using the index numbers. So [322:52] suppose we do not want to print all data [322:54] but we want to print a specific element [322:57] at that point we will type print within [322:59] its argument data that is being our [323:02] variable name and within square bracket [323:05] we may provide label or the index [323:07] number. Currently within double quote [323:10] the value B is provided that is the [323:12] label. So this B label will be assigned [323:15] to 20. So our output will be 20. Other [323:18] than this we may also provide the index [323:20] number. So next you can see that print [323:23] data within its square bracket provided [323:25] one. So here one refers to the index [323:28] number. So again index number start from [323:31] 0 1 2. So one refers to the element 20. [323:35] To check the output let us run the [323:37] program. So here you can see that now [323:39] the element will be 10 20 30 but the [323:42] index labels are a b and c. The data [323:45] type being int 64 only. For the second [323:48] we are getting the output 20 and same [323:51] way for the last one that is for the [323:53] index label we are also getting the [323:55] output 20. So whether you use default [323:58] index or custom labels pandas makes it [324:00] easier to retrieve the data. Till this [324:03] example we understood that using list we [324:06] can create the pandas series. But there [324:08] is also another option available to [324:10] create a series and that is from the [324:12] python dictionary and this is very [324:14] useful for handling the structured data. [324:17] So to understand that let us go to the [324:19] third example. So here you can see that [324:22] first we have created one dictionary [324:24] that is within its curly bracket we [324:26] provided key value pair key being the [324:29] apple and value being the 50 comma [324:31] banana col 30, orange col 40. Now this [324:36] dictionary can be converted to the [324:38] series and for that we can use pd dot [324:41] series and within its argument we will [324:43] directly pass our dictionary. So here [324:46] index will be the apple banana and [324:48] orange and the value being the 50 30 and [324:51] 40. Let us run the program to check the [324:54] output. So here you can see that apple [324:56] 50 banana 30 orange 40 and the data type [325:00] being the int 64. So here keep in mind [325:03] that the keys of the dictionary become [325:05] the index labels and the values of the [325:07] dictionary form the series data. This [325:10] method is useful when working with real [325:12] world data sets where data is naturally [325:14] stored as key value pairs. Now to [325:17] understand use of all these concept, let [325:20] us consider one example in the context [325:22] of mechanical engineering. So imagine we [325:25] are analyzing the properties of [325:27] different metals used in mechanical [325:29] design and for that we want to store the [325:31] metal densities in a pandas series. So [325:34] let us first store the material name and [325:37] its value into the dictionary data and [325:39] then we will convert it into the pandas [325:41] series. So here you can see that metal [325:44] density equal to aluminium colon value [325:47] is provided. Same way for the different [325:49] materials values are provided. Next we [325:51] are converting it into the series for [325:53] that PD dot series and within its [325:55] argument this dictionary data is passed [325:58] and then we are printing the final [326:00] material and density data. Now there is [326:03] a possibility that we want to access the [326:05] value of a specific material. So for [326:07] that we can access this using variable [326:10] name that is density series and within [326:13] it square bracket we will provide the [326:14] label name and label name. Suppose we [326:17] want to have the properties of density [326:19] of titanium. So as a label we have [326:21] provided the titanium. There is also [326:24] possibility that if you want to filter [326:26] the metals with the density above 5 g [326:28] per cm cube and for that very simple [326:31] approach is available and we are storing [326:34] that answer into the variable filtered [326:36] equal to density series and within it [326:39] square bracket we will provide our [326:41] filter logic. So density series greater [326:45] than five. So it will filter all the [326:48] data having the density greater than [326:50] five. And finally we have printed that [326:52] filtered data using the print statement. [326:55] Let us run the program to check the [326:56] output. So here you can see that all the [326:59] materials are listed with its data type. [327:02] The density of the titanium is accessed [327:04] and its value is 4.5. And now the data [327:07] is filtered based on the given entry [327:09] that is from these elements whichever [327:12] material is having density greater than [327:14] five that all are filtered and printed [327:16] over here. So you can see that engineers [327:19] can quickly compare densities when [327:21] selecting materials for lightweight or [327:23] high strength applications. The data can [327:25] be filtered, sorted or processed for [327:28] further analysis in material science and [327:30] manufacturing. And about these we will [327:32] learn in detail in our further sessions. [327:35] So to summarize today we learned about [327:37] pandas series, how to create them from [327:40] list and dictionaries and how they can [327:42] be applied in mechanical engineering. In [327:45] our next session, we will explore pandas [327:47] data frames which allow us to work with [327:49] multi-dimensional data. Thank you for [327:51] joining today. Keep practicing and see [327:53] you in the next session. Hello everyone. [327:56] In our last session, we learned about [327:58] pandas series. Today we will focus on [328:01] pandas data frame which is the most [328:03] commonly used data structure in pandas [328:05] for handling tabular data. A data frame [328:08] is a two-dimensional labeled data [328:10] structure similar to a table in Excel or [328:13] SQL database. It consists of rows and [328:16] columns where the each column is [328:17] essentially a pandas series. Data frames [328:20] allow us to store, manipulate and [328:22] analyze the structured data efficiently. [328:25] The key features of the pandas data [328:27] frame include it can hold different data [328:30] types such as integer, floats, strings, [328:33] etc. It can support custom indexing for [328:36] rows and columns. It allows easy data [328:38] filtering, shorting, and transformation. [328:41] and it works well with large data sets [328:44] and integrates with databases CSV, Excel [328:47] and more. Now let's explore different [328:49] ways to create pandas data frame. To [328:52] understand that let us go to the VS [328:54] code. So the first way is to create a [328:56] data frame from a Python dictionary and [328:58] it is best for small data sets where [329:01] data is naturally structured as a key [329:03] value pair. So here you can see that the [329:06] data is having the key name, age and [329:09] department and it is having the values [329:11] in the array format. So the key is [329:14] having the string data type whereas the [329:16] value is of list type. So the names [329:18] stored are Alice, Bob and Charlie. Their [329:21] age are 25, 30 and 35 and their [329:24] departments are mechanical, electrical [329:26] and civil. So all these data stored as a [329:29] dictionary format. Now to create a data [329:32] frame we will type PD dot dataf frame [329:35] and within its argument we will pass the [329:37] dictionary. We may print the data frame [329:39] using the print function and we will [329:41] pass our variable here. Keep in mind [329:43] that the keys of the dictionary become [329:46] column names and the values become rows [329:48] of the data frame. As we understood in [329:51] the concept of series the default labels [329:54] are provided that are starting from the [329:56] 0 1 and onwards. Same way in the data [329:59] frame also the default index labels are [330:02] provided and that will start from 0 1 2 [330:04] onwards. We can also add the custom [330:07] label and for that we may type df dot [330:11] index equal to now we can define the [330:14] index label that we want to provide. So [330:16] suppose we want to provide the index [330:18] label c001 c00 02 and c003. So again [330:23] these values are provided in the format [330:25] of list. After execution of these line [330:28] the index labels will change and the [330:30] custom labeling will be applied. To [330:32] check that let us run the program. So [330:34] here you can see that column names are [330:37] name age department and these are [330:39] nothing but the keys of the dictionary. [330:41] The default indexing labels are provided [330:44] that is 0 1 and two and remaining all [330:46] the entries are provided. Now after [330:49] execution of this custom labeling line [330:51] you can see that the the labels have [330:54] changed from 012 to C001 C002 and [330:58] further. So I hope you get the idea that [331:01] using data frame function we can create [331:03] the multi-dimensional data and we may [331:06] represent it in the format of row and [331:08] column as the data is available in the [331:10] tabular format. You are also having the [331:13] option to provide the custom labeling [331:15] and for that you need to use the index [331:17] concept. Now here you can see that we [331:20] have provided the some default data [331:22] available within dictionary and that [331:24] dictionary is converted to the data [331:26] frame. But there may be a chances that [331:29] the structure of data is no one but we [331:31] want to add the data later and for that [331:35] there is also option available to create [331:37] the empty data frame. So to understand [331:39] that let us consider another program. So [331:42] here you can see that we are writing df [331:44] equal to pd dot dataf frame and within [331:48] its argument we are only providing the [331:50] names of columns. So for that columns [331:53] equal to name, age and department and [331:57] then we are printing the data frame. So [331:59] it will only define the column names but [332:01] the data will remain empty. Now then [332:04] after we can add the data to this empty [332:06] data frame using this lock functionality [332:09] and about this we will understand in [332:11] detail in our further session. But to [332:13] just get the idea here we will mention [332:16] DF do.lo within its argument we will [332:20] indicate the index position that is zero [332:22] equal to we will define the data name [332:25] age and department. So that will be lis [332:28] 25 and mechanical. Same way for the [332:31] index position one further data is [332:33] provided and then finally we are [332:35] printing the data. To check that let us [332:37] run the program. So here first you can [332:40] see that the columns are defined but the [332:42] index values are empty. Now after adding [332:45] the data at the index position zero and [332:48] one name, age and department values are [332:50] added. So you may use this concept also [332:53] whenever you are having the structure of [332:55] the data but you want to add the data [332:57] later based on the requirements. Now let [333:00] us see how a data frame can be useful in [333:03] mechanical engineering context and for [333:05] that consider this example. So suppose [333:08] we are analyzing the mechanical [333:09] properties of different materials used [333:11] in the engineering design and for that [333:14] all the properties of different [333:15] materials