Data Analytics With Python Full Course 2026 [FREE] | Python Data Analytics Tutorial | Simplilearn

Key Takeaways

Hey everyone, welcome to this course on data analytics with Python. Ever wondered how Python can be the secret to unlocking valuable insights from data? Well, this course is your gateway to master data analytics with Python. So whether you're just starting out or looking to build an existing knowledge, this course will guide you step by step through the essential tools and techniques needed to excel in data analysis. Why is this course different? Because instead of just learning theory, you will be working on hands-on project from the start, including real world applications and step-by-step instructions. You won't just understand Python's role in data analytics but also how to build your own data projects using the most popular Python libraries like NumPy and Pandas. So here's what you will learn. First we will dive into the basics of Python programming and the essential role in data analytics. You'll get familiar with code concepts like functions, debugging, building a program step by step. Next, you will learn numerical Python NumPy, the go-to library for handling arrays, performing matrix operations, and applying statistical functions like mean and variance. Then we will move on to data manipulation with pandas where you will learn how to manipulate data, filter data sets, handle missing data, and work with JSON data. You'll also explore how to perform aggregates and clean data effectively. Finally, you will explore data visualization using Python plotting libraries to create bar charts, box plot, visualize key data insights with a focus on practical industry relevant skills. This course will equip you to tackle real world data challenges and develop the confidence to work on advanced Python projects. So, are you ready to transform your understanding of data and programming? Let's get started. So, before we get started, here's a quick information for you. If you're interested in becoming a professional in the world of data, you should definitely check out this data analyst masters program. Now, this course is specifically designed to help you focus on Tableau, which is one of the most powerful tools for creating visual stories, interactive dashboards. So, whether you're a beginner or looking to grow your skills, our syllabus covers everything you need to know. You will start from basics of Excel, SQL, moving on to Python, our programming, mastering data visualization. The biggest advantage of this program is that it's not just about theory. You will be gaining hands-on experience by working on dual projects like crime analytics, sales tracking. Plus, when you finish, you won't just have new skills. You will earn an industry recognized master certificate from simple learn and official certificate from Microsoft. So, this is your chance to join a field that is adding millions of new job. With our expertled classes and career support, we will help you get noticed by top hiring companies and land your dream job. So, here's a quick quiz question for you. Which Python library is used for numerical computing and handling arrays? Your option are pandas, numpy, mattplot, lib or sci. Let me know your answers in the comment section below. So without any further ado, let's get started. >> So now like we jump on the next part with python which is your data analytics, right? So so data analytics is is generally like most of the companies are doing data analytics, right? So first we try to understand like what is data analytics and uh like how we can use this data analytics uh using Python. Okay. So first of all like we have understood Python right and then uh there are some libraries in Python we can from where we can do the data analytics part right so py python libraries such as your mattplot right and your numpy your pandas right so these are some of the effective tools from where you can do the data analytics very effectively right so we will touch upon these libraries And the main agenda of this session is that we will try to look these libraries and do a practical right as we have done the practical demonstration of Python. So here also like we will be doing the hands-on practice. So we will first touch upon some of the basic concepts like why we are trying to do data analytics and why we are trying to learn it and then we will try to quickly jump in the next later half of the session with the Jupyter notebooks and we will try to touch upon these libraries. Right? Understood? Right. Okay. So, so why Python is used for data analytics, right? Because basically it simplifies data task and means for effect age and effectiveness, right? So basically Python along with some utilities such as your Matt plot, your Cbond, your pandas, right? So these are some of the libraries which can do the data analytics task for you. Okay. Right. Right. And And it also not only can work on small data set but also with large data set. So as you can see that now the era of data is like is is very huge right. So as you see that your social feed has been generated by your Instagram, your YouTube feed right your Twitter feeds right. So these are being data right your IoT data is also considered with data which is a real time data and this data has to be analyzed in the real real uh real time right and then Python is the library Python uses some library which can effectively leverage the data and effectively use it for the data analys and the data is being your it monitors your heartbeat or your blood pressure right And this data is being constantly stored on the servers. And then it will try to analyze something and from the basic trends it will try to give you that okay your heartbeat is being uh rising or you have some threat in your body right some kind of analysis it will try to do and it will try to inform the user. So this is the practical implementation in which the data is being uh fed to the servers and from the servers some sort of data analytics is being done using some Python code or using some machine learning right and some algorith which will try to tell or predict predict what are the chances that the user can get a disease or user is likely to get some disease right so these are the things which they can predict so these these is really useful by using your Python, right? So capability it manage this large data set effect efficiently and with and also scales it with demand right. As soon as the data gets uh like uh increased like more and more it it tries to increase right it will try to manage the data. It will effectively try to clean the data and then try to do some prediction right and it also have the versatitality. It adapts to diverse data, right? So the diversity means your data would be collected from various sources. It can combine that data. It can clean that data. It can transform that data and then effectively it will try to analyze some task or do some decibels, inform decisions, right? And the why Python is gaining importance nowadays is because of the community also. So Python community is very strong. The developer community is very strong. Even if you get can get some error, you can just go to the tag overflow or your Python uh og right and there some other developers are also facing some issues into that programming or some error. So they can guide you. Okay. So internet has lot of support in terms of uh Python. Some packages is not working, some frameworks is not working right. So you can get effectively very good support of it. It is not that like you are using alone you are using Python and the millions of subscribers and the users are using it and some of them has faced some issue in some versioning or in some particular program. So they might have uh written that code and and posted on the internet and and the Python community or the the developers which are working in the Python community. So they have solved that issue. Okay. So as compared to some newer languages let's say Golang or your Swift or or some other languages. So in some parts like there the community is not so so active. Okay. So now the Python community has grown bigger and stronger. There you will not face any kind of an issues or difficulties while using it and also it is open source. You can also contribute to it. Right? So, so this is the main advant advantage of using Python and in in terms of data analytics, in terms of machine learning, in terms of AI, right? So, as as we progress this course, right? So, now we are trying to learn why Python is effective for using data analytics. In the next session, we will try to learn why Python is effective for using your machine learning, right? So, this we will try to cover up. Okay? So now we'll talk about uh uh what like is a library right? So as earlier also I stated like there are some packages or modules which we call it that is your library right? So Python is a loan a language right. So you have started working with Python and we have seen that there are some data types in Python and there are functions right and then we can use that functions and these data types and using the if statement so we can just make some small programs right okay so let's say if we wanted to do uh some sort of analysis right there we need to have some Python libraries okay so what are these libraries so considered in this manner you are trying to construct a house. When you try to construct a house, so there you need a plumber, you need a carpenter, right? You need a painter to paint your house, right? So these are being referred as two external contractors which will try to do the job for you. Okay? So similarly in Python also these as you see see in the screen these are some external libraries like skypi pandas metro lake seborn numpy or stats model which has some pre-written code that can be imported and used to perform some tasks which you uh which you have a requirement let's say I wanted to uh do some uh um uh analysis of some data Right? And I need to do have I have huge data. I need to do some mathematical computations. So there I would use numpy. I will store the data into a numpy array. And then I try to do some matrix multiplication. Or else let's say I have a CSV and the CSV there is a long like uh record of CSV right which around 30,000 records or 50,000 records. So these CSV you cannot manually try to see each and every record and try to do some manipulations. However, you can do it by using macros and and some this uh Microsoft tools have given us the advantage but in terms of programming so it is very difficult for you to manually uh see it. Yes, CSV could be an Excel also. CSV is a basically a format. So Microsoft Excel, Microsoft CSV, right? So these are basically the or PDF also or TXT also these are various formats of data in where we have stored some data in the table format in the row and column structure. You are right. So this is what I'm trying to refer that CSV is mainly refers to the XLS or the Excel part. Right? Okay. So there you wanted to analyze something but the data is so large and so huge. So you cannot manually it it will try it will it will be very troubling situation for you to just analyze it manually. So what about like you have external library called panda. So there you can just see the records and you can just interpret give me the unique records which are present in the CSV. give me the records or the names of the students which are scoring less than let's say greater than 80 80 80 uh 80 marks right so I want to do some data manipulation right so these way the pandas is the extent library or you have tried to call that library and try to use its functions and methods right from that pandas library okay then after doing the manipulation or interpretation or cleaning your data, right? So, you wanted to showcase it. How you can showcase it? By use of some uh charts, graphs, right? Because visual representation is is is best like in terms of explanation, in terms of storytelling when you are trying to represent your data and you are wanted to uh convey your information to some other external third party. So, so visual representation works best, right? So that visual representation could be in terms of your uh charts or biograph. Okay. So let's say I give you an example. Let's say we all watch news every day, right? So we all watch weather forecast news. Okay. So there they will try to give you a graphical representation and in which area the rain is likely to occur, right? And this is how the uh the how much rainfall could be recorded in these areas. The winds are blowing at this speed, right? So they give a graphical representation. So however they could give it by numbers also but numbers don't appeal it to you right so the graphical or the visual representation appeal it to so they show it by the graphs or the bar charts that how many percentage percentage of voters are being listed in this constituency right so this this candidate is winning or losing by how much percentage right so these the things in which we can just show them the data analysis by using some kind of a tools which is called as metplot or c1. So c1 and metplot libraries are used for the data visualization. Numpy for numerical python. Numerical python means storing the data in which we can use this large storage of data effectively and we can do some matrix multiplication also. Right? So, so these external libraries we will be doing in practice. Okay. So, I'm just giving you the uh building up the context in which how why we are using it and and what what these libraries offer us. Right. and pandas effectively is a very effective library in which we can use these uh these for for for for taking the CSVs or vxls or the txt part right and try to uh do some manipulations on the data right to find some records right to that data and skypi and stat models include your models or your algorithms or doing some statistical analysis Right. So, so what you wanted to find the mean, you wanted to find the uh standard deviation, right? You wanted to include some of the ANOVA test, right? So, these we will try to learn in in in in our coming lectures. But I'm trying to give you a context that these all the libraries we trying to uh learn it practical, right? Okay. Okay. So now uh as I already told you so these are some of the libraries which are being broadly divided into three part. So one is your numerical computation another is your data manipulation and third is your data visualization. Right? So numerical computing uh they provide your array objects and your mathematical functions to perform your computations. Right? So in the end right so when you are trying to do some analysis of the data so you wanted to perform some arithmetic operations or mathematical computations also so there numpy and scypi libraries work best okay so these data has been converted into a numpy array from there you can perform the functions which is the inbuilt functions you don't need to write any code you didn't you don't need to write any complex algorithms where you can perform some operations so If you have a inbuilt function, you can just call that function and then perform that operation on that particular data set. So for that you need to just import these library such as your numpy and spark. Right? So we will try to look upon it. Okay. Right. And for the data manipulations so we need to modify some of the data. So we have some datetime libraries your pandas library. Right. So these are used for your data manage and for the data visualizations. So we can have your plots diagrams right. So these are the libraries which your matt plot libon or your stats model. So these are your data vization right? Okay. So let us try to see first uh datetime library module. Okay. So datetime library is one of the m most basic data manipulation tools in Python. Okay. So let's say we'll try to give you a uh like like every day we are trying to work with data uh data and that data is to be uh date specific right let's say I wanted to get the uh uh analysis of the previous data right which falls under so and so years okay Or basically for the futuristic data also let's say we are trying to have a real-time data occurring and we can just make some uh checkpoints let's say on the month of February or on the month of March. So let us try to analyze the data uh with respect to the user which has uh maybe a male percentage or a female percentage right or maybe with respect to some goods also let's say consumption of rice like like happens to be majorly uh during the festival season it it increases or in the northeast region so there are mostly people eating rice right in the south also people generally eat rice but in the north generally beat right so based on on on some some trends we can just uh uh use this datetime library and according that we can just leverage it right so it is a standard built-in library in Python and it also offer classes for manipulating date and time right so let us see so this datetime module is being divided into six parts so first is your date then it is your date time then it is your TZ info time time delta and time. So date it it will have only your year month and day right and your date time it will be a combination of date time with year month and hours microsconds and also the time zone information. So TS T is that means your time time zone information attributes right okay and also for the time and time delta it it also will give you the time basically it will only uh give you the time in hours minutes or seconds or microconds right and time delta will try to give you the difference between two days or two time periods okay so now we will try to uh do a practical also like how we can try to calculate the age of a where like you try to give the age and uh from your age and your current date you wanted to calculate how many number of years uh you or what age is is of yours right okay so we'll try to cover it a practically okay right so let us try to see this in practice okay okay so I have already opened it so you also open it And let me create a new folder and I will say Python. And I will create a new Python notebook. Okay. Try to create a new Jupiter uh file. Okay. Okay. So what we are trying to do is so this is your date time. Okay. So from day time date time is a complete package. We are trying to import date. Okay. So I have also written something like this from date time right okay so let's say this date time consist of some let's say if I say date time dot okay so you can see that so this C so this C is a class right so we are trying to import date so what we are trying to do is so from date time we are only importing the class date okay so C stands for class. Okay. And these are your instances. I means your instances, right? Okay. So here you can see that. So in this daytime module as we have seen earlier also in this slide. So we have your date, date, time, your time, time delta, time zone and t as info. So these are all the like uh six classes which we have seen in this slide. Okay. So this we can import and these are your instances. So these are your instances and these are your class. So every module or every library follows this kind of a okay you just need to know the name of their library okay don't need to go to the internet and try to uh search for it but in some cases we just need to search for it also right because we don't know we cannot check each and everything okay so this is how we can see that from date time we are trying to import only date and here in the line number four what we have written is import date time this means I have imported all the classes whether I am only working with date. Okay. But I have also imported date time. I have also imported time time delta time zone and t as input also. Okay. But these may not be useful. Whatever is useful you only try to import that that date. Okay. Is is is this what what we are trying to say is like you are trying to hire a carpenter. Okay. So carpenter comes with a bag full of his tools. But when you hire a carpenter or call a carpenter, so he comes uh the tools of the painter also, right? We haven't hired the painter but we haven't called that painter but the tools is bringing with this. So similarly from the date time we only need the date class or the date module but we are trying to import here all the modules in we when we are trying to call the date time. Okay fine. Okay. So I've shown you this thing. So this is the practice, right? So if you are trying to work with your data, don't try to import blindly. Okay? Right? Try to call that module whichever is required only because it will try to generally uh increase the memory of your code because you are trying to import loties and calling but here you are trying to call only those libraries. What will happen is that when you are trying to run this code first it will go to your date time from that date time it will only import this date module but when you are trying to write this thing it will go to your date time and import all the modules okay then it will be loaded into your memory and it will try to increase your it will slow down your Python code okay so I will not try to write something like this right okay so let us try to see this okay so date dot so in is date we have tried to import that class. Okay. So now you can see that from this class so these are some functions and these are some instance function if you have function you need to put a bracket and if you have instance you need to put a day uh you don't need to put a bracket okay right so this you need to understand okay so let us try to give some some date okay and if you miss some previous lecture so there we have the signature right so there by the shift and tab we can just see that what what kind of a signature is that so do string is your date year month and date so in this date we can just specify the year month and date and it will try to give us it okay so let us try to see that so I will say 2012 3 and 12 okay and I will try to see that like let's say this is your date therefore uh it could be here first state or something like that right and I'm trying to print that now you can see that if you wanted to get a day so you will try to get a day okay dopray dot year you can get the year day dot Right. Day month also it will be there. Yeah. Also there right so you can get each and every so this is how you can get you can convert any of the day into your birth day and year. So from the date time module we are trying to import some date. So date is a class. So class okay. So from that class we have some functions or some instances right. So these are some instances. So date dot day is instance instance means some kind of variable and we can also have some functions assocated with it. Okay. So if I try to write date time birthday dot and I could put a tab. So you can see that c time is a function day is an instance right is is of format from time is calendar is of week right. So these are all these things right. So strip time is also a very good function. Okay. So this is used for styling basically. Okay. I just want to uh get in uh one second. Okay. So I'll tell you about this uh uh in the coming slide. Okay. So have you done this guys? So I'll talk talk about this trip time in a later. Okay. So this was the date time module in which we are trying to have some date right. Okay. So the next uh part right so the today's date okay so let's say from date time so we are trying to import the date and we are trying to get the today's date. So date dot today will give you the today date right. So this also we'll try to see it. Okay. So quickly just see if you try to have the date dot today what will be the uh output. So let's say I will try to date dot today and I will try to have today date then I will say print today date okay so now you can see that today date comes into this uh thing So it is throwing error when we enter date or month with leading zero date or month. Yes. So actually date or month could be zero. Right. So it will give you error right. So date is being expected from 1 to 31 and your month is between your first and your 12. Right? So so that is why it is throwing error. Understood? Okay. 03. Yes. So 03 it will not be given I guess. So if you try to give it yes leading zero and decimal are not permitted. Okay. So it is a syntax error basically. Okay. So basically like it is giving you error. So it is saying that so it is designed in such a manner. You cannot give zero. Okay. So you just need to make sure that you don't give 03 only three is allowed. Okay. So the leading zero in decimal integer is not allowed by this liability. Okay. So basically this liability I have not designed it right. Someone has designed it and when he or she is designing it so they have made some constants. Okay. So they have placed some