Python for Beginners: Learn Programming Step-by-Step with Real-World Projects

1. Introduction: Hello, and welcome in this tutorial, we're going to learn Python basics, and I'm Manjanat your instructor for this course. Here are the learning objectives. Please make sure you have the programming paradigm of choice opened alongside while watching this tutorial and your learning is guaranteed. So we shall look at and understand how do we make use of comments and use indentation in Python. We shall also understand the ideas of identifiers and keywords in Python. We shall look at for a good measure Python types and various categories of data types Python supports. We shall also deal with basic types in Python. We shall spend some time understanding variable type and the assignment in Python. 2. Comments & Indentation: Welcome to Jupiter Notebook, the perfect tool for writing running Python code interactively. So what I'm going to do is I go to this new option and select Python three IP kernel. And by default, the Jupiter Notebook is named untitled. I shall click on this and let's give a meaningful name for this Jupiter notebook. I want to call it as Python basics. Notice dot IPYNB extension, which means it's a interactive Python notebook. I shall rename that. And we are good to go. This is how the interface would look like, and we shall cover all of the items that I listed under learning objectives and go in a hands on mode for learning and making sure we learn it in a systematic fashion. As you can see here, we have cells these let you execute code in these cells. You can see instant outputs and document your work with markdown. You can see your options for code, markdown and raw. Code is what you are going to write and execute, markdown and raw is more for documentation and for generating literature. Ideal for data science and experimentation, the Jupiter notebook, we can test idea step by step without re running everything. Each cell runs independently, making deugin and exploration seamless. We will dive in and start coding. So as I mentioned, first let us understand how do we implement comments and indentation in Python. I shall put that comment and indentation. You must have noticed I have prefixed this statement with the asterisk symbol. Comment always begin with asterisk in Python. If I execute line of code, needless to say, nothing happens because Python recognizes this to be a comment. This is a single line comment. So how do we go about implementing multiline comments in Python? Let's say multi line comments in Python. Instead of keeping it as code and prefixing with asterisk, I could also go ahead and select as markdown and I shall execute. This is the run command. I shall ho over my mouse, run this cell, and advance. Shift Enter also, you can use if you are comfortable using the keys or using the menu driven items either way. So you can see a multiline comments in Python would look like a broad header. So if I prefix with one more Asterisk symbol, and if I execute, see what happens. So the farm size gets reduced. Nevertheless, this is how you Start using comments. So make sure it always begin with an asterisk symbol. So now let's export the multiline comments. You should write it in a pair of like this. Either you could write multi line commands using three single codes, or you can have multiple double codes like this. So that is how you're going to implement the multi lane commments in Python. Let's just look at one multi lane comment. Let's say I'm going to start with three single codes. Okay. This is a, I'm going to write it as a multi line. Let's say Enter. I'll say comment, and I shall close with three codes. Okay, this is how you're going to execute multiline comments in Python. You can see this. This is how you're going to execute a multiline comment. You could also give these things in double codes. So I shall copy this to this and replace this with three double codes at the beginning and three double codes at the end and say execute. It does exactly the same thing. This is a multi line comment is implemented in Python. And we have the other piece to learn called indentation. Let's just look at understanding indentation. This is important to understand. So please be attentive. In Python, you cannot be casual about using indentation. What I mean is this? Let's say I'm going to assign value 20 to variable called X. And say enter, and let's say another variable Y is assigned. Let's say 60 it. And you know what? If I put my cursor here pointing to Y and then tire and I execute, see what happens. So this is expected. The error is expected. What I mean by that is, you cannot use casualty indentation in Python. Make sure not to use indentation. Casually, let's get this spelling right in Python. One other thing. This is called grammatic or semantic error. It specifically tells the category at sorry, indentation error. It greatly gives the meaningful information called unexpected indent. So how do we rectify? First thing is to recognize the error. Second thing is to learn to rectify. That's where the learning is. All I'll do is I'll make sure it is properly indented and I'm not going to use indentation casually. If I execute this it will not give an error. I can print it by saying X comma Y and execute. You will see 20 comma six tetas, the output. This is the expected behavior. Hope everybody is clear. 