are listed in the format of [333:18] dictionary. So the materials density [333:21] yanks modulus and the tensile strength [333:23] data are mentioned in this dictionary [333:25] format. This dictionary data will be [333:27] converted to data frame using PD dot [333:30] data frame within its argument. This [333:32] dictionary is passed. Then finally data [333:34] is printed. Now suppose from this data [333:37] we want to have the materials having the [333:39] tensile strength greater than 500. So [333:42] here you can see that we are having the [333:44] list in which key and value combination [333:47] is available and in the value again we [333:50] are having the list. So from that list [333:53] we want to extract the data. So we will [333:55] mention suppose we have stored it into [333:57] the strong materials equal to DF refers [334:01] to the outer data. From that outer data [334:04] again we want to represent this inner [334:06] list. So again we will mention DF within [334:08] square bracket. Again df within square [334:12] bracket we will provide the label that [334:14] is tensile strength megapascal and from [334:17] this we want to have the element having [334:19] the tensile strength greater than 500. [334:21] So we have mentioned greater than 500. [334:23] So it will collect greater than 500 [334:26] values from this tensile strength and [334:28] from these values again it will consider [334:30] the key data from these entire materials [334:33] section. So it will store only the data [334:36] of materials which are having the [334:38] tensile strength values greater than [334:40] 500. To check the output let us run the [334:42] program. So the data for four materials [334:45] are added and the columns are material [334:47] density youngs modulus and tensile [334:50] strength. Then after using this [334:52] filtration we are getting the list of [334:54] materials which are having the values of [334:57] tensile strength greater than 500 that [334:59] are steel and titanium. So engineer can [335:02] compare material properties before [335:04] selecting them for structural [335:05] application. This filtering materials [335:07] based on criteria is also helpful to [335:10] filter the data. So today we explored [335:13] the pandas data frame, its structure, [335:16] how to create it and its practical [335:18] applications in mechanical engineering. [335:20] In our next session, we will dive into [335:22] data frame operations. Here some key [335:24] points that you need to keep in mind are [335:26] the pandas data frame is a 2D labelled [335:28] data structure for handling structured [335:31] data. It can be created using [335:33] dictionaries, list, files or empty [335:35] structures. Data frames are useful in [335:37] engineering data science and real world [335:40] applications and mechanical engineers [335:42] can use them to store and analyze [335:44] material properties also. So thank you [335:46] for joining today's session. Keep [335:48] practicing and see you soon in the next [335:50] session. Hello everyone. In our previous [335:52] sessions, we learned about pandas series [335:55] and data frames. Today, we will focus on [335:57] an essential part of pandas that is [335:59] indexing. The index in pandas is used to [336:02] uniquely identify rows in data frame or [336:05] series that we have already understood [336:07] in our previous session. It helps in [336:09] faster data retrieval, filtering and [336:12] alignment when performing operation. By [336:14] default, pandas assign an integer based [336:17] index. But we can also set custom index. [336:20] This pandas index help in fast data [336:22] lookup. Supports custom and hierarchical [336:25] indexing and it can be reset, modified [336:28] or set from existing columns. So let's [336:31] explore different ways to create and [336:33] modify index in pandas. And for that let [336:36] us go to PS4. Here data is created using [336:39] the dictionary. The columns being name, [336:42] age, department and its datas are [336:45] provided. This data is converted to data [336:47] frame using functionality PD dot dataf [336:50] frame and within its argument variable [336:53] data is passed. Then data of data frame [336:56] is printed using this print function. [336:59] Here by default pandas assign an integer [337:02] index starting from zero. To check that [337:04] let us run the program. So here the [337:07] original data frame datas are printed [337:09] and the index is starting from zero. So [337:11] these default index are autogenerated [337:14] and this index can be replaced with the [337:16] custom values. Now to provide the custom [337:18] index we need to use the inbuilt [337:20] functionality that is set index and here [337:24] we have mentioned df dot set index the [337:29] index value that we want to set that is [337:31] the name of the data. So within double [337:34] quote we have provided the label that is [337:36] name comma in place equal to true. So [337:40] with the execution of these line the [337:42] name column is now the index and the in [337:45] place equal to true modifies the data [337:47] frame directly. Then further the data [337:50] frame data is printed. So again run the [337:52] program. So after setting the custom [337:54] index you can see that the index names [337:57] are Ellis, Bob and Charlie. Now suppose [338:00] we want to reset this custom index then [338:03] it is also possible and for that we need [338:06] to use the inbuilt functionality reset [338:09] index. So here df dot reset index within [338:13] its argument in place equal to true is [338:16] passed. So it will restore the index [338:19] with the original format. Then again [338:21] this data frame is printed. So to check [338:24] the output run the program. Now here you [338:26] can see that after resetting the index [338:29] further we are getting the numbers 0 1 [338:31] and 2. But in almost cases it is [338:34] suggested to provide the custom index as [338:36] if a column contains a unique values [338:39] setting it as an index can help in a [338:41] faster lookups. For example for the time [338:44] series type data setting date and time [338:46] as an index is beneficial. Now other [338:49] than this there is also one option is [338:51] available to create a range index and it [338:54] is useful when one want sequential [338:56] numbering of rows. Now to provide the [338:59] range index we need to again use the [339:01] inbuilt functionality that is pd dot [339:04] data frame within its argument we will [339:06] pass our dictionary type data. Then we [339:09] will define the index and index will be [339:12] comma index equal to pd dot range index. [339:16] within its argument we will pass three [339:18] data start stop and step. So suppose we [339:22] want to have the start value 100 stop [339:24] value 103 and we want the increment with [339:27] the step of 1. So step equal to 1. So [339:31] now the numbering will start from 100. [339:33] After that again the data frame is [339:36] printed. So let us run the program. So [339:38] here the index now starts from 100 [339:41] instead of zero. The start value is 100. [339:44] The step value is one. So it will start [339:46] from 100 1 0 1 and 1 02. Now again if [339:50] you want to reset this range index then [339:53] further we need to update the code and [339:55] use the inbuilt functionality reset [339:58] index within its argument drop equal to [340:01] true, in place equal to true. So here [340:05] drop equal to true removes the previous [340:07] index completely. Now let us again run [340:10] the program. So finally you can see that [340:12] the resetting to default index we will [340:14] get our default values. So in today's [340:17] session we explored pandas indexing its [340:20] role in data frames and how to [340:22] [clears throat] customize it. We also [340:23] saw its practical use. So some of the [340:26] key points that you need to keep in mind [340:28] are the default index in pandas is [340:31] integer based. Custom index can be set [340:33] using any column like names or ids. [340:37] range index are useful for sequential [340:40] numbering and engineering databases [340:42] benefit from meaningful indexes like [340:44] component ID and many other useful [340:47] names. So thank you for joining today. [340:49] Keep practicing and see you soon in the [340:51] next session. Hello everyone. In our [340:54] previous sessions, we learned about how [340:56] to create and index pandas data frame. [340:59] Now we will focus on how to view and [341:01] analyze data in pandas. This is crucial [341:04] for understanding the structure of data [341:06] sets before performing operations on [341:08] them. Data frame analysis is important [341:11] as it helps us to understand data [341:14] structure before processing. It allows [341:16] quick inspections of large data sets and [341:19] it is useful for detecting missing [341:21] values and data types. When working with [341:24] large data sets, it's inefficient to [341:26] print the entire data frame. Instead, [341:29] pandas provide functions to quickly view [341:32] parts of it. And the some of the [341:34] functionalities are head, tail, and [341:37] info. The head function displays the [341:40] first few rows of a data frame. So for [341:43] the head functionality within its [341:45] argument, we will pass the number and it [341:48] will print the n number of rows that are [341:50] passed as an argument. If we do not pass [341:53] any value, it will print five default [341:57] rows. Same way tail function shows the [341:59] last few rows of a data frame based on [342:02] the provided number. If we have not [342:04] provided any number then it will print [342:06] the last five rows. And the last info [342:09] function provides a summary of the data [342:11] frame including column names, data types [342:14] and missing values. To understand that, [342:16] let us go to the VS code and consider [342:18] one example. Here one data set is [342:21] provided having the columns component, [342:24] material and weight and its relevant [342:26] data is provided in the format of list. [342:29] This data is passed into the data frame. [342:31] Now suppose we do not want to print all [342:34] the data. In place of that we want to [342:36] print first three rows only. So for that [342:39] we will use the functionality within [342:41] print function DF dot head and within [342:45] its argument three is passed. So it will [342:47] print the first three rows of the data [342:50] frame. Now in place of that suppose we [342:53] want to print the last two rows only. So [342:55] for that we will type df.tail [342:58] and within its argument we have passed [343:01] to and to check the information of the [343:03] data frame we may directly use the [343:06] functionality df do.info to check the [343:09] output. Let us run the program. So first [343:11] you can see that it is displaying only [343:15] three rows of the data frame that is [343:17] bolt steel 10 nut brass and five and [343:21] washer aluminium and three but remaining [343:24] data is not printed in the second with [343:26] the tail functionality it will print [343:29] only last two rows of the data frame [343:31] that is pin copper and seven and rivet [343:36] steel and eight. Next info functionality [343:39] provide the information about the data [343:41] frame that is the range index is six [343:45] entries are from 0 to 5 that is index [343:47] position is from 0 to 5. It has total [343:51] three columns. The index are 012 columns [343:54] are component material and weight. The [343:57] nonnull count are 6 66. That means it [344:01] represents that there is no missing [344:04] value. The data