constraint. Then these constraint you need to follow along with it. Okay. And make sure that if you are trying to give some date to it and uh uh the syntax are proper the validation are proper the validation and syntax are not proper. So it will give you error. Okay. So you just need to make sure that zero is omitted and then you try to print that. Is this clear? So have you tried today's date? Okay. So we have tried to do the birth rate date. So you can take the birth date and here is the today's date. Okay. So now I'm building some intuition. So you just need to have a program written in which you try to take the birth date of the user and you need to compare it with the today's date and from there you just need to calculate what the age of the person is. Are you clear with that? So you just need to write the program and and here in the as we are I'm trying to explain you the slides and we are trying to do it. You just need to make a program and like in the end I will try to check like uh like you need to take the date from the user which is your birth date and then you need to compare it the today's date and then you need to tell me that how many years that that user is ages fine okay so this is how we are trying to build some intuition in which we are trying to learn some programming right so today's date dot year month and day right so as I already told you so these are some of the things I have already discussed with you that the what year is this, what month is big, what date, right? So we can perform some datetime uh operations on the the basic uh display, right? Okay. Rest. So now we try to do some practical. Okay. So I give you 5 to 10 minutes, right? To calculate the age using the datetime module and in in Python. So what you need to do is guys. Okay. So here uh you just need to write a program where you will try to take the user date this date okay in the form of some input right so you will uh tell the user that enter your age enter your enter your month enter your year enter your data so so this is what we are trying to enter in the websites also when you're trying to fill a form okay and automatically like at the background we they are trying to calculate the age according to today's date. Let's say you are trying to build some form which is your uh let's say some uh student registration form and the age is is is permitted only 30 years. Okay. And if you they are trying to calculate your birthday and if the age is is less than 30 so they then you can fill the form else you cannot fill the form there is a error message. Okay. So similarly at the background they are trying to calculate your age and if the age is permitted so they will proceed else they will not proceed. So this kind of application you need to do it. This kind of application you need to format in which you would take the uh the data from the user and then you will write some functions and in the top of that you will try to call that function and then you will try to return the number of years that how many years were there. Is it fine? Clear? Okay. I'll give you 2 minutes and after that I will try to do it along with you. Okay. So that you can also think and understand that how we are trying to make a program. Okay. So I will also guide you step by step. Okay. First what is the first thing you will do when you are trying to build a program or write a program. Okay. First you need to gather the steps. Okay. Then you need to just write some functions. Then you just need to make sure that that function is being called where that function is being called and then if the error comes then how to debug. So you by using this demo what we are trying to do is we are trying to just write some Python program and then we are trying to evaluate and try to debug also. So I'll try to write some steps okay and then we will try to make some okay first step is take the input from the user. So the first question is that what input we will take? We will take the year, we will take the month, we will take the date. Okay. So three inputs we need to take it. So what function we will use? The input method or the input function we will try to use. So this we already seen in the previous session, right? So from the input we will take it. Right? So when we take it now we need to convert it in the date format, right? Using your date function, your date library. So this we have already seen. birthday date, right? So, you take the input, you place it here, you place it here, and you place it here. The year, month, and the day, you make it a day, birthday, right? So, you now have your birthday? Understood? Right. Right. So, first you need to do it, right? Let me know once you have completed this task. So we will try to break down this task into simplest step. First we will take the input from the user. Then we will try to convert this input into a date which is your birth rate. Then what we will do is we will try to write some function where we need to calculate the age. Okay. Calculate age. It would be a function. Right? So in this calculation of the age you will try to pass the birth date right then this is your argument. Birth date is your argument right? So in the function we will try to pass the argument and inside your function body what we will try to calculate we will try to see or we will try to uh first uh uh will store some current date right so current date also we are seeing so current date date dot today this is our current date and this is your birth rate date right so now simply you just need to subtract it and just need to evaluate how many years have been passed. Right? Okay. So for that what we have done is we have written basically uh take the input you can divide it into a function also. This you need to make it a function also. So basically there are two functions which you have written. One is for calculation of your birth, calculation of your age and another is taking from the input. Another is your taking the input. Right? So these two function you first try to take the input then you try to write some logic and then you try to give the age right. So now the thing is that uh I'll give you like uh do you want guys time or or or let's let's uh give you five minutes right start try to do it and also like in the meanwhile I'll also join you right I also start typing because I what I want you uh is to think right because the thing is that your brain should start working that how if you try I have given you some program so this is a basic simple program Okay. So in the industry there could be complex program where you need to just gather the requirement. You need to take down these requirements note it down and then you try to break down into steps that how you will try to uh formulate this instructions or requirements into program. Right? So my task is that like I have given you a simple program in which you need to take the input from the user in terms of your year, day or month and then you need to try to calculate the age. Simple right? For that you just need to make or break down that statement so that if any user try to write the age right he or she must give the uh uh the birth date he or she must get the age of it. Okay. So I'll give you uh 10 minutes right or just try to start practice or try to do some programming and just let me know what are the errors and what where you are stuck because this activity is really very uh needed because to to understand you that how we are trying to do some programming and and in later stages so it could be a complex programming also right so just make use of your brains and try to just uh write or type and uh come with some errors if you if they are okay it's fine right so now the input which you have tried to enter is in the string format but we don't want to have it in the string so what we'll do is we'll try to take type cast to the integer right so then similarly for the day also uh the for the month also we just need to do it so what I will say here I will try to say month then I will try to save it here day right and then I will say here enter the month and enter the date right. Okay. So now uh what I will do is so once it has been entered so what I will do is I will try to have a date which is your this date okay from data import and then I will try to put the year then month and day here. Fine. Okay. So right so this is your date right and I will try to find and convert it to the birth date right okay and then I will try to print it fine okay so I hope this is clear okay so I will try to enter it enter the year let's say 2012 enter the month so I have entered third let's say fourth and day let's say 21. Okay. So now it is giving me the birth date as 20124 21. Okay. So now uh there is another uh thing. So let's try to check for the errors also. Let's say I tried to enter the month as 13 and day as two. Right? Now what will happen? The month must be in the 1 to 12. So we have written the program. Okay. So this everyone has written it. But the thing is that we just need to provide some exception handling also right that that the thing is that like if you don't provide the exception handling or the uh the thing so it will try to give some error. So that error will doesn't look nice. Okay. So just we need to include the error also. So what I will try to do is I will try to include some try and accept block. So let us try to do some try and accept and then I will say except exception right okay as e okay then I will say print enter the grand and right okay so now let's try to check again I will try to Enter the month right. So now it has say that enter the valid date month must be in 1 to 3. Okay. So earlier what we were trying to see is we have seen error. So error are bad. So if you are trying to showcase the error to the user so this doesn't look nice. So let's say you are trying to go on on the flip card or Amazon. Okay. Suddenly some error pops in right. So it will be a loss of the business for the Amazon life. Right. Okay. this I need to and you will not ever go to that site again because you have encountered some error. So when you are writing some program it must be validated and it's it must be exceptionally handled so that you will not get any errors. Okay. So now what we have written is we have written with the try and accept block so that any kind of an error it occurs then it must be handled properly. Right. So this is very important in which you just need to enter the validates and it must be validated. Right? So now we'll try to more refine this this thing right so this code okay how we can refine this thing so we can include a method we can wrap this entire code into a method so that in future let's say in future uh some additional modification has to be done so we will try to do the modification in this this function only right so let's say I will try to uh get some get birth date okay so try to make the function in such a manner that it will have some valid or meaningful name right okay so instead of print I'm trying to return the birth date fine okay okay so this we have written a function okay so now what I will do is I will try to call that function get underscore birth date. Any doubts, any questions you just let me know. Okay. So let's try to now try to write the logic. Okay. So what is the logic is that first we'll try to make the today date. Okay. So what we'll do is date dot today. Okay. So now then what we will see we need to calculate the age. Age is equal to today dot today date dot dot year minus we are trying to calculate the birth date. So birth day dot year right. So we are trying to minus right. So this it will give we give me the year right? How many years have been passed? Then I will try to calculate the today dot right. So this is how we'll try to get the each. So here this Right. So now we'll try to get the A. Right. So now this these two things what we will need to do is we will try to combine it in a single function. Okay. So what I will do is we will try to write a function and we'll we will say get age. Okay. And here I will try to give the birth date. Okay. Then I will say return age. Fine. Right. So now we have made two functions which is your get birth date and get age. Right. So here we are trying to pass it this your birth uh get age and then okay. So now what we will do is we will try to write the main function which will try to like use these functions and then we will try to uh call that. Okay. So what I will see is what I will do is I will say def main. Right. So here I will say part date= to get okay then age is equal to get age and here I will pass your birthday. Okay. Then I will say print the ages right and then I will try to run s name. Okay. So have you understood guys? So this is how we are trying to calculate uh the age right by using this init module. Okay. And uh we are trying to make this as a modular because we have written some functions right. So here we can just try to include or in future we need some modifications. So we can call only this function or else this function. Okay. So this is how we can try to begin with small program in which we can use it and then we can just make the bigger and the complex code. Is that fine guys? Yes. Deaf function. Which def function? Uh I need to because there are three functions I have written it. So one is this def get age uh and your get birth date. So what first one? Okay. So this get birth date. So this is a function which the which the definitions uh occur and we have included some try and accept. So this try and accept is for the exception handling if any error occurs. So we can catch this into this particular this thing uh this uh exception right in the accept in the accept block. So this is your try block. This is your accept block. If any error occurs or any exception occur in this block. So it will try to give it will not give a uh uh exception it will try to print this enter a valid date and it will give you that exception. Fine. So now we have made some input statements where we are trying to ask the user with this year, month and day and we are trying to calculate the birth date. Okay. So we have used our date module and uh here we have combined our date uh year, month and day to the birth date and we are trying to return. Okay. Then the second method is your get age in which the argument is your birth. So we can pass that argument and we are trying to calculate today date and here is the logic where we are trying to calculate the year minus year today's date year minus birth year. And this what we are trying to do is we are trying to compare right. If the month and your but month right if it is less okay so then we are trying to subtract it. If it is not then we are not trying to subtract it right. So this is the logic we are trying to follow here. Right. Okay. And it will try to give me the age. Right. Right. And these is the main function where we are trying to calculate the birth date. First it will try to go into the get birth date. Then after that it will try to get the age, get the age. So here it will try to go here. It will try to get the age and from the age it will try to print it and this main function we have called in this particular block which is your entry point. Right? So this is the whole program how it works. Right? So you don't need to write your function uh uh your your program uh right you need to just break your program into three parts. First is your main part which is your entry point then you are trying to write the functions which is your this function and this is this is so this is the ideal practice where we need to learn programming in this manner. So every program has to be written in this manner only right. So this is like the ideal scenario right. So this is the tpple basically right. So this is a tuple. So this basically what we are trying to do is let's try to break this down. Okay. Right. Yes. So it is given the calls. Right. So this particularly what it will it will only give you the age. Right. So this we are trying to check like whether this date is greater than this date. Right? So let's say your your month hasn't passed it. Okay. Uh uh and and and uh like the days like you are into same same uh same year, right? So let's say uh your 2016, right? So you have the 11th uh month and second and here you are trying to pass 2020 and here you are trying to pass let's say 12th. Right? So this it will try to compare like the 11th and the 12th. So whether it is being passed or not right. So if it is not passed then like you can subtract that day subtract that mark right. So this is how the logic is being written. So this is being a tpple it will try to compare this both this into these both these date the birth date and the Monday. Right? So you can just write it down in the piece of paper and try yourself. You will try to understand it. Okay. Fine. Okay. So I hope this is clear with everyone. Right. So this is basically a program in which you can write the logic in this this and this is a module right is the is the daytime module which which you can see. Okay. So right so now let's try to move to the another library which is your numerical computing libraries which is your numpy. Right. Okay. So numpy is stands for numerical python. Okay. So it is an open-source library which is used predominantly with when working with arrays. Fine. So it uses arrays instead of python list which is which makes it more computationally efficient. Right? So all we know that like we have been working with the uh uh list right python list right? So Python lists are not very effective in storing information guys. So if you have a have a long list of data let's say data in terms of your numbers right so that you cannot store it in a list and you cannot perform any operations on the list and it is computationally very inefficient and memory consuming so that we will see practically also right but your numerical python or your numpy is very much more effective and it also supports some algebraic operation which works with your 2D or 3D arrays right and the thing is that whole of In machine learning operations involves your metric manipulations. So your data is being stored into tensors. Tensors or your arrays right in in your tensorflow we call it tensors and in your numerical Python it is in the form of an arrays or numpy array we call it right. So there you try to perform the matric matrix multiplication addition division subtraction all these things you can perform it very effectively right and it also offers some mathematical functions your numpy.log log numpy dot sum mean variance right so this we will try to see practically guys all these things right and your another library is your sci-fi so similar to your numpy there is an sci-fi library which is your scientific python right and it also used to implement your scientific formulas so let's say there are very complex formulas which you don't understand and and these these are very challenging in which we you need to perform it in in terms of your python Okay. So their sci-fi library already has done the work for you. Just need you to call that function and then you provide the data along with it and it will try to uh perform that materical operations on that data. Right? Similarly, it also uses the linear algebra and uh the the and also is very effective in optimization, image processing and your signal processing. Right? So let's say you want to do for real analysis or or 2D analysis right or some kind of an image processing task. So strip library is the best form also in the terms of signal processing. So where there's there's a waves or the transforms in which we need to convert that into signal processing and do perform some analysis. So there Skype library is also very much used. Fine. Okay. So this is the code snippet. So like we will not working on this code snippet. I will try to work with this first numpy and then pandas and then we will try to see it. So this is the basic structure. So this code is the basic structure. So how we can use this? So similarly as we are trying to import your numpy library and we are trying to import the inverse function from your sky library right data data module. So similarly to this so we can uh first define an array and then we can try to take the inverse. To understand this first you need to have a look of this np array what it does what is trying to do. So that is why I I argue that at first we will try to see the numpy array uh code and your these functions then we will work on this ki part. Okay. So this is just a demonstration or or an explanation giving you the gist that how the sky numpy libraries are being used. Okay. So then another part is your uh numpy that is also an open source which is built on the top of your numpy which is your pandas and it is used to analyze your numpy array or or or your numpy array and and analyze and manipulate that data and it works with best with your table data time series matrix data right and the syntax which we can use is your this import pandas as pd Okay, as is basically an alias. We can import pandas, right? Similarly, as we are trying to do is import date time, right? As is alias, a short form, spd pandas data frame or pandas pd we call it. So this is the normal syntax in in in the industry and it has been widely used. Similarly for numpy also import numpy as np and pandas as pd right. So this is the basic syntax. So pandas basically are well suited to perform the following task your data import and export. Let's say we will try to see POT a CSV or an Excel that contains data or some analysis. Then after that when we have done the data cleaning or the analysis we need to store the data back to our CSV that also we can do it not only into the CSV there are various file formats which are been provided by conduct itself. We can do it in Dtxt format, right? So we can have it in in the in the JSON format or so and so right. So that we can do it effectively using your class and then we can use that or import that thing or export that thing. Then for the time series analysis so that is also best suited your pandas library best suited. Right. Right. So let's try to see an demonstration. Right. So these code snippets we will be doing in the next later half. Right. So I'm just trying to build an intuition that that these are the some of the libraries which we will be working on practically as we have worked practically for this uh daytime model. Similarly first we will see what are the methods what are the functions that numpy or your pandas gave us and then later on we will try to build build an project in which we will try to see some CSV read some CSV and then we can export that CSV file and and read it right. So similarly we can do it. So this is a sample code snippet in which what we are trying to do is we are trying to import pandas as PD. This we are trying to import the pandas library as means alias pd. Right? So this is your CSV file path. This CSV file path could be stored in your local system or into some server also. Right? Then what we are trying to do is TD. So PD is your pandas dot. Dot means your function. you are using some power as I already stated that every function every uh this library has some functions. The function it is a bracket. So function the nomic is that you have a bracket right in the read CSV function you are trying to give the path of that CSV file where which you can read that CSV and then you are trying to store that CSV data into some kind of a variable right. So there the variable gets stored in the pandas or pandas data frame we call it pandas data frame that we will try to uh see in future and you can just make the analysis right so this is the simple code in which any file what you have you can be easily integrated with your pandas data frame and then you can just do the manipulations uh your your your analysis and then you can just uh uh store it and then analyze it and then uh try to uh try to give some inference from. Right? So this is from the CSV file. Another is that you can have a read JSON function in which you have the JSON data from there also you can do the similar operations. It is basically you are trying to read this by giving the JSON file JSON file or JSON file path right the functions are different right so this is the sample code snippet we will try to see full-fledged is an SQL file can we import the Excel file yes so Excel file also we can write similarly for the read CSV we have the CSV file for the read JSON we have the JSON file for the read excel we have the excel file. So basically the the different SQL files uh the different file formats have different functions that we will try to look upon right okay in the D right so we will when we are trying to uh work on this pandas model so that there I will show you that how many file formats are being supported by this pandas we can have the CSV also