3. Identifiers: Let's continue, and now we shall learn something called identifiers and keywords, an important topic in Python that you must understand. Okay, so I'll just leave that comment. Let's understand. First up, I'm going to put them as comments, okay? Or I'll show you how to use raw. I'm going to put a iPhone and then leave some space. I'm going to write it. Python is a case sensitive language. Most important to understand, don't miss this idea. Python is a gas sensitive language. What I mean by identified in a Python is simply a name that is used used to identify a variable function. A class. I could also be let's say, a module or any other object in Python. So this is what I meant when I said identifier. I shall repeat. When I say identifier, I'm talking about a Python identifier which is nothing but a name used to identify a variable. Okay, let me just get the spelling mistakes were corrected. Variable, function, class module or any other objects in Python. That's what I mean by identifiers. I'm going to use the option a, again, from the dropdown and say, exude, see what happens. It gives you raw information depicted in the notebook. So this is equivalent of a comment, but it is good to use this as a best practice to generate the Python code along with the comments embedded so that it increases the not just the readability. It brings in clarity, and subsequently it brings in ease of maintenance. That's one other important idea of writing Python code. It must be easy to maintain. Turns out for writing identifiers, there are some rules. So let's call it out. Rules for creating identifiers in Python. This is, again, another important dimense. Let's say I'm going to select it as martyr and leave it there as a header. You must always start with a letter of an alphabet. Okay? So start always with a letter of an alphabet. Okay? Start with any letter in the alphabet. There is an exception. You can start with an underscore. Okay. The underscore symbol. So for creating the identifiers in Python, the identifier, if it's a variable, for example, must always start with a letter of an alphabet or an underscore. Then subsequently, you could have mix of multiple things. You could follow it by more letters, for example, more letters or even zero. Even zero, like I already mentioned, you could also use the underscore and you could also make use of digits. Let us understand this with an exam, and see what happens if I don't start with a letter or an underscore. Let's say, I'm going to start with eight were one. If I exude this, you will see that it gives syntax or invalid decimal literal. But if I simply put one, what happens? Let's check it out. There is nothing happening. It's not defined, it says, but it is not a problem with the variable. Naming or the identifier itself. So if I execute this, everything is fine. Okay? So let's go back. And let's say instead of starting with a letter of an alphabet, I would use just an iPhone and say, VAR one, see what happens. So it gives you error. It clearly says name error. Name one is not defined. Okay? So you cannot use a digit or you cannot use a special character like IP. Can I use a star? Let's see what happens? No, CT's star expression here is the message. I mentioned, if you notice here, underscore is the only exception of using some special character. So if I use underscore are one and say is equal to 103 and exit this, turns out it works fine. What does it tell you? For creating identifiers, you have to follow some rules. Start always preferably with a letter of an alphabet or an underscore. Here is an example. You can follow it by more zeros, I could give like this, it'll work or I could give variable one, some XY z. This will also work. I could remove the underscore. This will also work. But you know what? When you are having words as part of the variable name. Let's say this is variable one, underscore salary. If a variable is bolding some salary and you want to name, it has one underscore R. Make sure to always separate the words with an underscore. This will work well and it brings in a lot of readability. Okay? Supposing you're going to use keywords, for example, what are the keywords? We'll talk more. Think of this way. Int, if I write, see the color changes. If I write care, what happens? Nothing happen. So what I'm telling is something like this. Let's say I'm going to say math. Anything happens, nothing happens. See, for example, Abritton Int, okay? Check something else. Se falls, turned green. Okay? If I say from, it turns green. What I'm saying is, these are all the keywords in Python. Okay? These are all the keywords in Vital. So another bit of information is all keywords, if you notice, there are many more. I've written few. All keywords, if you notice, like in falls from. You could write, for example, if there could be many, we can talk about it, not, et cetera. They are all in lower case. So another thing to understand is that from this, let's draft a comment here for reference. All the keywords. In Python or lower case. Hope this is clear. These things, if you follow, you will have the basic understanding of Python really well. Yeah, I'm going to save this and we shall continue. 4. Keywords: Now let's continue to understand more about keywords. As you can notice here, It falls from if not, et cetera, keywords in Python. Like I already mentioned, all the keywords are in lowercase. Some of you may be curious, right? You want to know a list of keywords available in Python. Is there a way to see the list? Yes, let us print the list of keywords, print the list of keywords. This is interesting, okay? So how do we do that? We learn a few other things by looking at this implementation. There is a module called keyword that is loaded into runtime already, but you need to import that module called keyword itself, okay? You have to use the keyword called Import. Notice here, Import is written in green or depicted in green rather, meaning it's a keyword as well. So one other word of caution when you see keywords like this, int falls from if not, please avoid using these keywords for variable names. I don't want you to experiment this, but I want you to be aware of this. If I say It is equal to ten, unfortunately, unfortunately in Python, it doesn't throw up any error saying you're using keyword for a variable name, but it messes up the default behavior of this variable called It. It messes up big time. So don't even experiment by using the keywords for variable names. Don't make that cardinal mistake. A word of caution there from a practitioner. Okay? So Ipot is a keyword. Leave a space. You can type keyword, and this is the module. In Python, which will help you to list the set of keywords. Keyword is the module. What we're doing with this executable line of executable line of code called import keyword is to be able to import the functionality of this module at runtime in Python. I shall execute. Yeah, there are no errors. Then I'll use this keyword, copy this and put it dot. There is something called KW list, which gives me keyword list. So I shall print them. So I shall put it in parenthesis and use the function called print and execute. So you can see