type for these columns [344:07] are for the first two object and for the [344:09] weight it is being the int 64. So the [344:12] total data types are in 64 one times and [344:16] the object two times and the memory used [344:18] with this data being the 276 bytes. Now [344:22] suppose if we have not provided the [344:24] argument in the head and tail [344:26] functionality then by default it will [344:29] consider the five rows from the starting [344:31] or five rows from the last. So again let [344:35] us run the program. So here you can see [344:37] that first five rows are printed and [344:40] then last five rows are printed and the [344:43] information data will remain same. So in [344:45] today's lecture we learned how to [344:47] quickly analyze a pandas data frame [344:49] using head tail and info functions. [344:52] These functions help in understanding [344:54] data set structure before performing [344:56] further manipulation. In our next [344:59] session we will explore how to [345:01] manipulate the data frame data. Thank [345:03] you for joining this session. Keep [345:05] practicing and see you soon in the next [345:07] session. Hello everyone. In our previous [345:10] lectures, we learned how to view and [345:12] analyze data frames. Now let's explore [345:14] how to manipulate them by adding, [345:16] removing, and renaming rows and columns. [345:19] This is crucial when working with real [345:21] world data where we often need to clean [345:24] and modify data sets before analysis. So [345:26] data frame manipulation is important as [345:29] it allows data cleaning and [345:31] restructuring. It helps in customizing [345:34] data sets for specific task and it [345:36] enables efficient data analysis and [345:39] visualization. So to understand that let [345:41] us go to VS code. Here for the reference [345:44] a sample data frame is provided. It is [345:46] having the columns component material [345:48] and weight. This dictionary data is [345:50] converted to data frame and this data [345:52] frame is printed. So first let us run [345:55] the program to check the output. So here [345:58] original data frame is provided with the [346:00] four rows and four columns. First being [346:03] the index then component material and [346:07] weight. Now suppose in this data frame [346:09] we want to add the new column. So for [346:12] that we need to assign values to the new [346:14] column with name. So we will type df [346:18] within square bracket new column name [346:21] that is price and the values for this [346:23] column being 50 30 70 and 20. So with [346:27] the price label these new values will be [346:29] added to our data frame. Let us display [346:32] this updated data frame. Run the program [346:34] to check the output. So here after [346:37] adding the data frame new column is [346:39] added with the label price and with the [346:41] given values. So here directly assigning [346:44] values creates a new column. Column [346:47] values must match the number of existing [346:49] rows that one need to keep in mind. Now [346:52] next suppose one want to add a new row [346:55] and to add a new row we are having the [346:57] functionalities log or append but these [347:00] are depreciated in new pandas version. [347:03] So it is preferred to use the inbuilt [347:05] functionality concat. So for that we [347:07] will type new row equal to the data [347:10] being component is bolt material is [347:13] steel weight 50 and the price 10 and all [347:17] these data is created in the format of [347:19] dictionary. Now to add these in existing [347:21] data frame we will write pd dot concat. [347:25] We want to add it into the existing data [347:27] frame df pd dot dataf frame. within its [347:32] argument we will pass our new row as an [347:35] list and a comma ignore index equal to [347:38] true. So here this pd.com concat is the [347:41] preferred method for adding rows and it [347:44] is also suggested to set ignore index [347:48] equal to true to maintain the clean [347:50] index. So this will add the new row. Now [347:53] to check the output again let us print [347:55] the updated data frame and run the [347:58] program. and the new data added is bolt [348:01] steel 50 and 10 in the respective [348:03] columns. Now next let's see how to [348:06] remove unwanted data from the data [348:08] frame. Suppose we want to drop a price [348:10] column and for that we need to use the [348:13] inbuilt functionality drop. So we will [348:16] type df equal to df dot drop. We want to [348:19] drop the columns. So columns equal to [348:22] the label of that column within square [348:25] bracket. So we want to drop the price [348:27] column. So we will type its label. So [348:30] this columns equal to price ensures only [348:34] the specified column is removed. And [348:36] here keep in mind that the original data [348:38] frame is not modified unless reassigned. [348:41] So here we have reassigned the df.trop [348:45] to original df. Again let us print the [348:48] updated data frame and run the program. [348:51] So here you can see that data frame [348:53] after dropping the price column these [348:55] price column is removed and the [348:58] remaining data is printed. Next suppose [349:00] we want to drop the row with the index [349:03] two. So for that we need to type df [349:06] equal to df dot drop and here we will [349:09] provide the index equal to index value. [349:12] So here index equal to two refers that [349:14] row at the second index position gets [349:17] deleted. Here keep in mind that index [349:20] must match an existing row if needed. [349:23] After dropping the row, let us again [349:26] print the updated data frame. Let us run [349:28] the program. So here you can see that [349:31] the index available are 0 1 3 and 4. The [349:36] row available at the second index is [349:38] deleted. Now if needed you can also [349:40] reset the index using the df dot reset [349:44] index. And here we will keep drop equal [349:46] to true. And that we have already [349:48] understood in our previous session. So [349:50] suppose here we want to reset the index. [349:53] So we will type df equal to df dot reset [349:58] index and drop equal to true. Now run [350:01] the program to check the output. So here [350:04] you can see that now index is reset and [350:07] after deleting the row with index two it [350:10] will reset the index positions. Next, [350:13] let's learn how to rename rows and [350:15] columns for better readability. Now, to [350:18] rename the column, we will use the [350:19] inbuilt functionality rename. So, [350:22] suppose we want to rename the column [350:24] component and weight. So, we will type [350:26] df do.t rename within its argument [350:29] columns equal to within curly bracket. [350:31] Now, we will rename the data. So, [350:34] component colon part name. So, the [350:36] component will be renamed with the part [350:38] name and the weight will be renamed with [350:41] the mass. So the format is columns equal [350:44] to within its curly bracket old name [350:46] colon new name. Let us print the updated [350:49] data frame and run the program. So here [350:52] data frame after renaming the columns [350:54] the component is renamed with the part [350:57] name and the weight is renamed with the [350:59] mass. Next let us understand how to [351:02] rename the row indexes. And for that we [351:04] need to type df do.t dot rename within [351:07] its argument index equal to within curly [351:10] bracket again we will provide old index [351:13] colon new index so 0 col a 1 col b 3 col [351:18] c and 4 col d. So it will replace the [351:21] old index with the new index label. So [351:24] using this concept row index can be [351:27] changed to the custom labels. Let us [351:29] again print the updated data frame to [351:32] check the output. So run the program [351:35] here you can see that at the final row [351:38] index are changed and they are renamed [351:40] as A B C and D. So I hope you got the [351:44] idea how to add, remove and rename rows [351:47] and columns. So in today's lecture we [351:50] covered essential data frame [351:51] manipulation techniques that are adding, [351:53] removing and renaming rows and columns. [351:56] These operations are crucial for [351:58] preparing data sets for analysis. In the [352:01] next session, we will explore in detail [352:03] about pandas indexing and slicing. So, [352:06] thank you for joining today's session. [352:09] Keep practicing and see you soon in the [352:11] next session. Hello everyone. Today we [352:14] are diving deeper into the most powerful [352:16] feature that is indexing and slicing. [352:19] These operations allow us to efficiently [352:21] access and manipulate data within data [352:24] frame. Understanding how to use lock and [352:27] eyelock functionality properly will make [352:29] your data analysis task much easier and [352:32] more efficient. This indexing and [352:34] slicing is important as it helps in [352:37] selecting specific rows and columns for [352:39] analysis. It allows efficient data [352:41] filtering and subsetting and it is [352:44] essential for handling large data sets [352:46] in real world applications. Before we [352:48] explore lo and let's revise how to [352:52] access columns in pandas's data frame. [352:55] So as an example sample data frame is [352:57] provided and this dictionary data is [353:00] converted to the data frame and it is [353:02] printed using inbuilt functionality. Let [353:04] us run the program to check the sample [353:07] data. So here five columns and four rows [353:10] are available. Now suppose we want to [353:13] access a single column named component. [353:15] So we may print df and within its square [353:19] bracket we will provide the label that [353:21] is component. So it will print only data [353:24] related to the component and its index [353:26] position. So let us run the program. So [353:29] here the index positions and the [353:31] component data are printed. [353:33] [clears throat] So here you can see that [353:34] it returns the pandas series. Now next [353:37] we need to understand again two inbuilt [353:39] functionalities that is log and iO. So [353:43] to understand that let us go to the [353:44] presentation. The first LO functionality [353:47] is known as the label based indexing. To [353:50] use the LO functionality we will type LO [353:53] within square bracket we may provide row [353:55] label, column label. The second is the [353:58] ILO functionality and that is the [354:01] integer based indexing. So for that we [354:04] may type and within it square bracket [354:07] row index, column index. So in these [354:09] both functionalities you may get the [354:12] difference that LO is available for [354:14] label based indexing and eyelock is [354:16] available for integer based indexing. [354:19] Now let's explore LO functionality. For [354:22] that again go to VS code. Now here [354:24] suppose we want to access a single row [354:27] using LO functionality. We will type DF [354:31] do.log and within it square bracket 1. [354:34] So this will consider that we are [354:36] referring to the row with the index [354:38] level one. Let us run the program to [354:41] check the output. So here you can see [354:43] that row with index level one is having [354:46] component bearing material brass weight [354:50] 150 and the price 30. The name of this [354:54] row is one and the data type is object. [354:56] Now next suppose we want to access the [354:58] entire material column using this [355:00] functionality. So we need to type df [355:03] do.log within it square bracket. First [355:06] we refer the colon and we are not [355:08] providing