we can have the JSON also we can have the PHP also right and we have can have the XLS also right So that also data it will automatically uh import it. Understood? Right. Then there are stats model. Stat model is basically it allows the estimation of the statistical models and performs statistical test. So there are some statistical tests needed to perform on the data. Right? So that we will understand and that we will see in the stat models library that these some of the important features include your graphics your time series your an ANOVA test linear regression right so these are some of the uh models or the machine learning models we call it where we wanted to perform some analysis on your data and perform that algorithms onto that data. Okay. If these algorithms are complex and someone has written this and made a library out of it and you can just call that library and use that which is which makes it very effective for you because you just don't need to understand the math behind it you just need to call it but the only thing you need to know is that how these these models are working the theory behind it like what exactly ANOVA does what does linear regression does or what does statistical test does right and where we Can these apply these models or these algorithms onto our data and on what purpose? This only information you need to know it. Once you know this information so you can just call that library and use that. So now guys what we are trying to use it we are trying to do the practical implementation of these these libraries and using your Python right then come the third part which is your data visualization libraries. So data visualization libraries it is very like very effective as I already told you that you need to present your data in terms of your pie charts, graphs, histogram right and scatter plot right and and these applications can be integrated with your uh UI applications such as your tilter right ut or gtx right or wx python right where you can effectively show this uh data as in the terms of your story points story point through visualizations. Right? So let's say this is some kind of a data in which this is the uh the number of uh average income of your uh students or or this is the number of students right and then you can see that the average fall was something like uh 20,000 or 2,000 right so something like that right so this this is very effective in terms of your explanation and seabon is another uh highle interface which is very attractive so these libraries is metplot lib is not very though interactive but seon is a higher version of it which gives you a very uh attractive visualization right okay so now let us try to see the official documentation of these uh libraries so which is your numpy okay I'll also open the pandas also then you have your sebon and your mplot So let's try to understand or see one by one right in order to understand this thing right. So let us try to first go with the numpy right. So numpy stands for numerical python what I told you. So it is very powerful in n dimension array and it is like very versatile. Right. And it is open source easy to use. Right. And it is optimized with the C code. Okay. Right. And and you can see that so there are specific scientific domains array libraries data science. In data science you can have the extract transform and load where you can have the pandas right intake py janitor exploratory analysis which I already told you your semon mat plot lip right and your model and evaluate which we have your sky kit stats model spacy right spacy is widely used in your NLP domain right and reports in dashboard we have a dash panel bolia right so these are some emerging uh packages which are coming to the market right earlier days there only numpy, pandas and your mattplot. But nowadays like there are extensive use of these libraries have been evolved in the market and you can use and explore those things. The thing is that if you try to derive a maroti car you can drive any car right if you know this pandas and numpy so you can just easily go to the dash panel or any other library and the basic concept remains the same. Understood? Okay. Right. and and these libraries are used for uh specific purposes let's say for data for high data volumes so dash and ray are designed to scale right stable uh deployments on data working use your DVC right so these are some advanced concepts so I think like these these these you might not be able to grasp it but in the later stages so you will try to see that these libraries and these frameworks are very important right in terms of your machine learning right so these these the like how these data you can see that so here it is trying to use your libraries such as your sky kit sky right and deep learning frameworks such as tensorflow or pytor right and there we are trying to visualize this data so this you can see that how we are trying to visualize this data in in terms of your complexity in terms of your what data points are are are are in in contrast reference to each other right so right so this You can see that. Okay. So another library which we call it like pandas. So which is very fast and flexible and open source for the an analysis and your manipulation. Right. Okay. Right. So you can go to the user guide API and just you can see that like these are how the data frames right grouping shaping. So these all things we will try to see in practice right as the time permits and what are the basic things which we can handle effectively we can use it. So I will try to teach you how to drive a car right. So I will try to look upon basic operations of or your numpy or your metro lab and then rest you can just see and explore of yourself right. So these are various like uh you can see that uh operations which we can perform and it is uh increasing day by day right and and like the these guys are being according to the lead according to the data they are trying to integrate with this pandal because these library is getting expanded day by day with terms of data and terms of your east right okay then we have the C1 C1 I told you that this is also a graphical ical data visualization library and it is very uh what we call beautiful library in which you can see different colors and and and visualizations right okay so it is basic basically based on mattplot only right and but but it has got some rich uh contra rich what we call uh visualizations right okay so here you have the this mattplot is basic library in which if you wanted to use that how we can uh do the data and this is so this is also very effective to use right similarly your skypi right that is also uh very uh effective for scientific calculations and algorithms right and your this is your skyit learn skyit learn is another machine learning library which which will use your classification regression your clustering Right and your uh model selection pre-processing. So these are basically uh libraries based on specific operations for the machine learning there is sky cut and all the machine learning algorithms come for under this cut. Okay. So basically what I'm trying to tell you so these are some tools. Okay. So let's say you try to go for a data science right and and these are the tools which we you really need to understand first. That is why we are trying to understand numpy then pandas then metro lily and these kypi and your scientific skyit learn libraries we will talk about in in this course in this whole series right right then we have the stat model so that also we are trying to have your data set and we are trying to get the summary so this like so here uh the regression summary the rare result statistics log likelihood right so these all the things we are trying to see here right what is the standard deviation right so these are some the concepts so first we'll try to understand the concept and then we will try to use this library yes so uh deep this is very good question you have asked like what how will we know which tool to use and and what for what purpose yes actually okay so let's say uh the thing is that like let's say you have large amount of uh data to be transported from one place to another okay so first you will see that how large the data and what like transportation mechanism that you will use. Okay, this you will see that okay the data is small do you use a car? If the data is large you will use a truck. So similarly so first you need to understand these libraries. So these are the basic libraries which you need to understand. Okay let's say you are trying to do some numpy operations. So then numpy arrays and numpy operations that you need to understand. So that is what we are trying to learn. First you need to learn this library, understand this library and based on your problem statement. Then you will automatically decide like okay so these machine learning learning algorithms we have seven to 10 machine learning uh algorithms we have. So according to your problem statement what machine learning algorithm best fit for lake. So that you will try to use that and you try to uh like solve that problem statement. So this is how you will do it. So the thing is that first is you need to have an uh formal education or formal study that how what are the tools that I would be required to become a data scientist. Okay. After and after only if you try to learn that tools then you will start working on so so I hope you you got this list. So shall we move to the next part? So now what we will cover? So we will try to see the matrix uh manipulation uh by using the one file. Okay. Okay. So is everyone ready? Yes. No. Has everyone right? Yeah. So is my voice uh yeah now can are you audible just confirm. Okay. Okay. So, let's start with the second session involving your matrix uh uh your matrix multiquation manipulation by using your numpy. Okay. So, what we have done a quick recap. So, datetime module is a inbuilt uh library python which uh gives us the manipulation of the date time. Okay. So as we already seen that Python offers numerous libraries for data manipulation, numerical computation, your data visualization which includes your numpy, pandas, mattplot lip, ebon, skypi and your stat model. Okay. So like so deep has already asked this question that like what libraries and in which point of time we need to use it. Okay. So basically these are some tools right. So every data analyst or your your uh data scientist is been working on these tools in real life. Okay. So until unless you don't understand these tools and don't know so you cannot tell that for which problem statement for which uh problem right these tools has to be used. Okay. Now let's say uh we have given you a problem statement right so the problem statement is that that uh your monthly spending right is being categorized into uh categories such as your food your bills your fund travel and that has been categorized and you make a spreadsheet every month in which you try to do some basic uh analysis that on which uh category I have spent most of the uh items or I have like purchased most of the uh I the expenditure is more okay so so what what we are trying to do is let's say you have a spreadsheet in which there are five categories let's say food bill travel and fund and monthly you are trying to write some expense based on that particular category and then after that like to try to analyze it. Okay. So you are trying to analyze but you need some tools to analyze it. Okay. So that is tools we are working. Okay. And then after that you can just try to make sure that that whether this expense uh is really needed or not or should I cut down my expense into some area. Okay. So let's say I have like spend much of the uh my income in in your travel and fun right in food and bills it is still pending and it is showing in negative analysis. So there you can just try to uh do some analysis and based on that analysis you can just spend your saving effectively. So this is the real uh life situation in which all of us can can do a simple uh uh uh analysis using these tools. Right? So the learning objectives of this session is that like uh we will try to see the arrays and your numpy operations for data manipulation in complex and analytic task and onto that we will try to apply some mathematical and arithmetic operations right to to get that operations quickly handled and uh and that be done uh and the outcome should be some kind of an analysis and interpretation which we'll try to effectively run our business right right and uh we will also try to experiment with array indexing and slicing that we will try to see uh in in terms of your data extraction and alteration so we have also seen the indexing and slicing in your data types in Python also the concepts remain the same right is that like how you try to work on your large scale array and which fields you need to slice it and which fields you need to index it. Okay. Right. That is very helpful in which where you are trying to work on the larger data set and only a small portion of data needs to be changed or altered. For that you need some data indexing and slicing. Right? Then the next part we will try to also understand the numpy which will try to read the data from files and it will also streamline the process for data storage and retrieval. So not only in pandas we can just actively use our uh data sources such as your excel, your CSV, your JSON but in numpy also we can use some of the file formats in which we can try to read it and try to process that data. Yeah. So this we will try to understand. Okay. So arrays. So can anyone tell me that what are the arrays? What what is exactly uh definition of an array? So what is an array? Set of values. Okay. Set of values could be anything like uh can can you just more refine this like what is an array? Set of values stored in continuous similar data type. Yes. Yes. Continuous uh memory location. Yes. Yes. Yes. Anub you are all correct. So what are arrays? So array is a collection of elements uh which could be identified by an index or a key. Right? And that stores multiple values in a single value. Right? Which make the management of a larger data set easier. So basically the array what we call is array is a collection of similar data types. We can call it okay. So that is that is the n definition of an array. Right? And that array can be identified by an index. Okay? Because like in the list also we have an index. So similarly the concept of an array we can leverage it in the list format also but list in which we could have the similar data type also or dissimilar data type also or we can have that both right okay any kind of a data type can be stored in a list but array is consist of an uh similar data type or or or a values uh which which have the sort of similar data type. Okay. So the array which we store are being characterized by uh uh by fixed size by the homogeneous and by the index property right as I talk about the fixed side that can hold the fixed number of elements right and the homogeneous data type would be in the array must be of same type okay and that item which we are trying to store it in an array has a specific position and the index it has been ranging from or starting from zero. Okay. So basically what are the characteristics of array? It has to be stored in a fixed size length. Okay. And it could be it has to be homogeneous data type means that the similar data type can be stored in an array and also it consist of an index which is starting from zero. Okay. Index always start from zero. Okay. So these are the characters which have which we have already learned in the list. Right. Okay. Right. So so that also properties uh holds inside your array and your list. Right. Okay. Now we talk about the types of arrays. Single dimensional arrays, multi-dimensional arrays and jagged arrays. Single dimension means list of items of the same type are stored in a linear format. Okay. So we have a bund array. Bundy means single dimension as the details of a list. Okay. Okay. So list what we saw we have a list of items in which we are trying to store a stack. A stack means a uh stag operation which we store items one after the other right the recent items comes at the top and the later items are at the bottom which is a single dimensional multi-dimensional 2D array right rows and columns could be a 2D array right and it could be your n dimensional also maybe a threedimensional four dimensional sorry five dimensional 10 dimensional okay so that dimension is basically harder to explain if we talk about because we live in a plane which is a 3D plane right but if we talk about a 5D plane or 6D plane it is very difficult to imagine but we are trying to store that information in terms of an array which is a multi-dimensional array that we will see n dimensional we call it multi-dimensional we call it okay and jagged arrays which each row may contain different number of elements, right? So, so every row has a like like in in the case of your n dimensional like 3 + 4 or 4 + 2 or 2 + 3 the the rows and columns are fixed. Right? Right. Two rows, four columns. But here two rows, four columns and every row or every column may be different oriented or different having different size. It may not be four, it may be three, it may be two, it may be one. So on so that we call as a jacked arrays, right? So these are the type of arrays. So that we will see in in in your uh uh in your numpy. So numpy is basically a numpy array we call it. Understood? Okay. Right. So now we'll talk about matrix. Okay. So matrix is a array which is used to represent a mathematical object or a property of an object right. So that matrix is the arrange into a row and columns right. So in your fifth and sixth grade we have used the terms as metric matrix manipulations metric metric metric operations. Right? So in terms of array also we have some kind of an matrix uh expressions that is being represented data in the format of row and okay right and then it includes some metric arithmetic operations such as your metric multiplication addition scalar multiplication subtraction right okay so these are some some kind of an operations which we have already seen so addition and subtraction we have already seen but in terms of metric multiplication scalar multiplication right so these these these these you will see right so these can be easily done by using your numpy arrays right okay so now we'll move toward the fundamentals of numpy so we as we already know that it is a numerical python and it is free and open source and used primary for uh mathematical operations in in your uh data right Okay. So what is numpy? So numpy is efficient uh multi-dimensional container for generic data and it uses arrays instead of python list. Okay. So the advantage of using numpy using your python list is that numpy arrays are fast lists are slower. Okay. And the another advantage of using your numpy array is that you can perform metric multiplication, addition, subtraction or arithmetic operations in a very effective and a computational manner as compared to a list. So that holds lot of memory and it has additional challenges. It cannot store large amounts of data. But numpy arrays can effectively work because the underlying principle of your numpy array is your speed. Right? So, so the underlying because like in the top of that Python wrapper is being built where you you can use your numpy add that is why it is very effective for data manipulation and handling and for computationally also it is very efficient. Fine. So numpy is effectively used for processing of large data sets and it is widely applied in the field such as your data and latest and your scientific research. Okay. Okay. So numpy arrays arrays which we call it as as n dimensional n means n could be a number which would be one dimensional two dimensional threedimensional. So the n dimensional means that it is trying to stack your array into multi-dimensional. Let's say this is your uh first matrix. So green one which has three rows and three column, right? Another you have another array which is array two which is in yellow which also consist of three rows and three columns and another which is in in this orange which is your three rows and three columns. So together they combine and form a multi-dimensional array. Right. Okay. Right. So it is like like that two three rows three columns and three dimension. Right. Right. So third is your third is your how many dimensions have three dimensions and in one dimension you have three columns and three rows. So this is what the implementation says in your numpy array. So here you can see that how your data is being stored right. So this considered in this manner. So this is the data of one of the customer right. This is the data of another customer. This is the data of another customer and likely the it could be end data which could be stored in a effectively in a numpy array. And now you can think about it that how you are trying to store that data and when you are trying to store that data and if you wanted to do some operations on it how fast and effective would be. Okay. If I tell you there are 10 CSVs and you need to give me that all those informations from that 10 CSVs and find me that the number of uh customers who have purchased wheat or rice in in in your month of January or February. Right. So it is very difficult for you to just dig that information from 10 CSVs. But when you are trying to put that information into your numpy and leverage it using pandas and data frames and your this thing it would be very fast and effective. Right. So now you are trying to see the the real picture that how the data is being stored and from that storage how that effectively you you can leverage that information and use this for the analysis part. So now let's try to do the practice. Uh right. Okay. So what I will do is so let's guys try to open up your uh Jupyter notebook. So let's try to create a one-dimensional array right first. Okay. 1D array we call it. Okay. Then we will try to practically create 2D 3D and so on. Okay. Fine. So let's try to first import this library. So what we will try to do is import numpy as np right. So as mental as so now everything would be referred as np. Okay. So let's try to do a dot and try to see. So now you can see that so these all functions you are trying to have right. So they are very large in number guys. See okay some of these having a class module function right and and then right copy conjugate concatinate compress so some of these are modules also okay right so this is a very heavy and a powerful library okay but you cannot just try to memorize and try to see all the functions and the methods even I cannot also teach you any I need to teach you each and every function then that it would take like some months to complete this right okay the basic thing which you need to understand is that let's say you are trying to drive a car okay whether you know Maroti like you can drive any uh high-tech or or or a complex uh uh vehicle like which consist of some lot of functions okay if one function you try to understand then you can just see and search and go through that another function and then you can use that. The only basic thing is that like how you are trying to use this library for your own purpose and even I also don't know each and every function okay when it comes to the practical implementation when we are trying to work on some data or problem. So first we try to see that is this numpy is using any function and if if numpy provides some kind of an function to it then we can use it else we try to find like we can write some uh our own function and then try to leverage it. Okay. Right. So you can see that so this is a long list and we cannot use all these things uh in in in practice but but the thing is that we can just use and we can just understand like what this library works do right. Okay. So let's try to use the first function which is your numpy do array. Okay. So here we are trying to put some random number and let's say array 23. So this is your uh let's say a single number we are trying to convert this to a numpy array. So let us try to uh name it as array one. Okay I try to delete this uh this creating confusion. Okay. Right. So now I will try to print array one. So now you can see that it has given us the array one. Okay. If I try to put the type let's say type I wanted to check what the type of this array is that so you can see that numpy nd right so this is your numpy so we have converted your number to a numpy array okay so this is creating a a scalar value so this we are trying to give a scalar value to our numpy array and like it has been converted effectively to the nump MP dot nd array data type. So this is basically a data type as we have seen in python that we have a list data type we