here, these are all the keywords available in Python. These are all the list of keywords like false, none, true, and as ast. Is Avid, Bray, class. There are many Okay, can count it and see how many it's after all, a list now, so you can see the length and figure out. So I want you to tell me without counting the number of keywords using functionality, built in functionality, I want you to tell me what is the total number of keywords in Python. Okay? This is how you can list the keywords. We will spend more time on the built in libraries and the built in modules in Python a little later. I think this would suffice for now how to understand to print the list of keywords because this is important. So one more time, a word of caution if you like. Make sure none of these keywords listed here, you're going to make use for variable names, okay? Otherwise, it'll mess up the depot behavior of those keywords in the Python environment. Yeah. 5. Python Types: Now, let us focus on learning different Python types. Another interesting topic to learn. Maybe I'll make this into a Marton and create it as a header. Python basically supports three categories, okay? Supports. Very important to understand this supports three categories. Okay. Three categories of data types. What are those? Let's understand. First is called basic types, and the second one is called container types. Last but not the least, the third is called usual five types. We shall ex prime on each one of them and also see a bit of a code in action. Basic types, you can see here some of these like Id, okay? Like Id. Float. We'll just talk more on these things. For example, even complex. Quite often we don't use this datatype, but it's interesting string. You could think of bool, right? You could also understand something called bytes, which we'll talk later. So these are the basic types. One of the categories of datatypes as basic types, Itch in float complex string bool bytes fall into. Okay? Then the other category of data type is called contener You will have the popular data structure call list. You will have tuple you will have set and you will have dictionary. Then they use the different types is simply put it is user defined. You could think of them as let's say classes in Python. So broadly, just to summarize, Python supports three broad categories of data types. First one is called basic types into which data types like int, float, complex, string, bull, bytes, et cetera fall into. Second important category is column container type, and very, very important category at that, which includes list, sample, set dictionary, and the third category is user defined type. Typically I classes to this. Okay. So we're going to talk a lot about the basic types in greater details, and we should also spend some time to understand the container types. And, of course, we'll also look at a little bit later in this tutorial, the user defined types as well. So first, let us now explore the basic types as one of the data types the Python suppose. We'll do that now. 6. Basic Types: Let us now explore basic types as one of the categories that Python supports using some examples. First thing is to recognize these basic types can be expressed as binary can be a decimal, and therefore can also be exa decimal and can even be octal representation of the data. Okay? For example, if you have binary, it typically starts with something like this. So this is for binary. If you have, let's say, odcimal, then you will have something like this. Okay. This is exodimal representation. Then you will have octave which will be something like this. This is octal representation. Okay, that means binary starts with, let's say zero, small letter B upon zero captleter B, and octal could be with zero with small leterO upon zero with captleterO and Xa could be with zero small letter X upon zero captleter X. Okay? You could also have, for example, here something like this. Let me just write a few more examples and see if it works. Let's say, we'll take it as zero, zero, one, one, one would be another example. Okay. Then you could also have integers, which is simply, let's say, one, seven, eight. Okay. You could have various other representations. I think I'm going to leave it at this that will help you to understand a few things clearly. Okay. All of them, the examples here is integer representations. When it comes to float, let's look at float as one of the basic types. Float is typically expressed as a fraction or even in exponential form. You could express as a fraction. Or even in exponential form. So if you look at some example, then you will understand. Let's say X is equal to, I'm going to say, one, two, 3.456 and turns out if I print X, it'll work. And if I check for the type of X, use the keyword and say type of X. And you will see this is a float representation. Okay? So this is a positive floating point representation. So I could also take, let's say, for example, let's call the variable name X one, and I could prefix with minus sine, and it shall work with the negative floating point representation. It turns out this is also float. The value std is minus one, two, three, 0.456 in the variable called X one. So this is floating point representation that we typically represent. I also mentioned you can represent this as a fraction or exponential form. Let us say pile, you know how to represent, right? It is 3.14 16, et cetera. And I can represent this as expensalFm. Let's call this as X three and see if it works in this exponentiation form. Yes, it does. Let us check this type of X three. So this is a float. So this makes it amply clear you could have float represented in exponential form as well. Okay. And it turns out, I could also represent this exponential representation or exponential representation. Even if I write here E, it should still work. Let me just show you by executing this goal. So here, if I print this X four, you can see it is 313.16, and this is the exponentiation form. Okay? So we have talked about this basic types can be represented in various ways. It could be binary when it has, for example, different ways of representation for a data type or a basic type like decimal or decimal octal like this. Float would be expressed as a fraction or even in exponential form. So