any values. So it refers to [355:10] the all data of the row. Comma we want [355:14] to consider only material column. So [355:16] within double quote the label provided [355:18] is material. So this will select the [355:20] entire material column. So it will give [355:22] us the output the data of all entries [355:26] available within this material column. [355:28] To check the output let us run the [355:30] program. So here you can see that the [355:32] labels and the data of material column [355:35] are provided. The name of the column is [355:37] material and the data type is object. [355:39] Next suppose one want to access the [355:41] specific cell using this lock [355:43] functionality and for that suppose we [355:45] want to access second row and within [355:48] this second row we want to access the [355:51] data of weight column. So we need to [355:54] type df.lo Outlook two refers to the [355:57] label of the second row, weight refers [356:01] to the column name. So it will give us [356:04] the data from the second row and from [356:07] the weight column it will print the [356:09] output 320. Run the program to check the [356:12] output. So here we are getting the [356:14] output 320. Next we may also access the [356:17] multiple rows and specific columns using [356:19] this lock functionality. So for that we [356:22] need to type suppose we want to access 1 [356:25] 2 3 row and two columns component and [356:28] weight. So we will type df.log within it [356:31] square bracket 1 col 3 comma within list [356:34] format component comma weight. So it [356:37] will print the data of rows with the [356:40] label 1 2 and 3 and the data of column [356:43] component and weight. Run the program to [356:46] check the output. So here you can see [356:48] that the rows printed are 1 2 and three [356:51] and the columns printed are only [356:53] component and width. Here keep in mind [356:56] that this dot log includes the end point [356:59] meaning row index three is also selected [357:02] and as you can see in the output. Next [357:05] we may also filter the data using this [357:08] log functionality. So suppose we want to [357:10] print the rows which are having the [357:13] weight greater than 100. So for that we [357:16] need to type df do.lo within its [357:19] argument again we want to access the [357:21] specific rows. So df within square [357:23] bracket label that is weight and the [357:26] value we want to consider greater than [357:28] 100. So it will select all the rows [357:30] which are having the weight greater than [357:32] 100 and that will be printed on the [357:35] screen. So run the program to check the [357:37] output. So here you can see that the [357:39] data is filtered and all the rows are [357:42] printed which are having the weight [357:43] greater than 100. So here you can see [357:46] that this dotlo supports boolean [357:48] indexing allowing complex filtering [357:50] operations also. Now next let's look at [357:53] the functionality that is integer based [357:56] indexing. Suppose we want to select a [357:59] single row using these functionality. So [358:02] we need to type df do iillo within it [358:05] square bracket 1. So it selects the [358:07] second row that is having the index one. [358:10] Let us run the program to check the [358:12] output. So here you can see that the [358:15] second row with the index one is [358:16] selected and all its datas are printed. [358:19] To select the single column using iO [358:22] functionality again you need to type df [358:25] do.log. We want to select all rows. So [358:28] column, 2. So it will select the third [358:31] column for all the rows. Run the program [358:34] to check the output. So here you can see [358:37] that the third column its name is weight [358:39] data type is in 64 and its index [358:42] positions are considered. So all the [358:45] rows for this third column are printed [358:48] using this functionality. Again you may [358:50] select any number of rows and columns [358:53] you want to print. So suppose we want to [358:56] access rows one and two and column 0 to [358:59] 1. So we need to type df.lo I log for [359:03] the row 1 col 3 comma for the column 0 [359:06] col 2. Here keep in mind that iO [359:09] excludes the end point meaning row index [359:12] 3 is not included. So as we want to [359:15] select rows 1 to 2 we have mentioned 1 [359:18] col 3 as 3 is excluded. Let us run the [359:21] program to check the output. Here you [359:24] can see that rows with the index label 1 [359:27] and two and the columns with the index 0 [359:31] and one that is component and material [359:34] are selected and printed. In this case [359:37] for the row index three and for the [359:39] column two these two are excluded from [359:42] the data. So I hope you got the idea how [359:45] to make use of this log and [359:47] functionality. Now as we understood this [359:49] lo and functionality let's compare them [359:52] side by side to get the clarity. So this [359:55] dot lock functionality is the label [359:57] based indexing whereas is the integer [360:00] based indexing.lo functionality includes [360:03] the endpoint in slicing whereas dot [360:06] iillo excludes the endpoint in slicing. [360:09] The boolean indexing that is filtering [360:12] is supported in LO functionality but it [360:15] is not supported in the ILO [360:17] functionality. So in today's session we [360:20] explored indexing and slicing in pandas [360:23] using these lo and these methods allow [360:26] us to access filter and manipulate data [360:29] efficiently. Mastering them will [360:31] significantly enhance your data handling [360:33] skills. So thank you for attending [360:35] today. Keep practicing and see you soon [360:37] in the next session. Hello everyone. [360:40] Today we are going to discuss one of the [360:42] most fundamental aspect of data analysis [360:45] that is selecting data. When working [360:47] with large data sets, efficiently [360:49] retrieving specific rows and columns is [360:52] important. In this session, we will [360:54] explore various ways to select data [360:56] using query and filtering based on list. [361:00] Data selection is important as it helps [361:02] in extracting relevant information for [361:04] analysis. It improves data processing [361:07] efficiency and it allows for advanced [361:10] filtering and manipulation. We have [361:12] already understood how to access data [361:14] using basic indexing and slicing [361:17] techniques. We have also understood we [361:20] can pass multiple columns as a list of [361:22] column names. Next, we also explored log [361:26] and functionalities to select data. Now [361:29] we need to understand how to select the [361:31] specific rows or columns based on the [361:34] specific criteria. So for that we need [361:36] to filter rows based on the specific [361:39] conditions. To understand that let us go [361:41] to the VS code for the sample dictionary [361:43] type data is provided having the key [361:46] component material weight and price. [361:48] This dictionary data is converted to [361:50] data frame and it is printed using the [361:52] inbuilt function. Let us run the program [361:55] to check the original data. Now suppose [361:57] we want to select a component heavier [361:59] than 100 g. Now for this we need to use [362:02] the boolean indexing and it returns only [362:05] the rows where the condition is true. So [362:08] here we need to type df square bracket [362:11] within these again we need to check [362:13] condition for the weight. So df again [362:16] within square bracket label weight and [362:19] if it is greater than 100 we need to [362:21] print that rows. So it will filter the [362:24] rows having the weight greater than 100. [362:26] To check the output, run the program. So [362:29] here filtered rows are printed having [362:31] the weight greater than 100. Next [362:34] suppose we want to select the rows where [362:36] the price is more than 30. So here we [362:39] will understand new function that is [362:41] query and to use that function we need [362:44] to type df do.query within its argument [362:48] we will write the condition. So within [362:50] double quote is we want to mention the [362:53] label. So we will mention the label [362:55] within the single quote. So price that [362:58] is greater than 30. So it will consider [363:01] all the rows having the price greater [363:03] than 30. So you can see that this query [363:06] is more readable than the boolean [363:08] indexing. And the major advantage of [363:10] this is it supports the multiple [363:13] conditions also. To check the output let [363:15] us run the program. So here you can see [363:18] that the rows are filtered having the [363:20] price greater than 30. Now next suppose [363:23] we want to select the rows for which [363:26] weight is greater than 100 and price is [363:28] less than 50. So if we want to use the [363:31] multiple condition then at this point [363:33] this query function is also useful. So [363:36] for this we need to type df dot query [363:39] within double quote weight greater than [363:42] 100 symbol and and price less than 50. [363:46] Here keep in mind that within this [363:48] double quote whatever label we want to [363:50] select that will be mentioned within the [363:53] single quote. So this query select the [363:56] rows having the weight greater than 100 [363:57] and price less than 50. To check the [364:00] output let us run the program. So here [364:03] you can see that there is only one row [364:05] which satisfies this condition. So this [364:08] data is selected and printed on the [364:11] screen. We can also select rows where [364:14] columns contains values from a [364:15] predefined list. So for that we need to [364:18] follow the process. First we will select [364:21] the components that we want to select [364:23] that is within square bracket we have [364:25] mentioned gear, sub. It represents that [364:28] we want to select the component that are [364:30] either gear or sub. Now for that we need [364:33] to use the functionality is. So we need [364:37] to mention df within square bracket [364:40] inside that again we want to select the [364:42] list component. So df square bracket [364:44] component dot is in. Now within its [364:48] argument we will pass the component that [364:50] we want to check that is gear and shaft. [364:53] So within this we will pass this list [364:55] selected components. So it will select [364:58] the row which is having the component [365:00] gear or shaft. Let us run the program to [365:03] check the output. So here you can see [365:05] that only two rows have been selected [365:08] having the component gear and so this [365:11] een requires the argument list and it is [365:15] used for filtering rows where values [365:17] matches a list. So in today's lecture we [365:20] explored various ways to select data in [365:23] pendance. In previous sessions we [365:25] already learned about indexing, slicing [365:27] using log and log and in these we [365:30] understood filtering with conditions and [365:33] using query function. These methods are [365:35] essential for efficient data [365:37] manipulation. So I hope you got the idea [365:39] how to use these functionalities to [365:42] select the data. Looking forward to see [365:44] you all in our further session. Till [365:46] then thank you. Hello everyone. Today we [365:49] will explore two important pandas [365:51] functionality that are crucial for [365:53] organizing and cleaning data. The first [365:55] one is multi-indexing and it is used for [365:58] multiple level of row indexing. The [366:01] second is handling duplicates and it is [366:04] used for