have a tpple data type. So similarly we have converted our one single element to a nd array or an numpy array. Right? Similarly we are trying to create a one-dimensional array on one dimensional array. So let's say array 2 equal to np dot array. Here we are trying to provide a list. So list is a one dimensional. Okay. So this is basically a list. So now the this operation what it is trying to give us we have a list of elements and we wanted to convert this list into a np array. This we are trying to effectively calculate uh uh convert. So let's say I'll try to say print array to so this is your list. So it it will try to give similar to a list right? But if you try to check the type it is npd. Okay. So now the question arises so by seeing these two things right this is also a list and this is also a looks like a list but it is not a list it is an nd array and that was a list. Okay. So we have seen that like the list are slow but your NP array or ND array are fast right right how we can see like until unless you don't know like uh you don't know the difference that how you can just come to the conclusion that why the this list are uh slow and your ND arrays are faster okay can can we just have a practical demonstration of it yes of course we can have so let us try to See, let's say I will try to uh make a list. I will say range and like let's say a 10 thou 10,000 objects I will try to have it. So you can see that I have made a list of range of 1 to 0 to let's say 1,000. Right? So this is a list right? Okay. So there's an time module time it module in in Python right which will try to give you the time and I'm trying to sum this list and it will try to give me the time. So what it will has has given me. So I'm trying to sum this x which is the list and the time taken to sum this list how much time it will take. So it is taking about 2.75 ncond or microcond I guess it is microcond right okay uh time it is taking right. So we are trying to compute some operations on your list and trying to calculate the time on. Okay. The similar part what we do we will try to compute the similar operations on a nd array and also we will try to see the time and we then we will try to compare which one is faster and then we will try to calculate okay because we wanted to check. So as in this deck says that the ND arrays are much faster for operations than how how how we can just uh see. So this is the practical demonstration in which we are trying to see that why ND arrays are powerful and and effective to use. Let us try to convert this to np do array and here I will try to give the x which is your list okay and converted it. So I will try to say that I'm at IQ percentage and I will say that NP dot sum. Okay. So I have used an inbuilt function called NP dots sum because for for the uh for the NP for the ND ND array your normal sum will don't won't work because like we have converted it to NP array and NP operations will work. So I'm using your input NP operations to work on it. Right? So I will try to put a Y. Okay. And we'll try to check right. So now you can see that it has taken 1.23 Microsoft. Right. So it has given the seven runs. Right. And the seven runs and in each run there were one lakh operations one lakh loops for it. But here you can see that the uh groups has increased right and the time also took it slow right. So this way we can prove that like your ND arrays are much computationally faster right and also effectively storage also. Yes. Uh Deepak what what you want me to repeat. Okay. So what I have done is like I have tried to make a list. Okay. This is a list and from in the list I have tried to add this list data. Right. Sum is basically inbuilt function where we are trying to add the elements of lists and we are and and this operation how much time it will take. So this time it is an inbuilt method in Python which will try to give that how much time that this particular operation is taking. And now we can see that when we are trying to have a list and we are trying to use a sum function. So this took about 2.75 microconds right and the similar operation we did it on the numpy array we are trying to convert the list into a numpy array and using this same time operation we are trying to calculate the time but here the operation was np dot sum instead of sum which is numpy dot sum right and it took us lesser time as compared to the time which the list operations took. So this is a a smaller number. It could be ranging to 10,000 or a lakh or or or or a million. Right? But here you can see that small demonstration in in terms of that how why this uh NP uh are faster right. So now what we have done is we have tried to create a ND array which is your one dimension. Right? Similarly we can create a 2D array also. Right? How we can create a two 2D array? Let us try to see it. So array three. So you can see so this is like if you try to give one bracket. So this is your one dimension. But here the brackets are two. So in the end also the brackets are two. So this is the uh how you can see that whether your data is two dimensional or three dimensional. So by using this or analyzing this you can easily. So in this two dimensional data right so you have your two rows and three columns understood so you have having your two rows and three columns and then you are trying to convert your data into your NP array right here right so let us try to see NP array three right so this we have tried to calculate it right so let's say dot right in your this NP array in your ND array. So you can just see the uh n dimensional nd right. So n dimensional or nd will give you the dimensions right it will give you dimension that it is two dimension right let us try to see array two it is one dimension. So array 2 was one dimensional as we already seen. So this is your 1DL. This is your 2D. Okay. I hope everyone is clear. Okay. Right. Okay. So let us try to see one array. Let's say so this is our uh 3D array. So let's say I will try to see a 3D array. Uh okay. And uh let me copy it. So basically this is a 3D array right. So in this 3D array so you have one two right. So this could be seen in this manner. Okay. So right. So this way you can just interpret it. Right. So let us try to see the shape also of this numpy array. So numpy array dot shape right. So this means that 2 is to two two rows two columns and three are the dimensions three dimension it is having fine right so this way you can just interpret it. So the third column is is for your dimensions how many dimensions. So here if I try to see the array two dot shape you can see that it is it is your one dimension node right okay and if I try to see your array three it is 2 + 3 fine right so this way you can just interpret right you can convert any of the data into your n dimensionals space or n dimension. Fine. Okay. Now another thing is that you have your data and you wanted to give your desired number of dimensions also that also you can do it. Let's say I have a list and I wanted to convert into a n dimension. Let's say uh I will have array 5 is equal to np dot array and I will try to provide a list. Okay. And in the bracket I will say and dim and equal to I will say five. Right? You can see that 1 2 3 4 5 are there. This mean the dimension is five. Okay. So this way you can convert any of your data type into any any dimensions right. Okay. Yes. So so Deepak uh these braces. Okay. So these braces is basically how you are trying to store your data. Okay. So, so basically like as I already explained you in this uh this thing right. So this is the data of one customer. This is the data of another customer. This is the data of another customer. Right? So you wanted to store each and every data in the form of a data. Array one consists the information of one customer. Array two contains the information of second customer. Array three. So now in this part let's say this is the information of first customer. This is the second customer. This is third. This is the fourth. So there may be n number of customers. So in n number of customers means n number of dimensions. Okay. And then you are trying to convert into an np array. And then you are trying to let's say uh store that information and process it effectively. So that is why there is a need of storing that information. N dim. Yes. So not end dim. I was also uh trying to is this ND min? ND min. Okay. So it is ND min. I also got the same error. Okay. Okay. So this is good, right? So you are trying to learn it. Okay. So there could be your your purpose was right that at the and and dim, right? But here they have used another nomicature. Okay. In that way like what you can do is just put a do uh this comma question mark and then shift and tab. Okay. So here you can see the dock string. Okay. So now here you can see that the in in your uh python data type do string. So the dock string was less but here you can see that. Okay. So so there each each and every explanation has been effectively uh explained. Okay. Okay. So they have also given you examples also. Okay. Fine. This way here right ending right. Okay. So so basically like this this like you as I already told you you cannot uh learn each and every or cram each and every method or function. But what but what what you can do is just understand the concept that why we are using what purpose we are using and and if you wanted to look it like how we can use it. Okay. So this also says that see also empty like once like zero likes returns the array of zero with shape and type. If you wanted to create a element or or a zero shaped uh array so np.0 zeros np do once so that also can be used. Let us try to see that array five plus array 6 is equal to np do once. Okay. Right. So let us try to see the documentation. I also don't know. Right. So we need to provide the shape. Okay. So np.15 np.15 right. So np.2.1 right? So only it will try to create the arrays of uh let's say ones with the shape. Right? So this is also very important when you're trying to create it. So let's say we try to give it a shape equal to let's say 2 + 1 and we'll try to give it. So we'll say print array right 1 + 1 so 2 + 1 right right so this way we can do it we can also give it like something like this. Now you can see that dimension has been changed right. So this is a basically we are trying to learn Python and in in this uh properties like what are properties. Okay. So now the quick check how to create a numpy array from a python list. So what is the option guys? A and numpy dot array numpy. Numpy dot convert to array or numpy list to array. So quickly guys tell me how to create an array from Python is yes a A is the answer right? Okay. Right. So important attributes. So attributes are the keywords that store basic information about the array. So we have the ND end. We already seen that shape. So item size, data, d type and size. So these are some of the important attributes. So let's try to see quickly one by one. Okay. So if I try to see array any array you take two, right? Array 2 is not defined. Array array three, right? So okay rod size so size is six right. So size is six in bytes. We are trying to calculate here the size of we try to take the d type. So d type it is int 64. So all the elements in this array is of integer 64. Right? Okay. So we can also convert this data type to some other floatingoint values also. Yes. The uh scroll the notebook above. uh 1. Yes. However, I will share this notebook. Okay. Right. Okay. So, this is your D type. Uh then your item size and item size. Let's try to see the item size also. array three dot item size right so 8 is the item size right so what item size does uh let us try to see it item size right uh it is your okay so this is basically we are trying to give the item size and uh okay then data right so this is having some kind of an information that this data is being stored at this memory location right okay so however these these functions uh you might not get encountered right so in in because shape and your D type. So these are all the attributes which you really care about. Right? So these is for educational purposes only that how the data is being stored and what data size is present in that data. Right? Okay. We already covered that how fast is that? So if you like uh can also see that why lists are are complex to storage also right so they take more space to storage as compared to your numpy data type. Right? Okay. So now we'll take the uh number of axis or dimensions. So I've already told you. So this is your axis. So here x is zero, right? Row one, row two, row three. This is this is basically your x is zero. And this is your access one, right? The columns we try to iterate through each and every column that is your axis, right? Okay. And these uh iterations uh through your rows, right? is the axis zero right iteration through your rows is your axis 0 and through your columns is your axis one right so this way we can just uh talk about it we already seen that like it could be a 2 + 3 it could be a three 3 +2 and the it it provides us the length right that it has two rows and three columns and it is a tpple which will provide you that the first element consist of the rows and the second is of the columns right size is the product of the shape that is 2 + 3 or 3 + 4. Right? It will give you the size that how many elements are present. Right? Right. And the data type as I already told you could be your integer 32 to integer 16 or floating point values. And these are the data type which is being stored in your numpy right and dim slide. End slide. Okay. Yes. So this is the end slide which provides you the number of axis dimensions of the array right. So two dimensional array has two axis which consist of arrange in rows and columns. So the axis zero which is basically iteration through your rows right. So rows along the rows we are trying to iterate the x0 and along the columns which is your axis one. So this way you can just interpret it right. Then item size shows the length of one of the item size in bytes. So as I already told you that that for the for the different floating point right so it is it is been trying to calculate for the float 60 64 uh like we are trying to divide it by the eight right and the size is eight is the elements 8 8 is the uh number of elements which are being stored in an array and the type is your floating point 64 then 64 divided by 8 and the eight is the item size and if the item size is complex 32 2 and uh the items are the complex 32 and the items height could be 32 divided by 80 which is your four right so this is how you are trying to store the items in your uh get the items size then your items have been stored in your numpy array right okay important attributes right so we see the shape size and storage and your item size and right so these already we have seen okay so uh I guess you are also uh done this activity Okay. Then the another aspect is that reshape, right? You wanted to reshape your data into some other type. Let's say it is 2 + 3. You wanted to flip it to 3 +2, right? So based on that dimensions, right, the underlying dimensions won't change. Let's say 2 into 3 is 6. 3 into 2 is also six, right? But if you wanted to change it to the eight, right? So eight won't match these items and you cannot reshape it. Okay? So let us try to see the r shape also. Uh so I'll try to see one what is the okay so let us try to see this numpy array. Okay. So dimension is 2 into 2 into 3. So 4 3's are 12. Right? Okay. So what I will do is I will try to say reshaped 4 + 3 right. So now you can see that the data has been stored into this 4 + 3 right the same data right this numpy array right which is having dimension as 2 into 2 into 3 has been reshaped to this right. So now the another important uh thing to understand and to uh see is that in this reshape you are trying to reshape but the original data still remain the same right? The original data still remain the same, right? Until and unless you try to give it that is reshape numpy array, right? Then and only then this change will take place. Right? So now from this 2 + 2 + 3 we have effectively reshaped to 4 + 2. Okay? So let us try to do some operation in which it doesn't match this reshape with your 4 + 4. Let's say I want to do it 4 + 4. Right? So it has given me the error that cannot reshape a array of size 12 to the shape of 4 + 4. So 4 + 4 is 16 but we are trying to reshape into a size of 12 which is not possible. Right? So this we need to make sure that our data could be only reshaped into that format in which we have that dimensions uh multiplied understood. So that reshaping is important. So item size so item size shows the length of one of the elements in bytes. So let us try to see the item size. Okay. So let's say this is your uh I'll try to take a simple example array_2 array enter right so this is your normal array right okay how many items are there four items okay if I wanted to see item size okay it is eight okay so basically first we try to see it how this eight occurs let's say I will try to cover D type. Okay, it is 64, right? 64 divided by 4. Uh, how many items were there? Uh, 64 and 64 items are four. Yes. So, four items are there and every item is taking two by space, right? And then we are trying to divide 64 divided by 4 2's are 8 right now we are getting eight right. So this is how we are trying to calculate the item size. So every integer type it is taking two bytes. So that is why 64 and we have four items. 4 into 2 is 8. And then we are trying to calculate this eight. So this is how it occurs. So I think you are uh you understood deep. Yes. No. So now we'll begin with the arithmetic operations. So do you want me guys to cover this operations in this class or next class? So let's try to uh see some operations uh such as NP, add, multiply, divide. Okay. Or we'll stop here. Okay. So next class. Fine. Okay. So, so what we have covered today let's try to give an quick recap and overview right so let's try to take the practical overview of this okay uh so today we started with our session with our the import module which is daytime and we have seen that how we can write a effective program using our import module which is your date time right so we have used a simple program then we'll try to cover your numpy So numpy we have started it. This is a numerical python and it is very effective in handling large scale data. So we can convert your operations into nd array which is your ar which is your list and that list would be converted into np array. So there we have seen uh that how this uh np arrays are are very efficient in terms of computation. So we try to use some np array and try to see uh this operations and seen that numpy array is is is faster than as compared to your list. Okay. So then we have seen your ND dimension which give us the dimension then shape it also gives you the shape then your once it will try to create your uh n dimensional array with with your with your only once with only once right and d type and your data and your and uh size right so we have seen that right so this is we have seen that but in the next lecture we will try to cover more uh in-depth operations of your numpy and then we'll touch upon your pandas also Right. Okay. So, let's meet tomorrow and uh continue this session. And before we end the session, please guys try to rate me uh five. And uh in the next session, we'll try to see more practical implementation implementations of this Python numpy and your data science applications. Right. Right. Okay. So, the array makes the data management very easy for the other data set as we have already seen in terms of your numpy arrays. Right. The characterist of an array uh uh which is a fixed size homogeneous data type and indexed right it's a fixed size which can hold definite number of elements homogeneous which can uh hold the same data type and index it is known to be a specific position which is starting from the zero index uh yes you have any questions Yes. Yes. We'll do that again. We'll do that again. Okay. Right. So, types of array. Uh then we have the single dimensional array, multi-dimensional arrays and jacked arrays. So uh single dimension uh which are stored in linear form and multi-dimension uses more complex data structures like matrices and zed array which consist of uh uh rows which could be different right. Okay. So then we talked about matrix which is a rectangular representation of uh rows and columns. Okay. Then uh some of the operations of matrix which include your matrix, multiplication, addition, scalar multiplication, subtraction, division. Right? So this we will try to see today. Right? So this topic we we were trying to focus and uh we seen that how we can create a numpy array. But the matrix multiplication, matrix operation we haven't seen yet. Right? So today we will work upon those and see the practical implementation and we'll do the hands-on practice on these uh operations. Then we touched upon fundamentals of numpy array. So numpy array is a free open-source library which is used for uh designing operations in scientific and engineering applications and uh it perform operations on multi-dimensional arrays and matrices. Then the advantages of numpy it is it is uh considered fastest faster than your python list and it also uh saves a lot of memory as compared to a list right and list are much slower. We have already seen a program in which demo in which uh we see that like the numpy operations are really very faster as compared to your Python list right and the processing of your large data set uh also in the case of data analytics and scientific research. So numpy is very essential right arrays in numpy everything has been estabated as an array in numpy right so we have the rows table format in which we have a rows and column structure and this could be seen as a multi-dimensional system in which these particular uh structure could be repeated n number of times okay with we call it as a nd array n dimensional right okay so consider this as a uh uh information which is being stored in the rows and the columns of one patient or one student or one employee right and it could ends to multiple records then we need to perform some analysis on these records so that we could gain some insight and perform some data analysis right so then uh important attributes of the ND array we saw that data and dimensionals shape, size, d type, item size. Right? So these were some of the practical implementations and dimensions. So provide the number of axis dimensions in the array. The axis zero along the uh vertical position along the vertical right. This x is zero and x is one is really very helpful which in which we are trying to uh take the uh arithmetic operations along the rows along the rows all the rows inside a column and this is along the columns all the columns right so how you interpret it's up to you right I interpret it in this manner x=0 means that along all the rows like it It is vertical and axis one it is it is horizontal right. Shape uh you can have the shape uh the size of the dimension. Then size right it is the product that number of products like how many products are present in a particular array. Then d type is the dimension which type of data type it is storing mean is numpy integer float or boolean right? So the kind of a data data type item size we calculate the size which is needed to store that information. Okay. Then attributes. So this we have seen reshape. So we have a numpy array like two 2 + 3 right. So we can reshape into 3 + 2. So earlier the number of rows and the columns were 2 is to 3. So now it is 3 is to 2, right? So this means that we can reshape any array to a particular size but the constant is that the numbers could be multiplied and the result should be same that is 2 into 3 is 6 and 3 into 2 is also six right. So dimensions can be changed but uh the overall dimensions should match to the original uh uh array. Right? Okay. Okay. So this up till now this we have seen the arithmetic operations in numpy. Okay. Uh okay. So let's try to see uh this uh uh session which we have already covered. Okay. So what we have covered yesterday is that like we have imported date times from date and this is a daytime module and from that module we are trying to import date which is a class right and we are trying to take the birth date and here day month right and also we can calculate the today date by using a today function. So this is a function which will try to give us the today, date, right? Then we have seen a program in which we take the input from the user. Okay. And uh we take it as a year, month and day and we try to calculate the uh convert into a birth date by using this uh date uh method or class= to. After that we have written another function which is your calculate age which takes the argument as a