you see here, it is represented in decimal representation because it's float. Here it is represented using exponential form. Notice here, I've used the letter smallly for exponential form. I've used the letter capitally for exponential form. Then we set another basic type would be complex. Let us explore complex data type. So one more type already Itt, we quite often don't use in our data science or analytics endeavors. She could have this complex pipe like this. Typically, this is how the complex representation would be. I'm going to store the two value in a variable called Xpi and let's say four plus ten J. Okay? And I'm going to print xi, and I shall also print rather the type. So it turns out it is complex. So it's by convention. It is small letter J is used. Anything other than this, if we use, you will get the error. I'll just show you. Even if I use J, let's explore. Okay? What happens? It works. Anything other than J, let's say I'm going to use K, you will get error. I'll use the small letter K. You'll get the error. So please remember only capital or small letter J is allowed for complex numbers. I'll leave it as part of the recording. This is not because error. It's not because it is not remediated. It is by design, and this is expected. To demonstrate, you will not be able to use anything other than letter J, either smaller capture letter for representing complex data type. Hope this is clear. So what this has is both real and imaginary part. Four is real and ten J is the imaginary part. So that's what a complex number is. So more write it here. Complex will have we'll have real and imagine the real part. You need not have to worry too much about this because it can be rest assured. As a practitioner, I'm saying, very rarely we use complex numbers in typical data science or business analytics or data mining endeavors. Okay? Let's go up and see what else we have on our plate. Yes, we have string, we have bool, we have bytes. Let's explore for the basic types. Let's go one after the other. So let's explore string. So we shall explore in detail later, but we'll go to the extent required for this tutorial. Okay. String is collection, basically. It is an immutable collection. Okay? What is a string? String is animutable collection. What's all it could contain? It could contain uni code. Okay, characters, which are typically enclosed within single code like this or double code, or you could also have triple codes like this. Any way you represent, it's all recognized as a string in Python environment. So please understand this. String is an immutable collection. Basically contains unicode characters which are enclosed within single codes, double codes or triple codes. All these are presentation of stringed data type in Python. I'm going to put it as a markdown and execute this. Okay? So you can see some examples. I can put in a single code. This is Python, basic tutorial. Okay? Let's say, I'm going to put it as STR one is equal to this, and I'm going to print this STR one, and I'll also check for this type so that you will see both. Okay, I can see her data type is string, and it prints exactly what is given within single code. This is Python basic tutorial. Turns out I've just represented the string using single codes. I could do the same thing using what I call tea within double codes. Let's say I will replace the single code with a double code. Probably, we can overwrite the same variable, but just to make sure you are quite clear, I'm going to change the variable name to another variable name called string two Walla, exactly same output, but the data type is string. You would recognize by this, it can be done using double codes too. One last variation to this, let's look at this using triple codes and see if it works. As in the previous example, let's make this into three and see what happens. Any guesses, what happens? We can pause the video and execute this, then see in the video, what happens? You know what? I'll not keep you waiting. Sh shall run this. Yes. So this amply demonstrates string could be represented within single codes using double codes, using triple codes. So if I wonder, when do we use double codes versus single code? If I have to use apostrophe representation. Okay? This is a basic tutorial. Let's take this. For example, let's take this. This is, let's say, I'm going to put it deliberately unja unjnas Python basic tutorial. Notice here, within two single quotes, we also have a single quote as part of the string. If I execute this, it will get hell. That's because what in its wisdom Python thing says up to this is the string. After that, it doesn't recognize what it is. It simply says unterminated string literal. So whenever you want to use some representation of a string, which has single apostrophe inside of the string representation, make sure not to go with single quote representation. You shall go with double quote representation like this, and everything will be hunky try. Hope that's clear to everybody. Yeah. 7. Boolean Data Type: Now let's explore. Bool. Okay. Let's see how to represent. What is the variable count here? Okay, X six we can take. We can take any variable name and write that's not an issue. Xix. Let's say I'm bound to say true. Will this work? Let's say this. No, this is the expected behavior. Name true, not defined. So it has to be represented as a keyword. The moment I spell it correctly, as in the casing, T become captive letter, TRUE for the Boolean, it works. So we can check this by checking for the tip. So similarly, you could check for Boolean is typically what? True or false, right? So you could check this and probably I'll change the variable name. So you can print this and there you go. This is a basic data type called Boolean is being used for assignment and printing. Notice here, if I use the keyword type and then reference the object or the variable, it shall print category of the data type. In this case, it's a basic data type and of type Bool. Notice here, everything is implemented as class inside of Python. So if you want to check this out, if I use only type X seven, it simply says Bool. If I prefix it with print, and check this out, and let's see what happens. As I alluded to, even though it's a Boolean data type for the basic type as one