identifying and removing [366:06] duplicate values to clean the data sets. [366:08] These concepts are widely used in real [366:11] world applications especially in data [366:13] analytics, finance and engineering [366:16] fields where large data sets require [366:19] structured indexing and cleaning. So [366:21] these multi-indexing allows us to create [366:24] hierarchical indexes for our data frame [366:27] making it easier to store and retrieve [366:29] complex data. Instead of having single [366:32] level index, we can have multiple levels [366:34] such as country is connected to city. [366:37] There are two possible ways to create a [366:39] multiple index that is using tpples or [366:42] list. To understand that let us go to VS [366:46] code. So for your reference here data [366:48] set containing population and GDP data [366:51] for different cities in two countries is [366:54] provided. We will use tpples to create [366:56] the multi-index. So as you can see index [366:59] equal to pd dot multi-index dot from [367:05] tpples within round bracket we will [367:07] provide the list of tpples. So within [367:10] square bracket the tpples are provided [367:12] India comma delhi India comma Mumbai [367:16] comma USA New York and USA comma Chicago [367:20] and the names for these index are names [367:23] equal to country comma city. So for each [367:26] tpple country being India, India, USA, [367:30] USA and the city being Delhi, Mumbai, [367:33] New York and Chicago. Now suppose we [367:35] want to create the data frame having the [367:37] two columns that is population and GDP. [367:40] So data equal to dictionary is created [367:43] population with the values and GDP with [367:46] the values. Now using this dictionary [367:48] data frame is created. So pd dot dataf [367:50] frame within its argument data comma [367:54] custom index is created and that is [367:56] index equal to the name of variable that [367:59] is index. So index equal to index. This [368:02] created data frame is printed using the [368:04] print function. Now suppose if you want [368:07] to access all the cities in India from [368:10] this data frame. So we will type print [368:13] df.log [368:15] and within its argument we will pass [368:17] India. So it will access all the cities [368:19] connected with the country India. Next [368:22] suppose we want to access a specific [368:23] city New York. So for that we will type [368:26] df.lo from country USA we want to access [368:31] the New York. So we have mentioned New [368:33] York. So it will provide all data [368:35] related to the New York. Next suppose we [368:38] want to create the multi-index data [368:41] using arrays. So we will store the data [368:44] into the arrays. So array is equal to [368:46] within square bracket two [368:48] one-dimensional arrays are provided. So [368:51] first represents the country and the [368:53] second represents the city. So first is [368:56] India, India, USA, USA. The second is [368:59] Delhi, Mumbai, New York, Chicago. Now to [369:02] create a multi-index we will create [369:04] index equal to PD dot multi-index dot [369:08] from arrays. within its argument we will [369:10] pass the arrays comma names equal to [369:13] country city. Next suppose we want to [369:16] provide one column that is population. [369:18] So pd dot data frame within its argument [369:21] data provided is population with the [369:23] values and index being the custom index. [369:26] So index equal to index and then after [369:29] again this multi-index data is printed. [369:32] To check the output let us run the [369:34] program. So here you can see that the [369:36] first index is country having the data [369:38] India, USA. Second index is city having [369:41] the data Delhi, Mumbai, New York and [369:44] Chicago. And for these two columns [369:46] populations and GDP are mentioned as we [369:49] have accessed all cities in India by [369:52] passing the label India. So it will [369:54] print all the data connected to the [369:57] India country. So it will print the city [370:00] and the population and GDP data of the [370:03] Indian city. Next we have access the [370:05] data using country and city name. So it [370:09] will print all the data related to the [370:11] specific combination. So we are having [370:13] the USA and New York and it is having [370:15] the population 8 and GDP 1800. So it [370:19] will print population 8 GDP 1800. The [370:23] name of this data is USA, New York and [370:26] the data type is in64. Next again using [370:29] the arrays concept data frame is [370:32] created. It is having the index country [370:35] and index city and only one column is [370:38] added that is population. So here also [370:41] you can see that the multi-index are [370:43] provided. So using these functionalities [370:45] from tpples or from arrays one can [370:48] create a multi-index when we have [370:50] separate list. Next let us understand [370:53] how to manage the duplicate data from [370:55] the given data set. In a real world data [370:58] sets, the duplicate values can arise due [371:00] to data entry errors, systematic [371:03] repetition in logging or merging data [371:05] sets from different sources. To identify [371:08] and remove duplicates, pandas provide [371:10] functions like duplicated or drop [371:14] duplicates. For your reference, one [371:17] dictionary data is provided. This data [371:20] is converted to the data frame and then [371:22] original data frame is printed. Here you [371:24] can see that there is a duplication in [371:26] the name, there is duplication in the [371:29] city also and there is duplication in [371:32] the age also. Now suppose first we want [371:34] to find the duplicate rows. To find the [371:37] duplicate rows, we can use the inbuilt [371:39] functionality duplicated. So here you [371:41] can see that we have stored the answer [371:43] of duplicate rows into the variable [371:46] duplicates that is equal to df dot [371:48] duplicated. So this provide the answer [371:51] for each rows in the format of boolean [371:53] that is it may be true or false. This [371:56] function check the data of one row in [371:58] comparison with the previous rows and if [372:01] all the data with the previous row match [372:03] then it says that the data is [372:05] duplicated. So it returns the value true [372:08] for that row. Next suppose we want to [372:10] check the duplicate based on the name [372:12] column. So for that we may type [372:15] duplicated name equal to df do.d [372:19] duplicated within its argument we will [372:21] pass subset equal to within square [372:23] bracket name. So now it will consider [372:26] only column name. So it will compare the [372:28] name again it will compare the name with [372:30] the previous name. If it matches then it [372:33] returns the true as it returns the [372:35] false. Next suppose we want to remove [372:38] the duplicate rows. For that we may just [372:41] simply use the inbuilt function drop [372:44] duplicates. So here we will store the [372:47] answer into df_clean equal to df dot [372:51] drop duplicates. So it will remove the [372:54] duplicate rows. But here keep in mind [372:56] that it will keep the first occurrence [372:59] and remove the all remaining occurrence. [373:01] Now suppose we want to remove the [373:02] duplicate and keep the last occurrence. [373:05] So for that we need to provide the [373:08] argument into this function such that df [373:11] dot drop duplicates within its argument [373:14] keep equal to within double quote last. [373:17] So it will remove all the duplicates but [373:19] it will keep the last occurrence. Now to [373:22] check the output let us run the program. [373:24] Here you can see that the original data [373:26] frame is provided. Next we have used the [373:29] function df do.d duplicated. So it will [373:32] check for the rows if any duplicate row [373:34] is present or not. So here you can see [373:36] that zero false as it is the first row [373:39] that will be always considered as a [373:41] false. Then one false then for the two [373:45] you can see that amid 25 Delhi this data [373:48] is already available within the first [373:51] row. So this third row is duplicate. So [373:54] at the third row it is mentioned true. [373:57] Same way for the fourth row the value is [373:59] John 22 Pune that is new so it is [374:02] represented false and for the last row [374:04] again Sara 30 Mumbai this data is [374:07] previously present so it returns the [374:09] true so from this boolean answer true or [374:12] false you can see that if any duplicate [374:14] rows are present or not next we have [374:17] mentioned the function that checks the [374:19] duplicate within the name so here you [374:22] can see that again for the given index [374:25] position if the name matches or not, it [374:27] will provide the data. But here it will [374:30] only check the data for the given name. [374:34] Next, we have removed the duplicate [374:36] rows. So here you can see that once we [374:38] have executed drop duplicates, it will [374:41] remove all duplicate rows. So here [374:44] duplicate rows are amit that is at the [374:47] second index position and at the fourth [374:49] index position. So it will remove [374:52] remaining occurrences in the given rows [374:54] and it will only keep the first [374:56] occurrence. Next we run the function [374:59] drop duplicates keep last. So it will [375:02] remove all the occurrence but it will [375:05] keep the last occurrence. So here you [375:07] can see that first occurrence at the [375:09] index position zero and at the index [375:12] position one have been removed and the [375:14] kept rows at the index position are 2, 3 [375:17] and four. So based on requirement you [375:20] may select if you want to keep the first [375:21] occurrence or the last occurrence. So I [375:24] hope you got the idea that [375:25] multi-indexing allows structured [375:28] hierarchical indexing whereas handling [375:30] duplicates helps in cleaning the data [375:32] sets. These techniques are widely used [375:34] in data science and engineering [375:36] applications to manage the large data [375:38] sets efficiently. Keep practicing these [375:40] tool and make utilization of these [375:43] functionalities in your programs to get [375:46] the more clarity. Looking forward to see [375:48] you all in our further session. Till [375:50] then thank you. Hello everyone. Today we [375:52] are going to discuss about essential [375:54] aspect of data handling using pandas [375:57] that is reading and writing files. We [376:00] will cover how to read data from Excel [376:02] file. How to write an Excel data set to [376:04] a CSV file with specific formatting. [376:08] Then how to read data from CSV file and [376:11] how to write CSV data set to another CSV [376:14] file with different formatting. These [376:16] operations are crucial in data analysis [376:18] and pre-processing especially when [376:20] working with large data sets in [376:22] industries such as mechanical [376:24] engineering, finance, healthcare and [376:26] more. So let us first understand some of [376:28] the functionalities available to work [376:31] with the Excel and CSV files. So these [376:34] Excel files are commonly used in [376:36] industries for maintaining structured [376:38] data. Pandas provides read Excel [376:41] function to import these files [376:43] efficiently. So to use this function you [376:46] need to type read excel within its [376:48] argument as a first parameter you can [376:51] pass file path. Second you can pass a [376:53] seat name. It may be a index or name. [376:56] Third you can pass header and as a [376:59] header name you will