birth date and we have the current date and from that we try to write some logic okay the function which we'll try to calculate the age right so that function of calculation of the age uh you can design your own algorithm also right mine mine would be wrong and I have demonstrated uh specifically to to give you the structure that how the program is polished. Right? How the program has to be written. The program has to be written in such a manner that it should be modular, it should be concise and it should be written so that next time if you wanted to do some modification, you can easily do it. Right? Okay. So let's say we come up with this first statement. We calculate the uh we input the year, month and day by using our input function and we convert it into the integer because in the input function uh everything is stored as a string and since we are trying to take the year and we are trying to do some arithmetic operations so we need to type cast it to the integer right. So we have taken year month and date from the user and then we have used this date function or class to uh convert into a birth date into a particular format. Okay. And then we have also tried to use the try and accept block in this particular block if some error occurs. Okay. It will automatically go into the accept block and it will not give us the error and it will try to give us the print statement enter the valid date with the exception right what exception we'll have try to have this is the standard procedure because like nobody likes errors on their screen on their websites on their pages right so it is if you are trying to work as a developer so it is better you do the uh uh exception handling of your code. Right? So this is very important and very necessary to try to validate the cases. Okay? Then we have written another function which is your get age and we'll try to take it as a today date and we'll try to write some logic. Okay. So this logic according to you may change right. So I have given you a simple logic in which we are trying to uh subtract the year- wise date and here we are trying to check the month date is and the and the today's date is less than or greater than we can just write one statement that uh we can uh subtract one year or add one year right so this is only for the demonstration purpose so this logic might change right okay then this is your main function where we are trying to call is get birth date. So this is your get birth date function which is written over here and uh the birth date will get to be get age and then after that like your age gets printed. Okay. So this function we have called here in this particular block right. Okay. So this is the idle path in which we we can write a function and if these functions uh increase over time and you are building a project then these functions has to be written in a separate file. Okay. So this is like what we have seen then we touched upon your numpy. So numpy is your 1D array right 2D array right. So we took a 1D uh like a scalar quantity. Then we took a list. So this is a list and we try to uh convert into a numpy array. Then we are also seen your list right and we tried to do some operations into your list that we are trying to sum all the elements of a list. And it took us about 2.75 microconds. And uh when we did the same operation using your uh numpy array that it took us 1.23 millconds nic right okay then we talked about NM and uh shape right okay then we also converted once np.1s np.0 zeros, right? So these are also some of the methods, okay? In which we can create some array, right? Okay, then reshape is also another option in which we are trying to reshape some of the elements, right? And DT type item size. So this we have already seen, right? So up till now any doubts, any questions you are having, just let me know or we'll proceed with the next part which is your uh multiplication, subtraction, additions and your other operations onto your num. So uh arithmetic operations in numpy. So there are various types of operations which is your numpy dot add right. Numpy dot multiply numpy do.tract numpy dot divide. Okay. And numpy dot remainder and numpy dot mod. Yes. Uh so basically you got your error in get age program. Why? because this error you got it because you haven't imported this thing right from date time import date okay so it is uh when you are trying to encounter some error just look at the error first your name date is not defined basically you are trying to access the date okay so date is an external uh library okay which we have imported right if you didn't run this date so your program will not work and it will give you error Right? Okay. Try to run this line first and then you are trying to uh run this thing here. Okay. So these are some arithmetic operations. Arithmetic operations uh involve your matrix multiplication matrix uh this thing and which involves your element wise addition element wise multiplication element wise subtraction. Okay. So let us try to see uh this thing. I'll create a new notebook which is your eighth session. Okay. So first thing we need to do is like import numpy as np right. So this is the first thing or we are trying to import some library. So whenever you are trying to run so this statement you are trying to call an external uh what we call package or module. Okay, for your use. Okay, so then let's try to uh make some array, right? Okay, so let's say I wanted to make some uh 3 + 3 array. So np do range, right? So arrange is similar to let's say I wanted to create the array from let's say 1 to uh 3 + 3 1 to 10. Okay. So 3 + 3 I will try to create it. Okay. Right. So I will try to create array. Right. So 1 to 9 it is already created a 1D array. So I wanted to reshape. I will reshape it to 3 + 3. Right? And I will try to give it as array_1. Right? So now I will try to print this thing. So now you will see that. So this I have made a array which is your with using this arrange method. Arrange basically it starts from one and it ends at 9 n minus one. So we have seen the range function. So it is similar to range function. Okay. So what why I have done is instead of typing again and again so I've created a range function and created an numpy array we have reshaped the size to 3 + 3 okay I can even uh try to do something like uh array 2 np dot uh once right and right np do once and I will try to give the shape also let's say I wanted to give it as 3 + Right. Can't interpretate is equal to right. Right. So this is also created as a np. Right. Okay. Right. Okay. So uh I have created two arrays. One is your array one and array two. Okay. So let's try to do first addition. Right. So, array_1. Right. And I'll do np do add. Right. So, now you can see that the element wise operation has performed and you have tried to add the np. Right? So, 1 + 1 is 2, 2 + 1 is 3, 3 + 1 is 4, similarly 4 + 1 is 5, 6, 7. Similar to that, right? So, similarly np.ubract. Okay. So I urge you guys to start doing along with me. Okay. Even though I will try to share this uh Jupyter notebook. Okay. NP dot subract. So similarly try it with multiplication and division guys also. Right. So this is addition. This is your subtraction. Then we have division or your multiplication. Okay. So this is your multiplication. And then we have division also array. divide divide division right okay then we have mod also so it is flow division basically right so we have seen the flow division right so it is basically your uh uh remainder kind of a thing which we have already seen in case of your uh this thing right so more than alias of remainder okay right okay okay so let us try to see this arrow one uh yes hina okay okay sorry I'll go slow okay so first uh what you do you try to make Um yeah yeah yeah I can understand yes yes yes so I will try to share each and every line here first I'll keep that in mind from now onwards I will try whatever I write I will write first okay okay so I'll give you a amount of time okay to write and practice okay subtract multiplying and divide in mod. Okay. For right. So basically what we have done today is that we are trying to make an array. Right. So this is an array and a range function. We try to list me the elements ranging from 1 to 9. Okay. Starting from one and ranging from uh 10. 10 up till 10. 10 will not be coded up till 9. Then I'm trying to reshape that particular array into a size of 3 + 3 and automatically the it has been reshaped and it is a uh this thing uh uh 3 + 3 matrix. Okay. So deep it is not a range it is single R. Okay. Basically it is not np.t range range is single single. you have a I am trying to write in the chat again just copy this and try to write it okay then we have made another array which is your np once which will try to give me only the ones right and it is of shape 3 + 3 right okay then we have two arrays one is your array one which is a 3 + 3 matrix y12 in array uh after addition subtraction 1 2 1 2 Okay 1 dot okay so this is dot basically the floating point value so this is the floating point number basically right so when we are trying to have this ones the shape so we try to define which is the d type so d type can be defined as in in 32 right uh np in32 we can define Right. So if I try to define it now you can see that like earlier by default it was floating point value. So that is why it was getting one dot two dot. Right. If I try to now add it you can see that. Okay. So so by default it will take a floating point. So now you had defined the data type. Okay. So let's try to give this to you also so that you can try it. Right. Okay. Right. So then we have subtract. Then we have multiply. Then we have division. Model. Model is basically we are trying to uh like trying to calculate the remainder. Okay. So this is array one and this is array two. Okay. So this was one array. This is one array. So we are trying to divide it and find the remainder. Okay. So so that is why like it is giving us the modulus. Modulus we have seen in the Python itself only. Right. So this is what what we get got. Okay. Okay. So okay so another uh 30 seconds okay yes the mod function okay so if you are having some trouble with the mod function so just try to uh write and try to see the documentation okay returns the element wise remainder of the division fine it computes the remainder complimentary to the slow divide function it is equivalent to the Python modulus operator. So this mod modulus operator we already seen, right? Okay. So so it computes the remainder. Okay. So x1 module x2, right? Okay. So fine. So we have one array and another array array 2 and uh like it will try to do the element wise operation and it will try to divide x1 and x2 element wise remainder of the question. Right? Okay. So if you don't understand any of the function okay maybe there are n number of functions okay so so so it is better to follow the documentation so this is the official documentation of your numpy even though you can get it on the internet also but it may be updated uh uh it may be updated right like this is the latest one okay with with some specific versions it is updated and on the internet you get and information which could be outdated. Right? So, so this is the best practice where you don't even if you don't have the internet this also still works. Okay. So, so that is the main advantage of using this documentation. Fine. Okay. So, shall we proceed further? Yes. No. Or you want another 10 seconds. So, now we move to the conditional statements in numpy. So condition statements basically are two which is your np and np dot select right. Okay. So let us try to see the conditional statements where np do is basically your condition and your true expression and your false expression. Okay. So let us try to see this np. First okay np. Okay. So we already have an array array enter one okay right so what I will do is I will try to write np where okay so before going to uh this mpware okay so let us try to understand the documentation yes sort of you need uh Mod needs explanation. Okay. So you wanted to explain uh you want me to explain mod again. So basically mod you have seen right? Mod is basically it will give you the element wise remainder of the division. Okay. So let's say there's one element this a one right another element is let's say two. So it will try to first divide it and find the remainder. Okay. After the remainder right it will it will try to give that element wise. This is what is trying to do from one another element element wise because it will go it will divide it and it will return the remainder third element it will divide it and it will remain the remainder. So this is what it is trying to do what the mod operation right okay so you try it out and just uh uh follow the documentation and you will try to understand so what is this mod about if not like then let me know again I will explain you in detail okay because we have lot of functions and everything we cannot cover uh due to time constraint right okay okay right okay so let's try to see the np where so in order to understand npa there so we will Okay. So this actually you get used to this this operation or this documentation. Okay. Because whenever you are trying to work on it. So these are the parameters. Okay. And these are the uh returns like this will try to return it and and you you will try to see the sample also here. In some cases in some cases there is written in some cases is not written. Okay. Right. So you just need to follow this. This is the official documentation and wherever you go to the official documentation this uh this uh dock string and that dock string is more or less the same one. Okay. So this is given for your ease. Okay. So let us try to see this. Okay. So basically this has a condition right np.ware it is a condition. Okay. which is being performed on a particular uh this thing uh uh array right. So it is performed on a particular array and it will try to return me either x or either y right it returns the element choosen from x or y depending upon the condition. Okay let us try to see this operation. Okay. So from this array, this array one, right? I want to write the whatever elements are even and whatever elements are odd. So I what I will do is I will try to say array_1 module 2 equal to zero. First every element will try to divide by two, right? And if the result is zero, I want to print it as even. Else I will want it to print as odd. Fine. So what will happen? One will get as odd. Two will get as even three will get as odd. Right? And four even similar to that. Right? So I want to make that the elements where this is uh this thing uh like divisible by two it will provide me even else it will provide me odd okay let us try to run this now you can see that right odd even odd even odd even so this is what we are trying to see we are trying to make a condition right so I'm typing here and the array one we have already there right so array Um I'll try to so this this statement you already run it right so I'm trying to run it uh give it here again okay and you need to reshape it right and then after that you run this np right fine okay so this is very helpful in terms of when you are trying to find some logic okay so let's say I try to change some logic. Okay. So can you think upon it? Let's say uh no no you don't need to run rearrange and reshape again. If it is already been run, it is for your convenience I have given you because uh like uh right if it has been already been run okay then it is fine. You don't need to run again and again. it is your convenience like because if you are trying to uh run uh like uh you haven't run that in earlier cases I found that like the lumpy was not run or date module was not run so that in that case I shared you so that like the you don't get any error that you uh error one uh is not there right so that is why understood deep so this is your data type uh unsigned integer we call it right so this is your data type unsigned which is basically a string or unsigned okay okay so uh uh I'll let you know okay so what is exact but but to my knowledge so it was unsigned kind of a thing right so I'll let you know on this right okay so don't bother upon this okay so the only thing is that like just concentrate on the logic and the building okay okay so there like let's say in this particular thing I want the elements u uh let's say uh to become for the even I wanted to make it zero okay that also can be done okay so I will say zero or one yes okay so whatever events are there it can be zero which can be combined as zero else it could be combined as one right so this way also you do it or you can just type the where condition where you can say that array_1 greater than three make it a zero okay else one similar to that right okay right right so this way also you can do right right so this was your np so another part was np do select okay so let us try to see the documentation first Okay. Yes. So this signature the np dot select. So try to understand this. Okay. So these are the parameters. Okay. So the parameters means it is a function np dot select and it consists of your condition list choice list and default. Okay. So there could be many parameters. So sort of uh basically this is your model function. Okay. So if you have attended your Python session. So basically what it does it will try to divide your number by by two and it will give you the remainder and if the remainder is equal equal to two it will return you true or false. Okay. So basically this is a model loop function. Model operator we call it. And uh this array one is basically your array. the element first element will try to come and second element third element right so this way it will come and it will try to give you the remainder if the remainder is equal equal to zero then it will try to give you like the condition what we have written right yes divisible by two yes and gives you the remainder actually it is divisible by two and equal equal to uh remainder the the divisible by two and this mod operator will to give you the remainder right Okay fine shortcut of signature signature shortcut basically right I have explained you that it is question mark and then shift and tab right in earlier session also uh yes shift and tab yes question mark question mark shift and tab okay so we'll get the signature so this is very useful you don't need to go again and again on the internet and search for it right okay so these are the parameters you have the condition list, choice list and default and it will also give you that it is a list of boolean and arrays what data type it it demanding right and if you try to go through it carefully you will automatically understood understand like because because the thing is that if you are a programmer if you are a data scientist most of the things you need to do it on your own nobody's going to tell you okay then you need to understand it you need to follow the documentation maybe I'm trying to tell you one function okay I'm trying to tell you earlier also I you said said it that I'm trying to tell you how to drive a car. Okay. So, I'll try to teach you uh one uh car and rest you can take it on your own. Okay. So, this is the method. This is the way I'm trying to tell you that if in future any method you have encountered and you don't know about it, you just need to go for the documentation first. Understand it. Okay? Right? Tried it out. Then you need to uh if you didn't follow, then you need to go to the internet. Okay. So this is the practical scenario. Okay. If you just follow. So this uh it will give you the condition choice list and default. Okay. Then there's an explanation also. Let's say np.t range. So this is 0 to six elements one. Uh right. And the choice condition is that like if it is greater than three put it X. If it is sorry it is less than three put it X. Leave as it is. And if it is greater than three then multiply that elements with two not two sorry it is power it is basically power right. So if it is less than three make it as x same nothing will change and if it is uh greater than three you make it as a uh that element as a power of two right so let us try to do in practice right np do select okay so uh I'll try to copy this example itself from the choice or condition based right okay So here we will try to give you condition list and your choice list right choice list. Okay. Right. Okay. Okay. I will run it and it will give me error. Okay. So then you guys has to tell me what is the error because this is also a part of your uh data entries to identify the error and try to let me know that what went wrong. Okay. because most of you are encounter some of the errors. Okay. And in that case like you just need to tell me that what sort of error was there and how we can correct it where Python implementing logic array is missing. Yes. X is not defined. Yes. Yes. Where the array is missing and uh in this array X like it is trying to say that array is not defined. Name X is not defined. So we just need to put that operation on a particular array. Yes. Yes. So what we will do is we will try to see those this array array one. Okay. Then we will replace this X with your array_1. Okay. And here also we'll try to replace your array_1. And here array_1. And here also array_1. Okay. Okay. So this is this the uh basic uh uh uh motive was to like understand like what went wrong and the thing is that like we just need to make sure that we don't follow blind bracket right we just also uh see that what the error is that and how we can correct the that okay so here you can see that if it is less than three keep as it is it is less than three yes okay and if it is greater than three you can see that 4 raised to power 2 5 raised to power 2 6 raised to power 2 7 8 and 9 right so this you can have that and P dot select according you you can make a condition according to the choice right and if there is a default okay so I will put some default equal to let's say four right right now you can see that right so three has changed to four right so because three was not mentioned and default has been changed to four. Right? Okay. So this you can just understand and try it out because I have trying to tell you the method. There may be numerous types of methods and numerous parameters. Okay. So in later stages uh you can see as we see the parameters go on increasing there in in one function there could be 10 parameters and it is very difficult to explain you 10 parameters in in due to the uh time constraint right so I can explain you at max two or three but the rest of the parameters according to your requirement you can see and you can try it out. So the purpose is basically your you have seen this uh where right? So it is also basically we are trying to make some condition and we are trying to make some choices right choices in the sense like based on this condition. So here we are trying to put only one condition here we are can make multiple conditions understood right? Okay. Okay. So we can make multiple conditions according to that multiple condition we are trying to make it that that this is our choice list make it uh according to that and give me that array. Let's say practical implementation you have a long list of array right with the with some tax calculation right let's say salary of the employees okay so you have a 3 +3 metric of the nine employees you have the salary let's say these are some salary numbers so here I will make a condition according to that condition which is being uh taxed right let's say it is like fall under zero to uh uh like uh uh 50,000 that this much tax is being uh or five lakhs this much tax is deducted 5 to 10 this much tax 10% tax is there 10 to 15 20% right and rest on this this thing right this array is a range so when you are giving greater than three how will this function is check each number uh sort of basically like it is designed design in such a manner that it will try to check each and every ele it will iterate through each and every element. It will try to check it and based on that it will try to give you that condition. Okay, try to follow uh I I understand your point that you are trying to say that like this is the array whole completely and it is how it is trying to check. No, it is trying to check on each and every element. So that is why this LP select method or function which we we call it it is designed in such a manner. It will try to act on individual elements, right? Okay. So, so that is that is uh uh like it is it is it is designed in in that manner only, right? Only every element it will try to uh uh follow that choice condition list or choice list and do the operation, right? Okay. Okay. So, rather uh you got that point, right? The salary thing, right? So we have the uh structure in which we say salaries of employees and then we can make some condition and