of the categories, instead of Python, it's implemented as class, everything int, care, whatever, right? It's all implemented as class instead of Python. So you could use, for example, here, the type is here, so we could take and then print this. Okay? If I print this or execute this, you'll see string. But if I prefix it with print command, you'll see that also as a class called string. Hope that is clear to everybody. This is an important idea that you must remember in Python. Every data type is implemented as class instead of Python. Okay, so I hope this is clear. Any questions? Feel free, if you don't ask me. I'm happy to help you. And let's learn together in this. Yeah. 8. Integer & Float Ranges: Let's continue and we can discuss something with respect to numbers, particularly that's called integer and float ranges. And just explain what it is in a bit integer and float ranges. Okay. So it's an important idea that I want you to understand. Clearly, please follow along. Let's just say, I'm going to look at assigning variable X with value 100. Okay. I want to look at the same variable. I'll write it in the next cell. Same variable, 100, 200, 300. Okay? Or let me just assign it. So I'm going to look at in the next cell, same variable, I shall copy and add a few more digits to this. Okay, randomly, I add few more digits. So here is the fundamental question to think about when we talk about integer, okay? If I exude this and if I can check the type of this type of this. It is integer. If I execute this cell and check for the type, it is an integer. If I execute the X assigned a large value and check for the type, it is also integer. So what is the learning here? Integer can be integer. Let's put it this way. Integer can be of any arbitrary size. Hope you understand this. Okay. That simply means Python will provide Arbitrary precision for integers. Okay? You can create a large integer that is of length, whatever I'm not counting. Or you can create integer that is on the three digit length or little more than three digits and quite a number. In fact, maybe 15 digits or longer. I can count. Okay? So it doesn't have restriction on the size of the integer. Integer can be of any arbitrary size. And therefore, you can work with arbitrary precision of integers in Python. Another important idea is, you can also perform on these integers, certain arithmetic operations, right? You do not have to worry about do not have to worry about. I'll put this into the recording here. Oflown underflow, you don't have to worry. So while performing arithmetic operations, operations do not worry about overflow or underflow. This is a big advantage in Python as compared to other programming paradigms from what I've experienced. Okay? So an important idea that I want you to be comfortable with is that you do not have to worry about overflow or underflow while performing arithmetic operations of integer, which can be of any size arbitrarily speaking. I've also put something as float ranges. Let's just examine floats. Please understand this. This is an important idea. When it comes to floats, typically, it is represented as binary 64 bit double precision values. Okay? Typically, represented internally. As 64 bit binary double precision values. There is some nuance to this. I'll just explain that in a bit. Okay? This is aligned with IE standard. Okay? This is aligned with IE standard. So race is this standard you can think of float ranges like this. So based on this standard, what do you think would be the maximum value a float can take, Okay? Float can take. Okay, let me just put it into next sentence properly. Float can take. Okay? This is kind of approximation. Approximately, Okay? 1.8 times ten raised to the power. 308, if I'm not mistaken. So that means you can represent fairly large value for floating point representation. Okay? If the number is greater than this, if the number is greater than the above representation, meaning 1.8 multiplied by ten raised to the point of 308 if the number is greater than this representation. Okay, then it is considered we represent it as IN F, which means infinity. So that essentially means you could represent in Python, infinitely large values for floating point representation. That's what I meant by float ranges. Okay? Just to summarize this, integer can be of any size arbitrarily speaking, and you can see the demons station here in the form of live demo. And for floats, it is typically represented internally as a binary 64 bit double precision values as per IAE standard. Based on this standard, float can take approximately larger values which are typically to the tune of 1.8 times ten rays to the par of three not eight. If the number that is represented as a floating point representation is greater than this representation, that is one point time eight times ten rays to the p308, it is considered infinitely large number. Okay? Having said what I said, here is a small nuance. Quite often, float you will not be able to represent exactly in binary form. Floats quite often cannot be represented. Represented in binary form exactly. Okay? So there is a bit of a nuance to this. So because of this, what happens is when I have a floating point representation, internally what happens inside Python environment is that it is often an approximation of the actual value. So it is often an approximation of the actual value. Hope this is clear to everybody. When you look at as an approximation of the actual value, because quite often you cannot quite often you cannot represent in binary form. Let me just clarify this once and for all. Quite often, floats cannot be represented in binary form exactly. So what you see as a representation is oftentimes an approximation of the actual value. The difference between this represented value that is approximation of the actual value, and the real large representation of the float point representation is so small, for all practical purposes, this is quite insignificant, does not pose any challenges. Okay? Hope that's clear. 