pass header equal [377:01] to row number to use the column name. [377:03] Then you can pass the custom column [377:05] names also. For that you need to type [377:08] names equal to any required name or you [377:10] can type none. Next you can pass the [377:13] index column. So here you need to [377:15] mention index column equal to any number [377:18] which column you want to set as the [377:20] index. Then you can pass use calls also. [377:24] For that you need to type use calls [377:26] equal to the column which you want to [377:29] read. And the last is the data type for [377:31] the each column. So here the compulsory [377:34] part is the file path that you need to [377:36] provide. Remaining all arguments are [377:39] optional. Now let's understand how to [377:41] read data from CSV file into pandas. So [377:45] for that the functionality available is [377:47] read csv within its argument. First we [377:50] can pass path to the CSV file. Then [377:53] separator which is the delimiter and by [377:56] default it is the comma. Next we can [377:58] pass the header that is equal to number [378:01] for which we want to use the column [378:03] names. Then to assign the custom column [378:06] names, we can assign the value names [378:08] equal to any required name. Same way the [378:11] index column can also be set for which [378:13] we want to set the index. Next is the [378:16] use column. So here we will pass the [378:18] column which we want to read and the [378:21] last is the data type of each column. So [378:23] compared to read excel functionality, [378:26] one parameter is changed and that is the [378:29] separator. And here CSV only refers to [378:31] the comma separated values. But other [378:34] than comma you can also pass many [378:37] required different separators. Now once [378:39] we have loaded the Excel data we may [378:41] want to convert it to CSV file also. And [378:45] for that pandas provide the another [378:47] functionality that is to CSV. So within [378:50] these also first we will call the to CSV [378:53] function. Within its argument we will [378:55] pass the path to save the CSV file. then [378:58] the separator or the delimiter which we [379:00] want to assign. Next argument is index [379:03] equal to either true or false. So this [379:06] represents if we want to write row [379:09] indexes then we will provide the value [379:11] true. Next header equal to true or false [379:14] means again if we want to write column [379:16] headers then we will pass the value [379:18] true. And at last columns equal to [379:20] specific column that we want to write. [379:23] If we do not want to write any column [379:25] then we will assign the value none. Let [379:27] us understand all this functionality [379:29] using one example. For that go to VS [379:31] code. Now our first objective is to read [379:34] the Excel file and currently for the [379:37] reference purpose I have provided the [379:39] Excel file in the current working [379:41] directory and they are the sample data [379:44] dot CSV and sample data. Excel file. So [379:48] these both files are present within the [379:50] same directory. Now to write the Excel [379:52] file we need to use inbuilt [379:54] functionality read excel. So we need to [379:57] type df1 equal to pd dot read excel and [380:01] the path of the excel as the file is [380:04] available in current working directory [380:06] only. So we need to only provide the [380:08] file name. If the file is available at a [380:10] different directory then we need to [380:12] provide the proper detailed path. So [380:15] here within double quote we have [380:16] mentioned the file name that is sample [380:18] data dot xlsx. So this function will [380:22] read this file and this file will be [380:24] converted in the data frame format. Now [380:26] this read data is printed using the [380:28] inbuilt function print. So let us run [380:31] the program to check if data is printed [380:33] or not. So here you can see that the [380:36] Excel data available within this file [380:39] are printed over here and the index [380:41] values are also provided. For your [380:43] reference let us open the sample data [380:47] Excel file also. So here you can see [380:49] that this sample data Excel file is [380:52] having the three rows with the three [380:54] different values and the column names [380:56] are name, age and department. So here we [381:00] have executed the program and you can [381:02] see that the by default the columns and [381:04] the index values are considered to be [381:06] true. So if we are not defining the [381:08] value then this function will [381:10] automatically define the value to be [381:12] true and import all the data. Now next [381:15] after reading these Excel file suppose [381:17] we want to write these Excel data to CSV [381:20] file then we need to type df1 dot2 csv [381:25] within its argument first we will pass [381:27] file name here file name we have [381:30] provided to be excel_2 [381:32] csv csv and this file will be created to [381:36] the current working directory only the [381:38] value of index is considered to be false [381:40] means this index will not be considered [381:43] for the input Separator is considered to [381:46] be semicolon and the header is [381:48] considered true. That means it will keep [381:50] the header value in the file. So let us [381:52] check the current working directory. [381:54] Here you can see that no file named [381:57] Excel to CSV is available. Now run the [382:00] program to check the output. So here you [382:03] can see that we have printed the data [382:04] that it is printed from Excel file and [382:07] has been returned to Excel to CSV. CSV [382:11] and the separator used is the semicolon. [382:14] So again let us check the current [382:15] working directory. Here you can see that [382:18] one file is created named Excel_2 [382:22] CSV. Let us open this file. So here you [382:25] can see that within CSV data first [382:27] header is considered then all the three [382:30] rows are considered and all the datas [382:32] are separated using the semicolon. Let [382:35] us close this file. Now suppose one want [382:37] to keep these index labels also in the [382:40] CSV file then we need to keep the value [382:43] of index to be true. So change the value [382:46] to true. Now again run the program. So [382:48] further it will write the data to our [382:50] file. Let us check this file Excel to [382:53] CSV. And here now you can see that the [382:56] labels 0 1 and 2 are also added in our [382:59] CSV file. Let us close this file. Now [383:02] next suppose we want to read the CSV [383:05] file and for the reference one CSV file [383:08] is created with the name sample data and [383:11] it is having the multiple number of rows [383:13] having the columns name age and [383:15] department. So let us read this file [383:17] using these pandas. So for that we need [383:20] to type pd dot read csv and the file [383:24] path which we want to read. So here our [383:27] file is sample data dot csv. So this [383:30] file data will be returned to the [383:33] variable name df2 that is our data frame [383:36] and then the data frame is printed. So [383:38] let us again run the program. So here [383:41] you can see that the CSV data is [383:43] returned to the data frame and all the [383:45] datas are printed with the column name [383:47] and the index values. Here we have not [383:50] defined any index values. Then this [383:52] function will consider the default value [383:54] to be true. Now again we want to write [383:56] this CSV to another CSV file. So for [383:59] that we need to type df2 dot2 csv within [384:03] its argument the file name where we want [384:06] to store our CSV file then index equal [384:09] to true separator is vertical line and [384:12] the header is equal to false. It means [384:15] it will assign the index values but the [384:17] column name will not be assigned and [384:20] then one printing statement is provided. [384:22] So again let us run the program. Now [384:24] check the current working directory and [384:27] here you can see that one file is [384:29] created with the name CSV_copy [384:32] and once we open this you can see that [384:34] the index labels are provided all the [384:37] remaining datas are also provided but [384:39] the headers are missing as we have [384:42] provided the header value to be false [384:44] and you can see that the separator [384:46] provided between each data is the [384:48] vertical line. This vertical line is [384:51] known as the pipe as a separator here. [384:53] Here keep in mind that let us close this [384:56] CSV file here. Keep in mind that index [384:59] equal to true includes the row index in [385:01] the output CSV file. Whereas header [385:04] equal to false excludes the column [385:06] headers from the output CSV file. So [385:08] this process is crucial for industries [385:11] where data is collected from multiple [385:13] sources such as material testing labs, [385:15] manufacturing plants or the automo and [385:17] aerospace industries. By efficiently [385:20] reading, processing and writing these [385:22] data sets, engineers and analyst can [385:25] extract insights, optimize material [385:27] selection and improve manufacturing [385:29] processes. So today we covered the [385:32] essential skill for handling Excel and [385:34] CSV files in pendas. Mastering these [385:36] techniques will enable you to work with [385:39] structured data sets efficiently. [385:41] Whether you are dealing with [385:42] experimental results, manufacturing data [385:45] or material analysis in mechanical [385:47] engineering. I hope you got the idea how [385:50] to read and write data to Excel and CSV [385:53] file using these inbuilt [385:55] functionalities. Looking forward to see [385:56] you all in our further session. Till [385:58] then, thank you. Today we will discuss [386:00] about one of the most important aspect [386:03] in pandas that is pivot and pivot table. [386:06] These techniques are crucial for [386:07] cleaning and structuring data for [386:09] efficient analysis. Pivoting data helps [386:12] in reconstructing data for read for [386:14] better readability and analysis. Now [386:17] let's move on to the pivot function in [386:18] pandas. So this pivoting is useful for [386:22] reconstructing a data frame by setting [386:24] columns as a new index levels. So this [386:27] pivot function recepts data based on [386:30] unique values of columns. So the syntax [386:32] of p is we will use p within its [386:35] argument we can pass index, columns, [386:38] comm, values. We can define the column [386:41] to use as a row labels. In index we can [386:44] define a new columns which we want to [386:46] use as a new columns in our potted data. [386:50] And to define the values again we can [386:52] define different columns to use as a [386:55] filling data using the third argument [386:58] values. Let's see an example where we [387:00] pivot a data set with certain data. So [387:03] let us go to VS code. Here for reference [387:06] data in the format of dictionary is [387:08] provided the key is date and values are [387:10] provided in the format of list. Same way [387:13] city and sales data are provided. Then [387:16] original data frame is displayed using [387:18] the inbuilt function. Now suppose we [387:20] want to pivot the data in which we want [387:22] to define the date as an index, city as [387:26] in columns and sales as in different [387:29] values. So for that we need to type df.p [387:32] P index equal to label date, comma, [387:36] columns equal to label city and values [387:39] equal to label cells and then this ped [387:43] data