choice based on that and based on that condition choice we can just uh care that. Yes. Yes. Okay. Okay. So s you can just see the documentation would be any number of parameters uh uh used in this operation. Okay. Okay, it is a scalar. If the element inserted and output where all the condition value evaluates to false, right? So here basically if the this condition is not met, this condition is also not met. Right? So there it was zero. So we don't want that the zeroth element would be present. It would be default to four. So that is why we have made it default as four because earlier if you don't put default, it will try to give you zero because this condition is not met on uh because it's less than three and greater than three. But equal to three we haven't got it for that purpose we have made it default for okay so is this correct is this fine is np np dot select right okay so these are some of the methods in which you can just make your programming better okay and and try to learn programming understand programming okay don't cram it okay just go through the official documentation and try to uh learn each and every or or try to follow the steps which they have given Right. Okay. And try it out. Basically the more you practice the more you will try to understand this that okay fine to braha. Right. Right. So now we move to the statistical functions. Okay. Statical functions you already know that like some of the statical function which we have np do. Max, a min, a max, np dot mean, median, standard deviation and variance. Okay. So now I will not do it. Right guys? So you need to practice and just let me know okay try to create an array okay and try to use these functions and first try to understand what does it does do right let's say you are trying to do np.min and just question mark and shift and tab and try to read the documentation and then understand it okay the whole purpose of this this activity is that that I may not be present every time with you okay you just need to drive your own car okay I'm trying to give you the control and I'm trying to say that okay Go ahead, turn left, turn right. Okay, so this way you need to learn it, right? Okay, so I'll also try to do the statical function along with you. But your job is to look at these statistical functions and try it on your own. What is mean, median, standard deviation and variance, right? Okay. Okay, guys, are you ready? Yes. No. Is it fine? Okay. So, let us try to have that array again. Array one, right? Okay. So array np do a main right first I will try to see the documentation okay so now you see so here in the npin so we have the a right so which is the a is your array and axis is by default it is none out is none if initial where right so these are some of the arguments we can use it right but for for simplicity we will only try to use a this only or at the max we can use the x's also right okay so then if you try to see that these parameters okay right and implementation okay so let us try to see so this doc string has little implementation or little uh docs string you can see that in some cases it is it is given with an example in some case it is not right okay okay so let us try to see that and try it ourselves amn okay right so it will give you one so this is your one basically Basically it has given you as this is your integer this thing and it has given you the first right. Okay. So if I try to see the x is equal to zero. So remember x is zero across your vertical x is one horizontal right. So now x is zero. Just see it will try to give you minimum values across 1 2 and three fine right okay got it right and if I try to give x is one that is your 1 4 and 7 1 4 and 7 across vertical lines earlier was horizontal this was vertical okay so this you try it on your own x is one and I'll try to give it x is zero also just quickly try it with a max also right and np dot So now the question is that where it being it is being used right the question is that these are some of the uh data data in this sense uh maybe uh data in this sense the ages of your students. Okay. So this is particular row one row, second row, third row and this is your age. This is your let's say uh age and it is could be your class right uh and it could be your u u height right so basically you wanted to uh give me the names or the student where the age is less from that particular column from x is zero or x is one right so this way you need to find it out so this is a very good method in which you can find that age uh a min or a max. Right. Right. So then we'll try to compute the mean, median and standard deviation, standard deviation and your variance. Okay. So So guys, what is the difference between mean and median? Anyone? Yes. Mean is the average, right? So you can get the average and your median is the middle one. Absolutely. Absolutely. Sudir, you are correct. So mean and median basically is the middle one, right? So basically when we trying to uh yes yes column wise or row wise and anything any interpretation you make right so I have termed it as as a vertical and horizontal you can term it as a column wise or row wise so what is best for your uh memory right you can just try it out right okay okay so the inter the interpretation has to be uh correct basically right. Okay. So the mean right and then uh which is the average sum of all the values divided by the number of values right which is the mean right so here you can get all the mean information where all the elements are being summed and divided by the nine right and you get a uh number which is five right okay so this is basically your your mean and median is the middle one value right if the if the numbers are being there and the odd one right so odd one is the middle one right else in the even right we divide by two okay so this is your uh median okay so we'll try to calculate the median also and p dot median right and then we try to get error one right right so it is also five right so mean and median is basically the same here in that part right okay so can anyone tell me what is variance Yes. Anyone? So variance is basically like how far the uh data points are being separated. Yes. Yes. Distance between the data point. Yes. Very you are correct. Right. So how it is being calculated? First it is being the average calculation of the squared differences from the mean. Right? So then we take uh yes standard deviation. Yes. Yes you can say that right. So standard deviation is basically the square root of uh uh variance right. So this also way you can do it right. So, so now here np do variance and we can calculate array one right. So this is your variance right? And uh if we try to calculate the standard deviation standard deviation is also str and p dot right. So this is your standard image right. So these are some statistical properties or statistical operations which we you need to uh like perform when you are trying to do your data analysis. So data could be in the form of data points what we call it right. So that points could be let's say we are trying to record some uh uh temperature recording or or sensor recording right wind pressure right rainfall right so there you need to find what is the average rainfall right what is the standard deviation how far the data points are being separated so there you need to understand it what is the mode is also there the highest point we call it median mean right the average right so these thing you just need to understand and then you need to apply based on the analysis you will do. Okay. And then max minimum right along the rows along the columns right. So this you need to do it right. Median uh yes mean and median should be same. Yes. Why it is being same? You tell me. Because the thing is that if we try to calculate the mean of these numbers, you sum it all and try to divide by 9, it will be five. And your median is also the middle one. Middle one is your five because it is an odd number, right? So this you need to understand it, right? Okay. So I guess it is self-explanatory. So median and mean because basically the middle one is your median and mean is the average. Right. Okay. So that is why you are getting the output as five. Fine. Okay. Everyone done? Yes. No. Standard deviation variance. Shall we proceed further? Yes. Okay. Great. Okay. Right. So these uh we have done it. So minax you can try it out. So these are also some operations. Right. Okay. Right. Then how you can calculate the variance of elements in numpy array? So np dot median np dot where np dot mean np dot sum. Okay. So b yes yes yes yes you are all correct. So np.variance is the method to calculate the variance. Right. Okay. Indexing and slicing of numpy arrays. Okay. So this is very important guys. Right. So so how we try to index and slice your numpy array. Right. So this this you need to understand. Okay. So based on this I will try to give you one activity also right after the break. So let us try to complete this uh theory part first and practical also after the break I will try to give you a programs or or statements in which you just need to give me the answers. Right. Okay. Right. Okay. So what is indexing? Indexing we have already seen in the numpy in your python also. Right. So index we can access an element based on that index number. So index also always starts with zero. Okay. And that property follows your square bracket in which you can put a number right. Let's say 0 1 2 3 and we can have that particular element indexed right. Okay. So index like address uh no not basically a address. It is basically like your key you can say that like basically the address is different. So you cannot call it as index as address. Every uh element which is being stored has a specific address and you cannot say that like that address and that index could be same. Right? Okay. Index always start from zero where that element has been stored. So you name that as a uh index. Basically you are trying to store it in a stack like you have a stack of shoes in which you try to place your shoe at the bottom then after above then after after likewise right from top from bottom to top right you go on increasing and from the lower one it is your zero index and then it starts rising from the top okay and slicing is basically you extract a portion of an array let's say you wanted to start and stop and you try to give a step function also right so this is basically your slicing Okay. So let us try to uh see the indexing in in practice. Okay. Right. Uh so let us try to see uh the same as your a r one slicing. Yes. Uh slicing is basically like basically in Python also we did did the slicing right. Yes sir. Yes. So basically median right median is what is basically the middle element. Okay. So here you have given elements from ranging from 1 to 9. Okay. So you take four elements here four elements here. Okay. So what is the middle one? Five. That is why the result is five. Understood? So the middle one is your Now you try to give it as eight. Yes. Even count. No, it is not an even count. Even count will give you 4.5. Okay. But your odd count will give you the middle one only. So that is your median. Understood? Yes. No. Fine. So that is what is the median is. Okay. Right. Yes. Uh any doubt? Okay. Fine. Right. Okay. So what we are trying to do is we are trying to do a slice. Right. So let's say this is your array one. So I'm trying to print the array one again. Okay. So I wanted to get the excess of zero. Right? So zero index means your first row. Okay, this is your first row and if I try to write it one here, it is second row. Okay, uh third row. Okay, because indexing always starts from zero. Okay, three is not there because index is out of range like for the x the zero with the size three 0 1 2 up till two will be there. So third element does not exist or the third row does not exist. Okay. So this we have already seen in the Python when we are trying to have your index right. So negative index positive index so BFC okay so let's say if I try to give minus one what will happen guys yes no it will give give me the last entry right so indexing start from zero and indexing start from -1 right so this we already seen right -2 minus right and minus 3 similar that right okay so this we have already seen right so this is your indexing right fine so zero indexing Right? We have seen it. Okay. Right. Okay. So if I try to see the 0a 1, right? 0a 1 or 0 comma 0 would be the first element. This is first 0 comma 1 0 comma 2 1 comma 0 1 comma 1. Right? So this is how we can get that elements. Right? Individual elements. So this is integer element one if I try to give one right. Okay. So I'm trying to type it again for the sake of simplicity and uh for the future also. Okay. So take second row right fine understood this point. Okay. So this is how we try to index the rows. Okay. Now come the slicing part. Okay. So let us try to do the slicing. Yes. This is indexing. Yes. Yes. Right. So now we'll try to slice. Look. Slicing means we are trying to this this is particularly 3 + 3 matrix. It could be 5 + 5 also. But I wanted only these last four elements 5 6 and 8 9. Okay. I want only these last elements. Okay. So there we can slice it. There we can slice it. How we can slice it? By using your row one, right? So this is your nom right? I'll try to this thing. Let's say right you want to have zero up till two. Right. Right. And inside this zo uh sorry not this zero. Right. Uh I will row to row. Okay, column group column this I will try to do it from here I'll get one understood this point I'll repeat it again guys okay because this is a little confusing okay so let's say this is your array right I wanted to have the element let's say 1 2 4 and five so 1 2 4 and type first you need to have only first two rows. So first two rows 0 to two. Okay. So let's say 0 to two. Okay. You will get first two rows. Okay. From this first two rows you only want to get 1 2 and four and five. Okay. You put a comma. Okay. Then you are trying to have columns. So zero column till second first column. Okay. You zero and first right. But here you will try to write zero and two. Okay, you will not write one because if you try to write one only the first element entry will come. Okay, so that is why you have writing zero. Okay, if you try to write one 1/4 fine. Okay. So let me try to give you another example where you want only eight and 9. For the 8 and 9, what will happen? Array_1 Okay. 8 and 9. 8 is 8 and 9 is happening in this thing in the row second. Okay. So I will say only row second I want right 8 and 9. After that I only want the columns from 0 1 and two from one onwards. Sorry. 8 and 9, comma. Actually, I forgot to put comma. Yes, understood. Yes. No. Do you want me to repeat again? This is the normal structure. Row versus row, comma, column. So, you can slice any part of it. Right. I only want the fifth this single entry. No, it is empty. Sud your answer is 0 1 to 1. Okay. 1 2 1. So 1 2 would be you're right. 5 and 8 is there. Okay. So you are having five and 8. So basically first you want this and uh 01 5 One second I'll try to do it go into the next topic which is of file handling topic right so this is also very important okay it refers to the process of working with files stored in computer file system right so numpy array can easily save to and load load from files using the functions called np.save and np.load. Okay. So these are two functions which is np.tsave and np.load which is uh to load some file and to save some file and to read files we have load txt and gen from txt. Right? Okay. So these are the two operations these four four methods we will try to use it. Okay. So we have a CSV file in which some data has been stored. We wanted to load it and and then we wanted to perform some operation and then we wanted to save it and to read from files there is load txt and again from txt right so let us try to see this in practice okay right so let us try to see that we have np do load txt so first we'll try to see the documentation okay so now what the doc string and the signature says is the load txt takes these many parameters and by default some are none some are zero some are false right right and it will try to load a data from a txt file the file name parameter okay could be a file could be a string could be generator right so some extensions is also given right right so this kind of an uh dock string is very useful in which we are trying to uh work with some files and that file format is not supported There you can check the parameters first and whether it is supported and not uh you can just check right if the file name is extension is gz or dz2 the file is first decompressed right so it is trying to say that okay we have the deliminator which is string optional right and uh skip row column okay so these are some of the parameters you can just try it out fine okay then examples Let's say uh we have your string input output we try to load the txt right okay in our case what we will do we will try to uh make some file and uh from that file we'll try to make some deli and we will try to say that it is retype string right then we'll try to load it right okay so let us try to make some file first okay so in this uh particular jupitter notebook so I have made a sample csv Let us try to add sample dot CSP. So these are some speed sheets before vikas mon right. So I've added already right. So how I can make this file? So let me try to give you this file directly. Okay. So you can just modify it. I'll just uh give this file sample CSV to everyone. Okay. Okay. Okay. this this file has been released to you. Okay. So what you need to do is you just need to make sure that file is present in that folder where you have this notebook stored right. So I have this 8th Sunday session 2. Okay. And I have the same directory in which there file exist. Then I will try to say sample dot CSV. Okay. and it is deliminator equal to comma right and d type is your string okay so I'm trying to load this uh and try to store it in the array dot let's say three you can see that okay right and when I try to print this array three you get the values. Okay. Right. So, some unknown characters are also coming. It is due to that uh we have made that file and the f file format was not proper or or it is basically if the file is proper and the contents are proper then you can make this error. You can correct it yourself also. You can fiddle with the sample CSV you can create your own sample file and then you can just make sure that all the content is being loaded correctly. Okay. So this is only for your demonstration purpose that we can also load the file uh that is CSV file and then we can just uh uh have some options right that is the comma separated. So why we have used the delinator? Because it is a comma separated and we have specified the d type as string right. Okay. So we are trying to load this uh file. Okay. Right. Okay. So you can just try the other option also right skip value skip row something like that and try it out and you can just uh fiddle with the parameters. So these parameters are have are a long range of parameters. Okay. So that is the case. Okay. So this is how we can uh uh get the file. Okay. So another method is np dot gen from txt. Right. Let us try to check this also. These have other parameters also. And uh load data from a text file. This also loads the data from the text file. Right? And the only thing is that it will try to give the uh uh handle it will try to load the data from the text file and the missing values are being handled. Okay. So basically the difference between your load txt and gen from PhD is that like that doesn't handle your missing values but it will try to handle your missing values automatically right some of the values are not there not present okay it will always like it will try to handle okay so this is the common practice like when we try to get some data from the client or some external resources so they there it might occur that some of the data is missing and you need to correct that data Okay. So either you will try to use some where clause in which you try to filter out where the data is is not there. You try to put a default values to it or either you at the time of loading itself only you can just specify and try to put some value which could be a default value to the missing value. Right? Okay. So this is your uh basically gen from txt and this is also a sample CSV and we'll try to put the eliminator equal to comma and d type is also string right and let's try to put it in array Right. This 10 we got it right. So just try to skip one of the values and try it with the CSV and try to compare this those values. The only objective of uh doing this activity is that you got this step that how we can just load our data. Right? Okay. And then data could be complex and it could be challenging sometimes. So in that case you just need to make sure these functions exist and you know these functions. Okay. Right. Okay. Right. Okay. So the next operation is that we are trying to uh save the numpy array in a text file. Right. So let's try to have that array one. We have already have this array one. Right. Okay. So I wanted to save this. So what I will do is I will try to say np.ts save txt. Okay. And I will try to put a name of that file. Let's say sample file one txt or I will try to say csv. Let's say right. And I will try to give this array one. what I wanted to save. I wanted to save this array one into this CSV file. Okay. Then deliminator. So deliminator is basically your CSV file is always your comma separated. Okay. So I'll put a comma. Okay. And then I will try to save it. Okay. So what we will do it will try to save this CSV in that particular folder. Okay. Then we will try to see and look whether it has been saved or not. Okay. So I'll try to copy this and send it in the chat. Okay, just write it yourself. Okay, now let's see. Now you can see that sample one. CSV the file has been saved. Okay, let us try to open it. Okay, so the it is giving you some values, right? Okay. So, okay. So, let us try to open it. Uh Yes. So this file I got it actually I'll try to show you. Yes. So 1 2 3 4 5 6 7 8 9. Right. So this is already got it right. So this is basically your uh like floating point uh exponent value which is been given there here right up to this much decimal right. Okay. Okay. So this uh basically this is like it will try to save it right and this has saved it right okay so you can convert to a number and try to do some processing and then it will try to save it uh effectively right okay okay fine yes okay so the another method is that we can save that in the file in using your NPY method also right okay so let us try to do that np do save file dot npy right so this is your file L npy right so it is a document right and this file can be loaded also so this is your npy file format right so basically we are trying to save it in this format okay so let us try to see the documentation of this okay right so save an array to a binary file basically your nmpp file format is a binary file which is in the np and it is loaded as np format only okay so you are trying to save it in the uh array format or in uh save the array in the binary format which is which is P takes less space basically. Okay. So that is the purpose of saving this uh to the NPY format. Okay. Right. Then we can also load this file. Okay. And load this this np.v file and then we can just see the contents. Okay. So how we can do it? We we have another method called np.load. Okay. So let's say array 2 = to np do.load and we will try to say that file dot npy right wait okay and if I try to see v.2 you can see that the values have been exactly matching. So what we have done is we have tried to save that file and then we have tried to load that file. Okay. So let me try to copy all this and try to give you okay the practical part will not cover in the today's session. So practical part we'll cover in the next session but I just wanted to give you a touchdown with your linear algebra part. Right. Okay. And regarding the Python project, someone asked me that we would be having uh one session with the Python project also. In the next session, we'll take that one, right? Okay. So, what you do is you gather all your doubts and your queries, right, regarding your Python project. In the next session, right, we'll assign some uh dedicated time to your Python project and we'll we'll try to address all the issues, right? And we'll try to explain that Python project also to you. and whatever issues or or concerns you have or you are facing in some programming. So we'll try to cover next part right. So next uh Saturday and Sunday we'll dedicate some time in Saturday sessions and in Sunday sessions. Okay. So quick recap. So what we have covered? So we have covered all these numpy array. We