9. Variable Type & Assignment: Let's continue understanding in Python, something that's very, very important. I've called it variable type and assignment, and I'll explain the beauty of Python, how it differs from other programming languages per se. Okay? If you take, for example, C or C plus plus, or even Java, you need to define the type of the variable in advance. It could be integer, character, string, et cetera, but you have to define the variable type in advance. Whereas in Python, no need of defining the variable type. That's the beauty. Then how does Python recognize some variable of certain data type? I'm just going to talk about it. What happens inside of Python? At the time of execution, the type of the variable is inferred from the context. Okay? So at the time of the execution execution type of the variable. Is inferred from the context in which the variable is used. If this is mouthful, I shall simplify and explain this with a concrete example. Okay? What does these two tell you before we go to the examples? No need of defining the variable type. At the time of the execution, type of the variable is inferred from the context in which the variable is used. So this essentially points to what is called dynamically typed language. So Python is dynamically typo language. Okay? It simply mats this no need of defining the variable type at design time at runtime, that's the time of execution. Type of the variable is inferred from the context in which the variable is used. Okay? This is important distinctions. Visa, for example, the other paradigms of the world. Okay? So I think it's worthwhile to get these things understood in its entirety. So as I intended, let's just look at some examples. Let's say, I'm going to assign value X is equal to 100. Notice here, I've simply named the variable X and used the assignment operator with a single equal to sine. And to the right of equal to sine, we have the value 100. So this is assignment, okay? I'm assigning value 100 to X. I did not define the type of variable X. I said, at the time of execution, it is inferred. So how do we check this? So I can check this by simply typing. We've already seen this, but putting into context. So if I run this line, these two lines of code, turns out it is integer. If I can print X for you, the value of the variable is also printed. Variable holds the value 100 and it is of type integer. If I take same variable and do a different assignment, simply put, I'll say 100.6 X and see what happens. Now it has become float. What does it tell you? At the time of designing, I have not defined the variable type. At the time of execution, based on the context in which the variable was used, the variable assumes the data type. In the first instance, same variable assigned value of hundred turned out to be integer as understood by Python. In the second example, same variable assigned with the floating point representation, it has assigned float as the data type for the variable X. Just to give you another flavor of this, if I go ahead and put this hundred in codes. Okay? That's it. Then execute this. Turns out, it becomes string. So they should amply clarify things that Python is a highly dynamically typed language, and therefore, there is no need of defining the variable type. And therefore, at the time of execution, the variable is inferred from the context in which the variable is used. And also notice these instances of three examples that I alluded to would all be about simple variable assignment, meaning, I've assigned one value for one variable. So next we shall see how do we do multiple variable assignment? Hope everybody is great about variable type and assignments. Yeah? 10. Multiple Variable Assignment: Let us now understand how do we do multiple variable assignment in Python. There are two ways to look at this. One is, let's say I have variable X, variable Y, and variable Z. All of them would require a single value. Let's say all of them should be assigned 100, or let's say 200. I can do it very elegantly in Python, like this. X is equal to, sorry. X is equal to. Y is equal to Z and alla, I can print all the three and you get 200 assigned. This is multiple variable assignments. Where X, Y, and Z are multiple variables. Supposing in the previous case X was assigned 100, same variable was assigned a floating representation of 100.66 and same variable was assigned string 100. So instead of that, if I have to take X is equal to 100, let's say, and I'll make some space and say Y is equal to, let's say, 100.25 and Z is equal to, let's say, string hundred how will I do this? I can do this. If I execute, see what happens. I'll execute this. You will not be able to use it like this. This multi multiple variable assignment is a wrong way of using in Python, semantically speaking. What I can do I could use a semicolon as the separator, and therefore, if you use that way, you can print all the three variables and it works really well. Uh huh. Something really not happening. So let's see this. What I've done? X is equal to 100. Y is equal to 100.25. Z is equal to this one. Then I called out X Y and said, here you can see the mistake is here. I corrected it, and there you go. So in a single line of core, I have done multiple variable assignment. As in X is assigned integer, hundred, Y is assigned floating point of hundred 0.25, z is assigned a string of 100. So this is how it is done in Python. So we'll go ahead and explore some interesting operations arithmetically speaking, next. 11. Arithmetic Operators in Python: Let's now explore some interesting arithmetic operations in Python. Let's see a few interesting ideas of implementation. Turns out Python provides various arithmetic operators support, so to speak. Like, for example, you could use uniary operator like PAS. You could use minus. You could use star. You could also use slash. You could use percentage, and you could use double slash, and you could also use double star. If you're comfortable using these operators in the context of Python, it's good enough to do this. So let's go ahead and explore few of these operators. Okay? Let's say X is equal to four plus three, I shall print X. No points for guessing. X is seven. So this is using plus operator for addition. Let us just say I'm going to take five by two, sorry, 5/2. What do you expect? So it performs what's called true division and yields what? So let's print this. Any guesses for what is the output? Let's say this, 2.5. So this is called true division, which has resulted in 2.5 as a float representation. Okay? So this is the slash operator. Let's just look at some other example of okay? Randomly, I'll take something. By two. Instead of this, I think we've tried that. Let's use percentage here and two. What do you expect? 