frame is printed. So let us run the [387:45] program to check the output. So here you [387:47] can see that the original data frame is [387:50] provided with the columns date, city and [387:52] sales and the common index levels are [387:55] provided. Now the data frame is pivoted [387:57] using these date as an index then city [388:00] as in columns. So city being Delhi and [388:03] Mumbai and the sales data being the [388:06] values. So here these entire original [388:08] data frame is resaved using these new [388:12] index column and values data. So using [388:14] this p concept one can resave the [388:17] existing data frame based on its [388:19] requirement to collect certain [388:21] informations. Let us again go back to [388:23] presentation. So here the p function [388:25] converts a long format data frame into a [388:28] wide format. Next is the pivot table and [388:31] it helps to summarize large data sets [388:33] into a meaningful tables. They allow [388:35] aggregation of data using functions like [388:38] sum, mean, mode, median and count. They [388:41] support multi-indexing making complex [388:44] data easier to manage. A pivot table in [388:46] pandas is a tool that resets data to [388:50] provide a summarized view using a [388:51] aggregation functions. Unlike Patt, it [388:54] can handle duplicate index values and [388:57] perform calculations like sum, mean and [388:59] count. So before diving into example, [389:01] let's understand the syntax of the p [389:04] table function. So its syntax is pers [389:07] table. Within its argument, we will pass [389:10] values, index, columns, aggregate [389:13] function, fill value and margins. Values [389:16] refers to the the column to be [389:18] aggregated. Index refers to the column [389:20] that we need to use as a row labels. [389:22] Columns refers to the column to create a [389:26] new columns from the unique values. [389:28] Aggregate function is applied for the [389:30] aggregation and the default value is [389:32] always mean. Then field value is used to [389:35] replace the missing data and the margins [389:37] if true it adds row and column totals. [389:41] To understand the p table let us again [389:43] go to VS code and consider one example. [389:45] So here one example is provided data set [389:49] of sales records is provided in the [389:52] dictionary format then it is converted [389:53] to the data frame and these original [389:56] data frame is printed. To check that let [389:58] us run the program. So here default [390:00] labels are provided then columns are [390:03] product region sales and profit. Now [390:05] suppose one want to create a pivot table [390:08] with the values being sales and profit [390:10] index being the product columns being [390:13] the region and we want to use the [390:15] aggregate function sum. So to use that [390:18] we need to type df dot pivot table [390:21] values equal to represent the multiple [390:24] values we will provide it within the [390:26] list. So the labels are sales comma [390:28] profit index is product columns are [390:31] region and the aggregate function is [390:33] sum. So now new pword table will be [390:36] created based on these provided details [390:38] and this updated pword table is printed [390:40] over here. So let us run the program to [390:43] check the output. Here data frame is [390:46] resaved with the labels being the [390:49] product that is laptop and tablet. Then [390:52] the columns are region that is east and [390:55] west and for both these region the [390:58] values for sales and profits are [391:01] represented in this data frame. Next [391:04] suppose from the data frame we want to [391:06] show the total number of cells. So for [391:09] that we need to use the pword table and [391:11] we need to type df dot pword table [391:15] values being the cells index being the [391:18] product columns equal to region and the [391:20] aggregate function we will consider to [391:22] be count then the updated data frame [391:26] will be printed. So let us run the [391:28] program. Here one can see that the [391:30] products are laptop, phone and tablet [391:32] and the regions are east and west. From [391:35] these above original data frame it can [391:38] be seen that total number of sales of [391:40] laptop in the east region being only one [391:44] entry. So it counts the one. Same way [391:46] from this multiple row it can be seen [391:49] that in the east and west region the [391:52] cell for laptop, tablet and phone [391:54] represents the only one entry. So for [391:58] each entry we are getting the count to [392:00] be one. Next let us take an example to [392:03] understand the multi-index pivot table. [392:05] So we can create multi-index pivot table [392:08] by using multiple columns for index and [392:11] columns. So suppose we want to have the [392:13] values being cells then index being [392:16] product and region and the aggregate [392:18] function being sum. So for that we need [392:20] to type df.p P table values are cells [392:24] index within list product and region and [392:27] the aggregate function sum and then [392:29] updated multi-index p table is printed. [392:32] So let us run the program. Here this p [392:35] table now has a hierarchical index [392:38] displaying both product and region [392:40] together. Here you can see the index is [392:43] product with these region is connected [392:46] and then for this combination number of [392:48] sales are provided. Now in real world [392:51] data sets we may have missing values and [392:54] to fill that missing values the field [392:57] value parameter helps replace the no [392:59] data with the meaningful values. To [393:01] understand that let us consider this [393:03] last example. So we need to type df.tp [393:07] table values equal to cells index equal [393:09] to product columns equal to region [393:12] aggregate function equal to sum and fill [393:15] value equal to zero. So if any data is [393:18] missing it will be replaced with zero. [393:20] So let us run the program here. One can [393:23] see that all the values are present [393:25] within this data frame. So no zeros are [393:27] mentioned with this. I hope you got the [393:30] idea how to use p and p table within [393:33] pandas. Now let's compare p and p table [393:36] to understand their difference. In p [393:38] [snorts] there is no aggregation [393:40] function is provided whereas in the p [393:42] table we get the aggregation support. So [393:45] we can perform sum, mean, count, median [393:47] and many different aggregation [393:49] operations. Pword cannot handle the [393:51] duplicate values whereas Pword table can [393:54] handle the duplicate values. Means it [393:56] will remove all the duplicate data while [393:58] framing the new data frame. P only [394:00] supports the single level index whereas [394:02] P table also supports the multi-index [394:05] level. So the major important point is P [394:08] will fail if duplicate value exist [394:11] whereas P table will handle them using [394:13] aggregation. So in today's lecture we [394:16] explored how p and p table work in [394:19] pandas. We learned how to use p table [394:22] and p for summarizing data applying [394:25] aggregation functions handling missing [394:27] values and creating multi-index tables. [394:30] Some of the key points that we need to [394:32] keep in mind are we need to use the [394:34] pword table when dealing with duplicate [394:36] values and aggregation. Use multi-index [394:38] for handling complex hierarchical data [394:40] and use fill underscore value to replace [394:43] missing data for better readability. So [394:45] that's it for today. Keep practicing and [394:48] I will see you in the next lecture. [394:50] Hello everyone. Today we will learn how [394:52] to clean and analyze data set using [394:54] pandas and this we will understand using [394:58] example in context of mechanical [394:59] engineering. So in the aerospace [395:02] industry, selecting materials for [395:04] aircraft components is a critical [395:05] decision that depends on factors like [395:08] density, melting point, tensile [395:10] strength, hardness, and cost. However, [395:12] before making any decisions, we must [395:15] clean the data set to handle missing [395:17] values, duplicates, and inconsistencies. [395:20] We will perform data analysis to [395:22] summarize and [clears throat] filter the [395:23] data effectively. So let's go through [395:25] the code step by step and understand its [395:28] expected output at each stage. So here [395:31] some of the data are provided for the [395:34] aerospace industry and this data is [395:37] provided in the Excel file named [395:39] aircraft materials and this file has [395:43] data as shown over here. So it has [395:46] multiple materials with the different [395:48] properties such as density, melting [395:50] point, tensile strength, hardness and [395:52] cost. So to access this data first we [395:55] need to load the data set and here this [395:59] first line pd [396:02] excel within its argument file location [396:04] is provided. File is available in the [396:07] same working directory. So only file [396:09] name with its extension is provided. [396:12] This line loads the data set from Excel [396:14] file named aircraft materials and it [396:17] loads it into pandas data frame called [396:20] df. If the file is loaded successfully, [396:23] there won't be any visible output. But [396:25] if the file is missing, you will see [396:27] some error. So ensure that the file is [396:29] in the correct location. Now next, [396:32] suppose we want to display the first [396:34] five rows of the data set. So for that, [396:37] we need to use the function df. So this [396:41] command prints the first five rows of [396:43] the data set to get an initial look at [396:45] the data. Now to check that, let us run [396:47] the program. As you can see first five [396:50] rows of the data set is loaded over here [396:52] with the index starting from 0 to four. [396:55] Here you can observe that melting point [396:57] has missing value none at these row four [397:01] and this we will handle later. The data [397:03] set consist of the materials with their [397:05] density, melting point, tensile [397:07] strength, hardness and cost. Next [397:09] suppose we want to get the data set [397:12] information. For that we need to type [397:14] df.info. [397:16] So this command gives you an overview of [397:18] the data type and the missing values. [397:21] Let us run the program. Here we can see [397:23] that the total entries are 10. Index [397:25] positions are 0 to 9. Data columns are [397:28] six and here at the melting point we are [397:31] getting nine nonnull values. That means [397:34] there is one missing value. And for [397:36] remaining data all other columns are [397:38] fully populated. Next suppose we want to [397:40] generate a summary statistics. So for [397:42] that we need to type df dot describe. So [397:46] this command provides the statistical [397:48] insights about the data. Let us run the [397:51] program. So here you can observe that [397:53] this summary statistic provides the [397:55] count mean standard deviation the [397:58] percentile at 25 50 and 75 percentage [398:01] and the max value and the minimum value [398:04] for this given data set. So from this [398:06] data one can check that the mean density [398:09] is 4.8 g per cm cube. Next tensil [398:12] strength varies maybe from 90 to 3,500 [398:16] megapascal and the cost ranges from 5 to [398:20] 45. So from this data many can get the [398:24] insights about the data provided. Now as [398:26] we have observed it has some missing [398:28] values. Now to