have covered indexing slicing and conditional statements and we have also seen some projects and uh uh the uh practice assignments also we have done it. Right. Okay. So now what we need to think uh uh like uh the learning objectives of this course is basically understand the concept of scalers and vectors. Right? Because scalers and vectors are very essential when we are trying to uh do some analysis. Okay. So one variable is being dependent on the other variable or not. How much it is being dependent on other? Let's say age versus height is being dependent variable or independent variable. Okay. We need to perform some analysis whether this particular variable is of importance or not. Okay. Right. Then in that particular sense we just need to understand the concept of scalar than vectors. Scalar is basically a simple scalar value right which has some magnitude right. But mag vectors on the other hand has some direction as well as some magnitude. Right? So we can apply some basic operations for data manipulation and analysis and also uh we'll demonstrate how we can do a transpose of a matrix for data processing. Okay. So we'll apply the principles of linear algebra using Python programming and also for real world data analysis task. Right? Okay. Fine. Okay. So let us try to uh introduce linear algebra. So we touch upon what is linear algebra. Okay. So it is a branch of mathematics that deal with linear equations, vectors and matrices. Okay. So everything what we study is basically your linear algebra where we are trying to uh write an equation and represent data in the form of an uh vector. Right? And also let's say we wanted to represent line right so we try to represent a line in terms of a 2D dimension right so basically data is being represented in terms of an 2D array which has some data points and there we are trying to uh formulate a line where these data points collide with each other similarly in terms of your data points it can be represented into n dimension let's say I have some data data points and I distribute the data points into intervention space whether is a 2D space 3D space then I wanted to make some connections with that data points whether a line whether a curve and I just make some manipulations to it for that understanding I just wanted to understand the concepts of linear algebra how to construct a line 2D line how to construct a a graph or a circle or something like that right for that I just need to understand how this linear algebra or mathematics can be used in terms of Python core because what we are trying to understand we are trying to understand ma math which is mathematics which is pure algebra. This mathematics we are trying to run our algorithm using our Python code because data we already converted into Python code right now we have data residing in our Python program. Now we wanted to apply some mathematical formulas to our data. Right? So that mathematical formulas we first need to understand it then we just apply with some library so that we can use. Okay. So it is very essential in terms of understanding of your uh uh analytics or deep learning right. So most of the AI based algorithms what you see nowadays has been uh made or grounded on this concept only the mathematics part which is your lineariz. this is basically the base of your AI right so these algorithms have been built uh on the top of it okay so in in a broader picture I'm trying to say that like first you need to understand this linear algebra come up with the algorithms the machine algorithms and so on and there we can just have it in a uh this uh deep learning part right okay fine right So linear algebra represents data in linear equations. So whenever there are data points so we understand the data points and we represent that uh equations by use of matrix and vectors right so let's say by our data we have the data right so this is a 2D plane so our data points are being structured in such a manner so from these data points we can just uh see that these are some equations and they are following some curve or path right Okay. And this path or curve has some properties which are some mathematical properties. This this path or curve this is followed. Right. So, so basically from this we can drive to some conclusions and then we can represent that data. Right? Okay. So this was the previous slide where we try to introduce linear algebra which is the branch of mathematics which deals with linear equations, vectors, matrices and it is very essential for the understanding of data analysis. The the essential parts like we have seen as operations we see notation and matrix factorization. Okay, operations there could be various operations which could be applied on that linear algebra. Notations could be nomicure, right? So we have performed some nomomenclature that based on the mathematics right. So there is predefined nomic lecture that how we can write a vector representation or a transpose representation. So this is basically your notation and matrix factorization basically uh uh yes yes uh yes the LMS manager will try to upload the slides and the right. So basically uh they are very helpful for the practitioners in data analytics right these these uh these parts the notation u like which helps us understand the algorithm presented in papers and books right so this is dot notation this is x small x right transpose right so inverse operation right uh right so then we have a dot product right transpose uh this is modulus right So this is your uh uh multiplication right right. So, so these all the notations uh could be represented right and uh these these notations have various uh what we call uh definitions right if we talk about right so these operations have various definitions and and like using our Python code also so these hold true right so these notations in terms of mathematics and in terms of Python may be different and then we just need to be aligned to that maybe dot operation could be different when We are trying to write mathematics but the dot operation in in your ma numpy could be different okay transpose could be minus one raised to power minus one right or or transpose t right this is inverse this is transpose right so e raised to power t or minus one so that could be different but in your numpy operations or in in your python code that would be different right okay so this we need to understand and this we just we need to relate it in order to when we are trying to follow some paper or group book or notation. So this is your standard practice, right? So and while dealing with your Python code, so just make sure that that the practice of the notation is exactly the same. If you are referring to dot product, so and you are trying to do a Python code to that dot product. So like it it has to be matched, right? Then basically uh when researchers try to uh implement your mathematics to your Python code so they they are very careful for their notation. If the notation says uh dot product and they are trying to do some multiplication the whole code will mess up. Okay. So in that sense like they has to be very careful if they are trying to do transpose. So they will try to do a transpose in the Python code itself only. Okay. But if they try to do some other operations so that might not work. So this is the basically notation is very important when you are trying to work your actual mathematics and converted that mathematics to your Python. Okay. Right. The operations they refer to some of the operations which are performed on vector and mathematrices. Some of the common operations are your multiplication, addition, transpose or your inversion. Right? So this we will try to see in practice like what all operations do we have? We have ma matrix multiplication, we have inversion, we have addition, we have transpose, right? So this we will try to see right. So let us try to understand the scalar and vectors. So okay. So I'll just take 10 minutes of yours. Okay. Scalar is a measurable quantity that is entirely characterized by it magnitude. Okay. So only the magnitude is there in the scalar. Okay. It does not have any direction whether you is your area, length or volume that is your scalar. Okay. But the vector is an object that has both magnitude as well as direction. Okay. It will try to give you that that that uh is something which is uh having some magnitude and as well as direction. Let's say wind which is blowing with the velocity of uh 15 uh kilometer or or towards the northeast direction which has both speed and direction. Okay. So this we can see that along the direction also it will see right and scalar is something which only have your magnitude the number right. Okay. So now the linear algebra represent both your scalers as well as your vectors. Okay. So they are used to represent attributes of entities such as your income, your test scores. Okay? Right? And they are often represented by an arrow with the same direction as the magnitude of the quantity. So you when we try to describe your vector so this is just an arrow which is which is your uh something like an arrow x and the top of that you try to represent with an arrow which is your vector the your direction represent your this but in case of your magnitude or a scalar quantity we just simply replace it by a variable okay right so they are the most fundamental fundamental mathematical object in data analyics right so basically All these operations under go the vectors operation and we will try to study this vector operation and they are ordered list of finite numbers right okay so we see the representation also we have seen that they are often uh given to represent such entities such as income your test scores right okay right so they can be added together to create another vector object let's say feed uh vector vector X with the speed of vehicle A and second vector with the speed of vehicle B and third vector is your Z which is X + Y right and we can also multiply a vector to a constant value right we are trying to multiply to 2X to obtain 2X okay and the result will also always be a vector if a vector is being added or vector being multiplied by a scalar the result is always a vector okay so this theory what why we are trying to do because we need to do it practically and when we are trying to do some programs or or in Python numpy so these these vectors are really important okay this we'll see okay so now we check which of the following best describes the vector in the context of linear algebra uh for data analyst a single array value representing a numerical quantity array of data points organized in a spreadsheet A quantity with both magnitude and direction often used to represent data in multi-dimensional space. A statistical measure of center trans such as mean or median. Okay. So option A, B, C or D. Yes. So the option is C. The quantity which has both the magnitude as well as the direction which is often used to represent data in a multi-dimensional space. So often in a numpy array what we are trying to do is we are trying to represent your data into your n dimension space. This we are trying to do. Yes. Okay. So now the dot product of two vectors. So we have vector one vector two and we are trying to do some dot product. Right. So dot product is the sum of the products of the corresponding elements of two vectors. Let's say this is your vector. This vector is being represented by these values x1, x2, x3, x3, xn. And y is a vector which is an array we call it, right? This could be an array or some data points we call it, right? Which is being presented in some direction which has some magnitude as well as direction. Let's say these all data points are being represented in one direction or in one dimension. The y direction could be another direction. Right? So this way we can interpret this and we wanted to have a dot product of this particular thing of this x vector or this y vector or this x uh array or a y array we can call it. Okay. Right. So this we trying to have a dot product and this you can see that we are trying to do an element wise operation that x1 y1 plus x2 y2 plus x3 y3 and so on right and if we try to see it is in the form of an equation. So this is a mathematical equation which we try to say that this is a summation from i to n and where we are trying to give a x1 into y1. This is your multiplication x1 + y1 then plus this is summation from i to n x2 y2 plus x1 x3 y3 right. So this is basically your summation. This is your expression which is being converted into this form and in the Python we can just do this operation. Understood? Right? So this is your dot product and you need to understand this dot product will try to give me a scalar value. Right? So when this dot product this scalar this vector will try to have a dot product with this dot uh with this another uh vector the the result will be a scalar one right understood up till now yes no so let's say the dot product of two vectors x is a vector y is a vector right and we are trying to do a dot product so 5 into -1 6 into 2 7 into 5 and this is a scalar quantity. Right? So this is your dot product by example. Understood? Yes. No. Are we clear? Yes. Okay. No. So I will not go further right. So I will try to uh stop here okay because uh up till now so this is all the theory part. So we'll try to cover practical part in the next session. Okay. Right. So this is all how we are trying to calculate the dot product. So just uh quick overview what we are trying to do is we are trying to create one actor vector A and vector B and we need to calculate the dot products using numpy dot dot. NP dot dot NP dot dot is a a function where we can perform the dotproduct of these two vectors and the dot product of this particular vector is a scalar function. Right? Right. So basically if you are trying to do this uh wanted to know what a particular function does just put a question mark inside the brackets. These are the brackets. So this is a function. Function by means we this has to be inside your bracket. Okay. And you put a question mark and then you put a shift and tab. Shift and tab. Okay. If you provide in the box fine. Okay. So reshape. So what does the reshape does? So these numbers it will try to provide you in the a particular shape. Five means 3 + 3 matrix or or or your uh like 2 + 5 right. The first number what it represent? It represents the number of rows. The second number what it represent? It represents number of columns. If it is a 2 + two then the first represent the colum rows. Second represent the columns and it could be your n dimensional. We call it first dimension, two dimensional, three dimension, fourth dimension. So we can give any dimensional to it. Okay. So this is what this uh power of arrays. So array could be nd array and it could be n dimensional. Right? Okay. So we have some data. So in in in the coming classes we will see that we have a list of data in which uh we have the person's age, person's uh uh salary, person's uh geographical information, right? Person occupation. So these are some fields. Okay. So these could be uh rows could be uh let's say one person, two person, three person and it could be 10 persons or it could be million persons, right? So rows could be number of persons and the columns could be referring to your age, your your sex could be your occupation, could be your salary, right? Could be your geographical location. So these are the things which could be arranged in your row and columns format right. So this is to be defined inside your array right that is why we are trying to learn this array so that when we are trying to work with some machine learning models or algorithms so this mathematically helps us or if you don't know you can just quickly type it on your Jupyter notebook and just tell me that by quotation mark and shift and tab and what is this np yes provide me the array of once. Yes. So we can specify the shape in which shape we can do it and also the d type we can specify. So what here we are trying to specify we need a uh np once uh that all the elements should be one and it should have a data type which is your np.in32 change in 32 right so this is what we are providing in npass okay so then we did like uh np add so these are the methods in which we are trying to add one array with the another okay so this was the array one and here we are array two okay so here one thing to note the operation which is been done that is your element wise that one is being added to one, two is being added to one, three is being added to three, right? So this is how the operation works. Similarly for the subtraction also, right? So we can have the np add, we could have np dots subtract then the multipation. So multiplication also works in the same manner. Here we are trying to work with arrays in which one array gets multiplied with the other. Fine. Okay. So, division. So, division also uh is same in which we are trying to divide one array by other. So, let's say we have this array uh this np1 this is your array one and this is your array two. We are dividing first element by first element, second element by second element, third element by third element. Right? will provide me the remain. So, okay. So, can anyone tell me what is for mod? So, what does it does? It will try to give you the remainder. It will try to divide uh the element wise and it will try to give you the remainder. Okay. So, these were the some operations we did in numpy array. Right? So, then there were conditional statements. So that we did like np where and np dot select. Okay. So np dot where okay so for example this is an array and the use case right. So we are more interested in the use case. So why we are learning and how we can apply this into our real life problems. Okay. So as we are trying to uh work uh with the large data set. Okay. So there we need to do some uh filtering process. Filtering process means we need to uh filter the salaries of employees which are greater than 10,000. Okay. So we only are concentrated of those employees where we are having the salaries greater than 10,000. So this is your array. So this is your array in which you have uh three rows and three columns right. Okay. So consider this is a first column as the entry of one student or a patient. Okay. Or could be your uh stock market analysis. Right? So this is could be a one stock. This could be another stock. This could be a third stock. Okay. This could be a first personal information where first row represent or the first column represents your age of the person, your uh salary of a person, okay, your occupation of the person similarly, right? Okay. So then I wanted to filter out from this array those employees which having the salary greater than 10,000. So similarly here like we have made a condition that ar=0 it is even and o okay so here instead of this uh I could simply say okay uh let me run this array so let us try to understand it and I will try to multiply this array by th000 right okay or 10,000 Right? Okay. So now you can see that this array consists of 10,000 20,000 30,000. Right? So these would be the uh employee uh salaries. Right? So I was wanted to make this like is uh greater than 20,000 I need to write greater than 20 or here I could write less than 90k. Okay. Okay. So now, so what I did is like I wanted to do the array operation but I haven't saved that array, right? So this was only trying to reflect on that array locally but it didn't I haven't uh put it into the back to the array, right? So that that is why it was giving me uh not giving me the results. So now when I will try to put it back. So it has given me greater than uh greater than and uh less than. Yes, greater than because this we would be using frequently like when we are trying to deal with our large data sets and we wanted to extract some information uh and perform some modifications. So basically like uh so this you can make any condition based on your data right okay whether it is a male female also that also you can make it based on the data which you got it right but we are working with numbers so that way we are doing some arithmetic operations to it. So when we deal with your pandas. Yes. Okay. H high debug. Yes. Uh fine. So another operation what we did is was np do select. Okay. So the this is a uh this is also very good uh uh uh conditional operation where we wanted to uh perform the similar operations right. So np.ware is also very used very commonly used but uh np. selected boot where we have multiple conditions. Okay. So here we can specify our conditions into a condition list. So this you can see that it is a list. Okay. And there we have the choice list also. Okay. So according to the condition list so we can perform if the array one has values greater than three then keep it as array one only. No change. If it is having uh condition greater than three then you make those entries multi uh to the power of two. Okay. So this is what the interpretation says. So you have a condition list and you can specify multiple condition list by a comma separate. Okay. So here we were trying to separate only one condition based on one condition we were trying to give it like if this condition holds then it is this and then it is this but here you can perform different conditions also. So that is the difference between your beer and your select. Okay. So let's say uh now we have the salary right. Okay. So salary have some brackets right. So according to the tax percentage right if it is less than five lakhs if it is less than 10 lakhs it is less than 12 lakhs right so according to the tax lab there are various amount of percentage which are being estated according to your taxes okay so there according to the text lab you can just mention right right are uh con list and choices defined variables Uh no no this is the variables right? So this is the variable name right? So you can have any variable name you can put it here. This is also a choice variable you can put it here. Okay. So if we wanted to see root documentation or the doc string so you can see that. So this is your con choice based and default. Okay. Right. Okay. So let's say if if you uh wanted to put it. Okay. So here you can just put that context. Let's say I will put it here uh my variable like my my condition my condition right and here I will put my choice list right okay so now here this is your default arguments or the parameters of a function here you just need to specify my this this is this is my condition which goes into your default called function f list and this is choice list which goes into your my choice list. Fine. Right. So this way you can specify. Okay. Right. Fine. And the default is for your default that if these according to these conditions if doesn't hold true then make it those entries as your four. Fine. So this was your np select where we have made a condition and your choice. Fine everybody got it? Yes. No. Could I repeat it again or shall I proceed further? Yes. Just let me know. Everyone okay with this? Okay. So basically what it is like np select right okay so np dot select okay so let me try to give you an example uh let's say we have this array right okay so let's say in this uh I wanted to separate out those entries which are uh falls between let's say 10,000 and uh to less than or equal to 10,000 then it is uh equal to or greater than uh 20,000 like 10 to 20,000 20 to 30,000 50 and 50 onward right so these three conditions I want you to separate now okay so what I will do is I will try to specify the condition if is greater than or equal to 10,000 right so if if my condition is this right. So what I wanted to do though with those uh conditions. Okay. So I wanted to make this divisible by let's say 10. Okay. If I wanted to have this condition greater than 10,000 and uh greater than 10,000 and one, right? It should be ready right? Okay. And default I will not specify anything on this I will put it. Uh here the condition could be wrong. So this way I have done it right. So basically you are trying to formulate a condition. If this condition was true then you are trying to divide by 10. And if this condition uh falls greater than 20,000 then then it by two right. So this way you can make this condition and your okay so if you can quickly do this and let me know. Yes, everyone. Okay. So, let me know once it it is done. So let me formulate another program. So what I'll do is I'll try to do it a mass. Let's say max equal to dot array. Okay. So this is your mark, right? So what I will try to do is I will try to make this condition and choice again. So let me make uh my condition. So I will say mark is greater than uh less than or greater than 90. Okay. So give it an at a grade right. So marks is less than is greater than or equal to 60 right? If it is 60 and then give it as a B grade then I will try to specify marks is less