11/2, you will see the remainder. Let's see if I print x, we should get one. Yes, 11/2 results in remainder one. Okay? This is the percentage operator. Let's go ahead and explore this starstr operator. Let's say, I'm going to have Y is equal to two times not two times two raise to power of three. Okay? So this should give you eight. Yes. So this is exponentiation. That means two is raised to the power three. So let's explore this double slash operator. Let us say, I'm going to say set is equal to three slash slash two. Okay? So what it does, let's run this. So it has given one. What does it mean? It has given the tient to one, and it completely ignores the fractional part. That's the whole idea of this apparatus. Okay. Conventionally, this is how we use. Can I use this exponeniation slightly differently? Instead of assignment operator, I could use some shortcut for arithmetic operations. Okay, I'll just copy this. And instead of this representation for exponenation, I could use something like this. Okay? Let me try star star equal to three. And let's let's see what happens to this. Will it work? Yeah. Yes, it does. So why is 88 raised to the pore of three? This is same as this one, but instead here, two rays to the pair of three is what we have represented. Here, eight raised to the pore of three is what we are getting. Okay. Then let's use some interesting things we will use. Okay. Let's say, I'm going to put it as X one is equal to. Okay, I'll use the short notation. I'll say percentage equal to. Then Okay, and print X one. So what do you think happens? Let's see this. X one is not defined because we have not defined X one. Let's say we're going to take X and see this. Yes, there you go. It is same as this is same as just writing for reference. X is equal to X percentage ten. If I run this in the next line of code, you will see exactly the same output because it's a different representation. There is no difference in the output. Okay? This is using assignment operator. This is in place of assignment operator. We have got shortcut for arithmetic operations. So the long story short, I'll put it just above here. You'll be able to use the various shortcut for arithmetic operations. Okay, shortcuts for arithmetic operations. So what are those? You could use. You could use plus equal to, you could also use minus equal to. You could also use star equal to. Similarly I could use percentage equal to. I could also use slash equal to. I could also use, for example, double slash equal to. You can explore more as a practice, okay? I could also use double star equal to. All these are possible. Okay. I think hope everybody's clear on this. Shortcuts, four is what I meant. Let's go ahead and look at this. Yeah, I want to save this. Hope you're enjoying this learning. 12. Floor Division Operator: Continue. I want you to understand very interesting operator in Python called for Arithmetic Operations called floor division Operator. Floor division operator. So let's explore this because it's interesting, okay? I'll show you some variations of this, which will be really worth everybody's while to follow and understand this. Let's say I'm going to use a function called print, and let's say I'm going to say ten slash three. What do you think is the output? Okay? So let's say this. It has given the output three. So what does it even mean? What does it even mean? Can somebody explain? Go to the comment section of the description wherever you want to interact. Go ahead and explain this operator. Bring some subtle variations to this and then talk about it. Let's say I'm going to make it minus ten slash three. Will the answer be something different? Than this, or will the answer be same? So when in doubt, please execute and see. Here, it has given me three without any negative value for ten. When I had given minus ten, it has given me three. Okay? So one more time, I want you to explain why it is three here, and I want you to think about why it is minus four. I shall explain. In this example of minus ten slash slash three, what it has done, Python H, it has looked for multiple of three, which will yield minus ten. So it turns out, it'll be multiple of three which yield minus ten would be -3.33 33. We don't need to be too precise. I'm just stopping at the fifth demu. Floor value for this, okay? Floor value for this is minus four, and that's why you have seen this. Okay. Is everybody clear on this? Let us look at some variations. So what are the other possibilities? We have ten positive, ten negative. We have three positive here, three positive here. Let's make three negative here and see what happens. Any guesses for the output? It's a matter of execution. So minus four. So what has it done? Similar to what I explained in this example, cell number 25, cell 26 is something like this. Multiple of minus three that will yield ten is Let's put it here. Okay. Multiple off. Let's put it completely here, multiple of minus three. That eels. Ten is -3.3 point sorry, 3.33 33. Whose floor value again is minus four. And that's why you see the answer minus four. So what other variation possible to this example? You can make both minus, minus ten, minus three, again. Any guesses, how does it work? It's a matter of execution. One more time. Learners. Same argument can be made. I'll just copy this and whatever is required to be changed, I'm going to change. Okay. So multiple of minus three that yields minus ten because we have the numerator minus ten is now it becomes plus 3.3 point sorry, 3.33 33, whatever. Whose floor value is three. That is the reason you see the output three. Hope that is clear to everybody. Okay? If I have any doubts, feel free to ask me. I'm more than happy to clarify or bring more examples. Yeah. 13. Operations On Boolean Data Type: Learners, I'll scroll up to show you something interesting. We looked at several examples in this tutorial and we explore the datatype called Boolean true and false. Is a mathematical operation possible on bull and data type. Let's understand some