check that missing values [398:31] we may use the inbuilt functionality is [398:34] null. So for that we need to type df dot [398:37] isnull dot sum. So it gives the data [398:40] about how many elements are missing from [398:43] these different columns. So let us run [398:45] the program. Here one can observe that [398:47] melting point has one missing value. For [398:50] remaining all columns we are having the [398:52] zero missing values. Now to remove the [398:55] rows with missing values we need to use [398:57] the inbuilt functionality drop n. So [399:00] this function removes the rows with the [399:02] missing data and it will reduce the [399:04] number of rows. Let us run the program. [399:06] So here one can see that row with the [399:08] index position three is deleted from [399:10] this given data set. But here the index [399:13] positions are 0 1 2 and then it jumped [399:16] to four. Now if one want to have the [399:19] consistent index then we need to pass [399:21] the argument that is in place equal to [399:24] two. So it will update the index [399:26] position and it will start from three [399:28] onwards up to the 8. Now next to fill [399:31] the missing values in the melting point [399:32] with the columns mean we need to type df [399:35] dot fill na within its argument we will [399:38] pass the column melting point. So here [399:41] we have passed df within its list label [399:45] melting point dot mean. So it will pass [399:47] the mean value from this melting point [399:50] column to these empty data available in [399:53] the excel. So let us run the program. So [399:55] here one can see that the melting point [399:58] is missing for the material carbon [400:01] fiber. Now once we run the fill na [400:04] method to fill the data we can see that [400:06] the carbon fiber is having the melting [400:08] point and that is nothing but the mean [400:10] value from this given column. Next to [400:13] detect the duplicate from this given [400:15] data set we need to type df do.d [400:17] duplicated dot sum. So it will give the [400:20] count that how many rows are duplicated. [400:23] Let us run the program. So here you will [400:25] see that the number of duplicate rows [400:27] are zero because within our data there [400:29] is no duplication available within this [400:31] data set. If any duplicate rows are [400:34] present to remove that duplicate rows we [400:36] need to type df dot drop duplicates and [400:40] it will remove duplicate rows from the [400:43] data set and it will keep only the first [400:45] occurrence. [clears throat] [400:46] Let us run the program. So here data set [400:49] will remain same as there are no [400:50] duplication so it will not remove any [400:53] rows. Now suppose we want to count the [400:55] unique material occurrence then we need [400:57] to type df within its argument label [401:00] material dot value counts and let us run [401:04] the program. So here you can see that [401:07] the unique count for all these materials [401:09] are printed and all materials are [401:11] unique. There are no duplicates present. [401:13] So we are getting the value one for each [401:16] material. So in short this tells us how [401:18] many times each material appears in the [401:21] data set. Next suppose we want to filter [401:23] the data we want to select the material [401:25] with tensile strength greater than 500 [401:27] megapascal then we need to type df.log [401:31] within its argument df within its list [401:34] data we will pass the label tensile [401:37] strength and then we will pass the [401:39] boolean operation that is greater than [401:41] 500. So it will select all the rows [401:44] having the tensile strength value [401:45] greater than 500. Let us run the [401:47] program. So here one can observe that [401:50] all the rows are filtered having the [401:52] tensile strength value greater than 500. [401:54] Next suppose we want to retrieve the [401:56] first three rows having specific column [401:59] then we need to type df doiloc within [402:02] its argument column 3a as a list [402:07] provided 0a 1 comma 2. It means it will [402:10] select the rows starting from 0 up to 3 [402:14] and it will select the columns from 0 to [402:16] 2. Let us run the program to check the [402:18] output. So here one can observe that the [402:21] rows selected are 0 1 2 3 is excluded [402:25] and the columns selected are 0 1 and 2. [402:28] So this extracts the material density [402:31] and melting point data from our given [402:33] data set. Now next if you want to filter [402:36] the materials having density less than [402:38] five and cost less than 15. So we need [402:41] to type filter materials equal to DF dot [402:43] query within its argument we will pass [402:46] our condition that is density less than [402:49] five and cost less than 15. Here keep in [402:53] mind we need to provide the same column [402:55] names as we have mentioned in the data [402:57] set. So these filters lightweight and [403:00] cost effective materials like [403:01] [clears throat] aluminium and magnesium. [403:03] So let us run the program. It has [403:05] printed two materials aluminium and [403:07] magnesium. So with this example we have [403:10] cleaned missing values and duplicates. [403:12] We summarized material properties. We [403:15] filtered high strength and cost [403:16] effective materials. So these techniques [403:19] help aerospace engineers in material [403:21] selection and [clears throat] defining [403:23] many of the important data from this [403:26] data set. For the example purpose we [403:28] have only selected 10 to 11 materials. [403:31] But in your actual area of work there [403:34] may be possibility you are having the [403:35] list of materials. It may be in the [403:37] range of 1,000 to 2,000. Then again [403:39] using this concept you can get the [403:42] insight from your data set. So this [403:44] process is essential in real world [403:46] applications where engineer must rely on [403:48] accurate and well structural data to [403:50] make informed decision. By mastering [403:52] data cleaning and exploratory analysis, [403:55] you can work efficiently with large data [403:57] sets, identify patterns and gain [404:00] meaningful insights. I encourage you all [404:02] to practice these techniques with [404:04] different data sets and try applying [404:06] them to real world engineering problems. [404:09] I hope this session is insightful for [404:11] you. Looking forward to see you all in [404:13] our further session. Hello everyone. [404:15] Today we will discuss Jet GPD for data [404:18] analysis using pandas. It is an exciting [404:20] and highly useful topic for anyone [404:23] working with data. In this session, we [404:25] will explore how Jet GPD can assist in [404:27] performing exploratory data analysis and [404:30] data cleaning using the pandas library [404:32] in Python. Data analysis is an essential [404:35] step in making informed decision whether [404:37] in engineering, finance, healthcare or [404:39] any datadriven industry. However, data [404:42] sets often contain missing values, [404:44] duplicates, inconsistencies and other [404:47] challenges that must be addressed before [404:50] meaningful insights can be extracted. [404:52] This is where JGPD becomes a valuable [404:54] assistant guiding you through the data [404:57] cleaning, transformation and [404:59] visualization. The pandas library in [405:01] Python provides the powerful tools for [405:03] loading, cleaning, analyzing and [405:05] visualizing data and how to utilize that [405:08] we have already understood in our [405:09] previous session. So now let's explore [405:11] how CGP can help automate this task [405:14] making data analysis faster and more [405:16] efficient. Now to understand the pendas [405:19] for data analysis you may ask the chat [405:21] GPT or provide the prompt and some of [405:23] the important prompts are listed over [405:25] here. Now here you can see that some of [405:27] the important prompts that you may ask [405:29] that is the first one is how to install [405:31] and import pandas in Python. So if you [405:34] are a beginner you are not having any [405:36] idea. So you may start asking the [405:38] question from this first one and in [405:40] similar manner you may ask for the [405:42] different questions. Let us go to check [405:44] GPT to understand how it will provide [405:46] the answer. Now suppose first we want to [405:49] understand how to install and import [405:52] pandas. So for that we may ask to jet [405:54] GPT how to install and import pandas in [405:59] VS code. [406:11] So here you will see step-by-step [406:13] information is provided by the Jet GPT [406:16] and if you follow this information you [406:18] will be ready to use pandas in your [406:21] programming. Now next you can ask how to [406:23] structure a data [clears throat] set in [406:25] the pandas. So you may type how can I [406:28] check the structure of my data set. [406:43] So here you can see that different [406:45] functions and its use are provided by [406:48] the Jet GPT and you may make use of this [406:51] direct code in your program. So some of [406:53] the important functions we have already [406:55] understood in our previous session. [406:58] Other than this there are many different [406:59] functions are available that you may [407:01] explore using these concept of GPT. Now [407:04] suppose in your data set you are having [407:06] the missing values. So you can also ask [407:09] the chat GPT how to manage that missing [407:11] values. So you may type how can I handle [407:15] missing values in pendas. So here chat [407:18] GPT will suggest methods and best [407:20] practices based on the nature of your [407:22] data. Same way based on data you may ask [407:25] how do I remove duplicates from my data [407:28] set. So for that again it will guide you [407:30] with step-by-step instructions [407:32] explaining when to use subset parameters [407:35] and how to handle large data sets [407:37] efficiently. Now if you are having the [407:40] large data set there is also possibility [407:42] that you may ask that how you can [407:45] summarize the categorical data using the [407:48] pandas. So let us type how do I [407:51] summarize categorical data in pandas. So [407:55] here Jet GBT will provide code snippets [407:58] and alternative approach like pivot [408:01] tables and cross tabs and how to utilize [408:03] them in our program. So before engineer [408:06] makes any decision they must clean and [408:09] analyze this data set. Jet GPT helps [408:12] streamline this process by suggesting [408:14] how to remove missing values, how to [408:16] filter materials and how to detect [408:18] outliers in the data. By using Jet GPT [408:22] alongside pandas we save time, improve [408:24] accuracy and enhance efficiency in [408:27] decision making. So here you can observe [408:30] that there are lots of different [408:32] functions and their usage are provided [408:34] by the Jet GPT. So in this session we [408:37] explored how Jet GPT can assist in data [408:40] analysis using pendas by automating task [408:43] like data inspection, cleaning, [408:45] summarization and filtering. Mastering [408:47] these techniques will help you work more [408:49] efficiently with data, extract valuable [408:52] insights and make better decisions in [408:54] any industry. I encourage you all to [408:56] practice these techniques using real [408:58] world data sets and explore how KGPD can [409:01] assist in solving complex problems. I [409:04] hope you got the clarity how this pandas [409:06] library is useful in real world [409:09] scenarios and in the mechanical [409:10] engineering