than 60 right then give it as a uh grade right okay so this is my condition then my choice would be like a grade then B trade and this will be C. Okay. So I will try to do is do or select then condition list equal to my condition. Okay then I have choice list equal to my choice right? Okay, let let us leave as default. Okay, so my choice is not defined equal to here. forward to moving forward saying choice list and default values do not have a common data type. Okay. So let's put a default also. Okay. Right fine. So here we got it. Okay. So now you can relate like what is the simplication and what is the select does. Right. So you have the marks of the student according to that logic. You can just give it the grades of the students and then you can just uh try to uh uh what we call uh try to modify your uh data right according to these choices and your condition. Okay. Is it fine? Yes. No. Deep. Okay. So, have you practiced this thing? Okay. Okay. So, Okay. So, I'll wait again. Okay. So, so I want everyone to do it and let me know on the chat they have done it or not. So, let me share this code also with you guys. And this is your marks first. First you need to run this and then you need to run Yes. Done. Okay. So deep. Okay. Fine. Great. Okay. So, okay. So, any doubt up till now? Okay. Just let me know in the chat. Okay. So, then what we have seen like uh a main, right? And this is function is used to calculate the minimum right across your axis right. So this is x= 1. So basically it is basically your uh xis one means your across your uh these minimum value across these uh uh so zero is for across your rows and one is for across your columns right okay so across your these things so it is it is giving you 1 4 and 7 so across your vertical lines Sorry horizontal lines not the vertical lines. So along the horizontal lines it is trying to calculate you the minimum values right so sometimes it is confusing between zero and one. Okay. So I'll make it in this manner. One means across your uh horizontal lines. Zero means across your vertical lines. Right? So this is how you try to interpret it. Fine. Similarly for the max values. So we are trying to calculate across the zero. Zero is for B across your across your tell me horizontal or vertical. Yes, vertical. Yes, exactly. Okay. Yes. So zero is vertical, one is horizontal, right? Okay. So try to uh do it in this manner or else you can just look into the documentation. If it is find you confusing right access 10 okay right and uh it will try to give you the documentation okay in some part it is given and in some part it is not given but it is better if you are trying to refer the documentation okay so then uh mean median and standard deviation okay so what is the difference between mean median and your standard education anyone. So what do you mean by mean guys? Yes, average right. So this is basically you need to do it in every operation. So so basically it is try to calculate the average right. So you have the data points and you wanted to find the average right and median. Anyone can tell me the about the median. Yes, median is the middle value. Okay, middle value. Yes. So in case let's say you have the numbers let's say 1 2 3 4 5, right? Okay. So can anyone tell me what is the median here? Yes, three is the median because it is the middle value. So 1 2 4 5. So here three is the middle value. If I try to put six here. So now can anyone tell what is the middle value? Yes. 3.5. Yes. So because 3.5. So 3.5. Why it is 3.5? Because now this part and this part. So if we try to divide this part into two parts. So first three parts is this and first three second three part is this. And the middle value is three parts. Right? So this is how the median uh works in case of your odd and even part. Fine. So this you need to make make sure that if you're trying to calculate for the odd part. So the median is the middle value and for the even part okay is to divide by two. Okay. So you try to divide by two the numbers and then you try to divide by two and find out the middle. But provided you need to arrange those values in the ascending order first. Okay. So this is the uh basic rule. Fine. Okay. So now variance anyone Yes. So basically the square root of standard deviation right so we call it that uh right uh right variance right so basically variance is the fluctuation like how the two values are are are in conjunction with each other how it is been separated linearly separated with each other right so that is what we call it as a variance okay so real root of variance is your standard deviation okay right so this is some of the arithmetic operations that is very useful when we are trying to work with the data. So practical implementation. So let's say uh so in our monsoons right so India is trying to record the what is the average rainfall right recorded uh in in the month of June right so like what is the highest right rainfall which is being recorded in the month of June right so they median highest right so highest means highest SP right or you can see that or or if the mode can also be calculated right right so so there mean it is the average so how much average rainfall we are trying to get it right and the variance is that like how the data points are separatable by each other right plus minus you can say that like according to the yesterday's temperature and now today's temperature how is the uh temperature fluctuating by how many points or how it is varying right so that is being calculated as a variance right so these operations these statistical operations are very useful when you are trying to do some analysis okay that is why we are trying to learn this first and programmatically we are trying to analy this okay got it everyone yes no okay fine so Now then the slicing part okay so slicing part is very important so I gave you an activity also based on this slicing part I think everyone has done it beautifully right to those who have not followed so I'm repeating again uh we have the array right so it is a 3 + 3 array so I wanted to get the entrance of the particular array fine so for example so so zero at this zur position of the zeroth row and the first column. Right? So what is the entry? So this before comma represent the row and after the comma represent the column. Right? So what we are trying to say is at the zero column and first column what entry we are trying to have in an array. So the entry is two. So zero row first column two enter like two two value right stepping in value this this value okay if we wanted to have all the rows so only we specify zero or one or two right so this is basically we are trying to specify all the rows okay for this slicing what I've told you that row from this row to this row and from this column to this column I I just wanted to have it in uh sliced part of the array right. It means that from 0 to one and from one first column and onwards I wanted to have that array right. So for example here 2 3 and 5 6 right. So this this is what I want right. So, so you can see that like from 0 to two. So, 0 to two both these entries and from one to this thing from one. So, this is your first row from one. Right? So, you can have only 2, three and five. Okay. So, this is how you sized it. Okay. Right. Fine. So, anyone have any doubts? Should I give you an example to practice? Yes. No. Yes. So let me give you an example. Okay. So array one. Okay. one put I'm trying to make an arrow okay and I will try to make it dot reshape also here okay so instead of putting the commands in in uh you can put it in the single of this thing also right so what I'm trying to do is first I'm trying to uh make the array of elements single dimension array of 1 to 10 right for 10 elements or basically it is the nine elements right it will start from one but it will end at 9 okay then I'm trying to reshape it reshape it into a 3 + 3 matrix right so if I try to put this array right so now I have a 33 matrix fine okay so similarly I could give a 5 + 5 matrix also. Uh image 1 to 26 and it is a 5 + 5 right? Okay. Fine. Okay. So from this particular array first you make all of you make this array 5 + 5. So what I want is I want uh these elements 7 8 9 12 13 and 14. Okay. So I will just write it here. What the output is? Output I want 7 8 9 12 13 and 14. Okay, everyone has to do this activity. So, first you need to make an array of size 5^ 5 and then you need to filter it out and give me the code in which you will try to give me 7 8 9 12 13 and 14. Yes. Uh when we are trying to run this. Okay. So np. So np is basically you had to run this import numpy as np every time. Okay. So this is the library which we are trying to run. Okay. So you are trying to import this library. This is the external library. So first you need to run this. After that running, you just need to run uh the commands which I I'm trying to show you. Is that fine? Yes. So do you want okay fine okay so now then the next part which we have seen is loading our CSV right so there is a simple operation where we are trying to load the CSV and whatever is present at the CSV we are trying to print it as a array right okay so these are simple functions where you have some data and the deliminator so deliminator is basically uh the CSV format. So CSV format is always a comma separated format, right? And you need to specify that the uh type of data type which that CSV hold is of string data type. So it may be integer also in some cases, right? But you need to specify that. Okay. Similarly, this is also another uh operation generated from txt and this is your load txt. Right? Then we can also save the numpy in a text file. Okay. So where this is used? So suppose in the previous example we have done some operations, right? So now this operations we have done it. So let's say you got the list of students and list of marks. Okay. But you don't know that where that marks could be uh graded, right? So the grading scheme could be simple enough where you are trying to load the uh CSV and convert into a numpy and then after that you perform uh operations and then you save it again as a t right. So this operation is very much useful where you can perform some operations and give up give the result back to the desired person. Fine. Let's say the manager says that okay I want you to uh do this a CSV where it is having some data and your task is to just do some operation onto that file using numpy and then give back me the results in the same format. Generally it happens right the file you are working so the customer or the client wants that information in the same format which you have given as a input file. So there these operations or these u functions are very handy to use. Okay. So these are very handy to use and they this you can do it by using your save txt or save and then you are trying to load. Fine. Okay. So in in future in in further uh analysis we will try to do some operations where we are having some CSV file. There we are trying to load it by using pandas. The specified format or the well-known format is your pandas. So here we are trying to load a single uh numpy but in pandas we could load a CSV and then make some analysis. Okay. So that is what we are trying to learn today. We will have the introduction to num pandas today and uh then we will look it up on it's fine. Okay. Okay. So, we started with the linear algebra, right? Okay. So, linear algebra it is branch of mathematics that deal with your linear equations, vector and your mathematics. Fine. Okay. So, why it is essential? Because every numbers is being associated with your another data points. So, we just wanted to match some relation with that number. Let's say let's say your your uh age is increasing what will have that impact on yourself. Your salaries has to also be increased. Right? Okay. Right. So this has to be some kind of a linear dependence among among that data types or the data points. Right? So that is basically why we are trying to learn the linear algebra. So linear algebra is basically we represent data in the form of an equations. Okay. So whether it is a straight line, whether it is a curved line, right? Whether it is a a declining path or inclining path. So there we can just make some analysis. Let's say the as the car is getting older day by day, it is the price of the car is decreasing, right? If we get a declining path or a negative gradient, right? Or a descent, right? So then we can just make some predictions easily. If it is a higher then we can just make that okay the age of the person is increasing this average has to be rised as we have seen that time. Fine. So that is why if we try to represent your data the form of an equation. So then the analysis is very easy. Right? Okay. By looking at the number you cannot make some analysis. Understood? Right? Okay, the essential part of the linear algebra perform some operations uh uh that we perform some operations on the linear algebra part or or the uh uh or the for the analysis. Then we have notations and your matrix factorizations. Right? So these were some of the notations that we already know. So dot transpose, right? Then we calculate the uh norm right right distance right okay so normalize right so these are some of the operations we will try to learn then matrix already we have covered this matrix part uh which we are trying to have a multiplication of the matrix okay inverse addition and transpose we will try to see today Okay. Okay. Then the scalar. Scalar is a quantity which is entirely characterized by its magnitude. So scalar we don't have any uh direction. We only have its magnitude whether we call it as area, length or volume that are being scalar quantities, measurable quantities. Right? So area we can have a measurement. Right? So area we can measure it. Right? volume also we can measure it that how like what what that uh quantity uh how many liter of that quantity we have right but vectors is is something which we have your magnitudes magnitude as well as direction right so for example uh velocity so wind is blowing at the speed of 15 uh kilometers per power right in the northwest direction right so which we have both the speed as well as direction right so this is how we represent your vectors and vectors can also be represented in the form of an letter x or with the with an arrow arrow of what means that it is a vector right and they are the most fundamental objects in data and vectors vectors right so because uh so vectors basically we study vectors in your linear algebra whether it is your income or your test scores or already we have discussed right so that is also considered as a vector right so first we can add it add a vector we can multiply a vector okay to create a three vector a third vector which could be in addition to uh uh addition of first first vector to it second vector which can also be multiplied by a constant. Okay, this we will try to see in practice, right? That how we can have a vector which could be uh created uh and then we can multiply that vector by a scalar. Okay. So now the dot product. So dot product is also very important. Dot product is a scalar quantity which we have two vectors that vector x and vector b y. Okay. So this dot product we are trying to calculate the resultant of these two vectors. which is a scalar quantity which is a which is independent of that uh vector right and how it is being calculated the individual elements are being multiplied and it is being added to every element let's say x1 * y1 + x2 * y2 + x3 * y3 and so on Right. So dot product right basically like it is very essential when you are trying to find the distance as you go deeper into your machine learning or your deep learning. So so dot product uh or or or cosine similarity. So these these topics are very important in finding the relationship between two objects or two vectors. Right? If you wanted to calculate that how similar these two items are then we calculate the dot product of two vectors right fine or the cosine similarity also we call it. Okay. So this is how the mathematical expression where we are trying to calculate the dot product. So we have this vector 5 6 and - 7 and this again another vector which is min -1 2 and 5. So here we are trying to calculate the the dot product and it is a scalar con. Okay. So whenever you are trying to open your Jupyter notebook it is very important first you are trying to import the numpy right. So that is why you are trying to get the er uh the error that numpy is not defined. Okay. So this is very important. Okay. So then uh let us try to first create uh the array right. Okay. Yes. So, np dot array. So, let us try to create one array. Okay. So, just try to type along with me guys. Okay. I'll try to take some arbitrary values. I call it as a What has happened first to mention? Yes. So now this is done 3 + 3 added. Here it is 3 + 3. Right. Okay. Okay. Great. So here these are the arrays and this is the operation of dot operation where you are trying to do a dot product of those two right. Okay. So always like there like basically the dot product happens right. Uh this I have taken a three + three array. So I could take a a simple array also a simple operation also which I have shown you in the slide that this is the single element and this is your single array a 1D array right. So there also we can just formulate it and then this is basically your 42 is coming but here how this 11 14 and 9 are being calculated just look at this operation. So this first this thing is being multiplied by 1 2 and 3 1 1 are 1 2 2's are 4 2 3 are 6 right so this is how it is being multiplied and the resultant is 11 right if I try to give you a simple demonstration where I could say numpy dot array here I would simply say 1 2 comma 2 and this is your y np dot array here 1 2 and 3 right and then I will say np dot x comma y you can see the answer comes to be This is a scalar. So this is how your this operation has resulted. Okay. Quickly do this and try to just let me know if you try to multiply this right with this right 2 4 and 2 then this 3 2 3 you get the four and 19 then the second entry then with the first right similarly you can get all the values calculated fine. Okay. Now just let me know uh if you wanted to calculate right this 14 fine. So how this 14 will come? So third row multiply with the first column. Okay just give me the points. Just do it and just uh try to put it in the chat deal. So everybody has to do it understand the concept guys right? Okay. So this was the single right single like column entry and this row entry. So this we are trying to do right. Okay. So this is how the dot product is being calculated for the one-dimensional. So every piece is breaking into one dimension and then we are trying to put that values inside your three-dimensional. Right? So this you need to understand the how this dot product is being combined when you are trying to loop it into your whole array. No matrix multiplication rule will follow basically right. So what you are trying to say is correct right. So if you wanted to see this thing right so first row and this third column right? So this is fine right? Okay. So if you wanted to have the uh this second row and second column it will give you this 16 entry. If you have wanted to have your this entry right so this third row okay and your this column right so automatically it will follow. So let us move forward. So we have already created one array right okay so this is the array. So if I wanted to multiply with any scalar value, let's say I wanted to multiply it by three, right? So I can multiply it. But the thing is that this will not get reflected into the array until unless I do it a = a into b. Fine. So now every entry gets multiplied by three. This is basically a scalar multiplication in which we are trying to multiply this thing right. Okay. So let us try to see uh the transpose. Can anyone tell me what is the transpose? What is you? Yes. So basically transpose is is basically we try to interchange row to columns, columns to row, right? or vertical to horizontal or vice versa right okay so how in a numpy we can perform this transpose operation so this we will try to look it up okay so for that let us try to uh see a simple array right let's say I try to have at array one okay equal to np dot array right and I will say 1 2 3 Okay. So, A1 is this area. So, I wanted to make this as a transport, right? So, let us try to see the shape also 2 + 3. Right? So I will say B = or I will say A1 A1 transpose equal to NP dot transpose I will say A1. So you can see that the first row has become your first column has become your first row and your second row has become your second column. Right? If I try to say A1 transpose dot shape it is three. So done. We'll follow the introduction to matrix. So this we already know that like it is a rectangular representation with rows and columns. So this I'll skip then it is a matrix multiplication addition scaler multiplication and subtraction. So this we have already seen right. So matrix is basically your when we are trying to add some matrix. So the corresponding elements are being added right. So two matrixes of different orders cannot be added right only the same order matrix can be added. So if you are trying to add add your uh different order matrix so that will not happen right. So here there's a simple demonstration where how we are trying to add your 3 + 3 matrix and the resultant is also a 3 + 3. Right? Here we are trying to add x and y. So 2 + 3 - 1 + 2 5 + 9 right. So element wise operation follows it right in which we are trying to add some matrix and give the resultant back right fine. So this is the theory part behind it like how we are trying to do the addition and how in numpy it is very easy that numpy dot add numpy do.tract subtract. So this we have already seen in the previous session right so this I think uh we don't need to uh give much time to this subtraction that is also very easy in which we are trying to subtract that matrix right element wise every element corresponding to that every element in the uh uh other other matrix is being added or subtracted or multiplied right scalar Scalar is basically every element is being multiplied either it is being divide or multiplied. So every element gets its operation right? 4 into 2 8 0 0 into 2 0 3 into 2 6 right 7 into 2 14 right and this is also how this scalar multiplication happens fine right to obtain the product of two matrix multiply the elements of rows with the first matrix with the corresponding columns of the second matrix. Right? So this is what like uh I think uh uh someone asked me this question right. So we are trying to have this rows and columns right rows of first first matrix is being multiplied by the columns and the resultant we get it right 1 into 7 + 2 into 6 + 3 into 3 right similarly we are trying to have this matrix multiplication right so in this picture we are very much clear that 2 into 5 + 0 into 2 + 5 into minus 3 right so This will give us the first entry. So 2 into 1 0 into 2 5 into - 8 it will give us the second entry. Right? So then 8 into 5 - 3 into 2 5 into -3 it will give us the third entry. Right? And 8 into 1 - 3 into 2 5 into - 8 the fourth entry. Right? So now see the shape. So this is your 2 + 3. This is your 3 + 2 and the resultant is 2 + 2 right in the matrix multiplication. So this is basically your uh high school mathematics operation where we have a matrix right. If this 2 + 3 and this is your 3 +2 and the resultant is your 2 + 2. Okay. So determinant is basically like we are trying to calculate uh for 2 +2 matrix like a d minus bc. So this is how we try to calculate the determinant. But for the 3 +3 matrix so what we need to do is we need to first keep this aside and then calculate the determinant of a 2 +2 matrix first then minus b then d uh then this minus p and then d i d uh di and fg. So this we can calculate and then plus C right determinant of this. So it is a scalar quantity guys. Okay. So this is how we are trying to calculate the determinant of a matrix. Okay. So we'll try to see that this is a mathematical operation. Right. So this we have already seen in our school graduate the determinant by using this multiplication of this matrix and then multiply by a then minus b and doing some math we get that scalar code right fine so this is how we try to calculate the determin but when it comes to your python so this is done by a single operation which is your numpy right it is providing us library where we feed this matrix whether it is a 2 +2 matrix or a 3 + 3 3 matrix and the job is been done by the Python. Okay. So you don't need to calculate this long lengthy math and uh try to fiddle with your mathematical operations. It automatically it will try to give you the result as a scalar. Right? Okay. So let us try to see this determined ca