interesting ideas of operations on bull and data type. Operations on Boolean Pat? Follow along. This is another interesting idea here. Let's say, I'm going to say X is equal to. So true, we can represent it like this. Can we print X? Any guesses per value X? Let's execute. It is true. Okay? If I Print X is equal to false. No point for guessing it. False is the answer. Can I go ahead and add True like this. What do you think is the output? You can pause this tutorial and then think and come up with an answer. Better still execute it to find the answer for yourself. You'll be surprised to see the answer no. Instead of true and false here, I'm executing cell number 28. S two it is. If you're wondering why True is considered one. Okay? And false is considered zero. I'll just put it here. False is zero. What do I mean by that is if I say false plus true true is one, false is zero. If a print is, you will get one. Okay? So another variation to this will make, let's say, false plus false. No points but guessing the output would be zero. So another variation you can make to this. Of course, it's very obvious, but I'm still making it for thoroughness. Okay? So this will also be one. So everybody is clear. Since true is treated as one and false is treated as zero, they can be added. Okay. Everybody's clear, right? 14. Precedence & Associativity: Learners. By now, you're comfortable, I suppose, with the arithmetic operations. We have done operations on Boolean data type, and we have used four division operator and we have used several arithmetic operations, right? So here is the fundamental idea of understanding for thoroughness. When you are using operators in an arithmetic expression in Python, is there any priority of the operators is used? Put it another way. Is there any order for the operators to be used when it is used as part of arithmetic expression in Python. And that's what is called precedence and associativity. I just want you to understand this. These are some of the specific things not many people would cover, but these are important to understand. Someone will give you an abbreviation to start with, which goes something like this, P E. Yeah, ma'am, DAs. This gives you the order of priority of the operators, okay? Order of the priority of the operators. Priority of the operators arithmetic operators, if you want to. Yeah. So explain. Don't worry on that. Would I would go in and qualify this decreasing order of the priority of the arithmetic operators. So remember this. When you use multiple operators as part of arithmetic expression in Python, it's always evaluated based on some priority of the operators. And it is always in the decreasing order of the priority of the arithmetic operators, which is specified or abbreviated as PEMDAS. So what does this? We'll just simplify this. Okay. This stands for parenthesis. P stands for parenthesis. So remember, it is the operators in the decreasing order of priority, which is this, parenthesis. Then the next would be exponentiation. The E stands for exponentiation. Okay. I'm also giving you the operators, which is represented typically as double star. Next decreasing order would be several operators. You will have multiplication, you will division. Sorry, you will have multiplication. You will have division, you will have floor division, and you will have this percentage operator. Okay? So all this can be put under this, which stands for D rather, which stands for multiplication division. Okay. So I'll just put it like this multiplication. And division. These are the operators. Then we are left with AS. So no points for guessing. By now, it should be crystal clear to everybody. You have addition and you have subtraction. So that's what it is. So addition subtraction. So please remember when you're using multiple arithmetic operators as part of arithmetic expression, it's always evaluated. I mean, operators are always evaluated in the decreasing order of the priority of the arithmetic operators. First one, highest priority goes to parenthesis, followed by exponentiation, followed by multiplication and division operators, and the least priority goes to the addition and subtraction. Okay. I don't know if it has occurred to you in your mind. Some of you might be thinking ahead of what I'm going to say or ask rather. Let's say there is a tie between operators of same precedence. Okay? I ever there is a tie between operator of same precedence, then it will always be addressed using associativity of operators. Okay? So just put this when there is a tie between operators of same precedence. Then it is addressed using associativity, associativity of the operators. Okay. So slightly deeper concept, please understand that every operator would have either left or right associativity or right to left associativity. Let me phrase it properly. Every operator would have either left to right associativity or right to left associativity. Okay? So it can be thought of like this for an example. If X is equal to, let's say, x one multiplied by X two, divided by X. Okay. I I like this. In this example, the star is done much before everything else. Star is done before the slash operator, since alphabetic operators will have left to right associativity Think of it this way. Star and slash are part of multiplication and division in the decreasing order of priority. So they both have same precedence, star and slash. So look at this. Whenever there is a tie between operators of same precedence, then it is addressed using associativity of operators. That simply means every operator, either star or slash, would have either left to right associativity or right to left associativity. If I have this example where X is equal to X one multiplied by X two divided by X, this multiplication is done before this division because arithmetic operators have left to right associativity. I'll put it here as a comment that will clarify. Okay? Star is performed before since arithmetic operators have left to write associativity. Hope everybody is clear on this. Okay? So I want you to be comfortable with these ideas, which is important in the context of Python Basics. Okay. Hope you're enjoying. Continue watching. We will have some more interesting Python basics coming your way. Keep at it. You'll get better at it.