5 Best Python Libraries for AI | Day 2 - Pandas Explained for Beginners 🚀

Ajouté : 2026-05-19

[00:00:00]Hello everyone, welcome back to day two of our five best Python libraries for AI series. Friends, in day one, we learned about NumPy and understood why it is the foundation of AI and machine learning.

[00:00:16]And today we are going to learn one of the most important libraries for handling data which is pandas. So now before we begin, if you are new to the channel, make sure to subscribe and like this video for more AI and Python related content. So let's get started.

[00:00:36]So friends, first let's talk about why data is important in AI. Before understanding pandas, first let's understand why data is so important in AI. So friends, artificial intelligence works on data. Machine learning model learn patterns from data. So if your data is messy, incomplete, incorrect, then your AI model will also perform badly. So that's why data prep-processing is one of the most important steps in AI. And this is exactly where pandas helps us. So now let's talk about what is pandas. So friends, pandas is a powerful python libraries used for data handling, data cleaning, data analysis and data manipulation. So it helps us work with structured data easily. for example, Excel files, CSV files, tables, database.

[00:01:55]So, pandas makes handling large data sets very simple. Friends, now let's talk about why is it called pandas. The name pandas comes from panel data which refers to structured data sets. And today pandas is one of the most widely used libraries in AI, machine learning, data science, analytics. So friends, now let's talk about why pandas is important. Let's suppose you have a data set with missing values, duplicate rows, wrong formats. So cleaning all this manually would take hours but pandas can do it in seconds. So that's why AI engineers love pandas.

[00:02:55]So friends, now let's learn how to install pandas on your computer. So installing pandas is very simple friends. We use a tool called pip and pip is Python's package manager. Let's understand this in a simple way. So friends, pip help us download and install Python libraries from the internet. So to install pandas first need to open command prompt in windows and if you are using Linux or Mac then you need to open terminal in Mac or Linux and then type this command pip install pandas just like you can see in the screen and then press enter and after a few seconds Python will automatically download and install the pandas library on your system. So now let's understand this command step by step. So here pip means python package manager. Install means we want to install something and pandas is the library name. So this command basically tells python please download and install the pandas library. So friends, now let's talk about how to check if pandas is installed. After installation, we should verify where pandas is working correctly or not. So for that you need to open Python and type import pandas as pd and then print pd double underscore version and then double underscore just like you can see in the screen and if pandas is installed successfully then you will see a version number like 2.2.2 two or another version number. So that means pandas is installed correctly. So now let's talk about why do we write as pd import pandas as pd. So friends here pandas is the library name and pd is a salt name or you can say nickname. So this makes coding salter and easier and almost every Python developer uses PD as the standard shortcut for pandas. So friends, now let's see our first pandas program. Now let's create our first data sets. Just like you can see the code in the screen. So now let's understand this code step by step in detail. So in line one you can see we are importing pandas.

[00:06:05]import pandas as pd. So this line imports the pandas library into our python program. And here pandas is the library name and pd is a salt nickname.

[00:06:24]So instead of writing pandas dot dataf frame again and again developers simply write pd dot dataf frame and this makes coding easier and ster. Now let's talk about step one create data. So now look at this part carefully. So here we are creating data using a Python dictionary.

[00:06:51]Friends, a dictionary stores information in key value pairs. So in this example, name is a key and Ammon, Ria and current is its value. Similarly, age is another key and 21, 22, 23 is its value. So you can think of it like columns in a table.

[00:07:18]The name columns contains names and the age column contains ages. So basically we are preparing structural data before creating a data frame or we can say we created a small student data set. So the important thing to notice here are both columns contain the same number of values. Why? Because each row must have complete data just like you can see here. So this creates a proper table structure. Now let's talk about step two that is create data frame. So now look at this line in Python code. df is equal to pd dot dataf frame and inside that we are passing data and this is one of the most important lines in pandas. So what does pd dot dataf frame do? Now let's talk about that. It basically converts normal python data into a pandas data frame. So you can see here data contains our dictionary and pd. dataf frame data converts it into table format and we store it inside a variable called df. So why variable name DF only? Because most developers use this as a shortcut for data frame and it's a common naming convention in pandas. So now let's talk about what is a data frame. Now this is very important friends. A data frame is a table like structure in pandas.

[00:09:09][clears throat] It contains rows, columns exactly like Excel sheets, SQL tables or Google sheets. So friends, data frames are the most important structure in pandas. So now let's talk about step three which is print data. So this basically display the data frame. Friends, now let's talk about why data frames are powerful.

[00:09:42]Friends, data frames make data handling very easy. We can filter the data. We can sort the data. We can clean the data. We can analyze the data. We can remove duplicates. And we can fill missing values. All using simple commands. Now let's talk about how to read CSV files. So friends, in real world AI projects, data usually comes from CSV files and pandas can load CSV files very easily. You can see the code in the screen and this loads the CSV file into a data frame. Now let's talk about how we can check data information.

[00:10:34]So to understand data set structure we can just print df dot info just like you can see in the screen. So this tells us total rows, total columns, data types and missing values which are very useful in AI projects. Friends, now let's talk about handling missing values. Real world data often has missing values. For example, some rows may have empty cells.

[00:11:10]So, pandas helps handle this easily. We can remove missing values.

[00:11:18]We can fill missing values, filter the data. Let's suppose we only want students older than 21. So, using data frame, we can easily do that. Apart from that we can add new columns. We can sort the data and many more. So friends, now let's take real world AI example. So now let's understand how pandas is used in AI. Let's imagine you are building a house price prediction model and your data set contains area, bedroom, location, price. So before training the AI model, you need to clean the data, you need to remove missing values and then you need to organize columns and then you will need to normalize values.

[00:12:15]So pandas helps perform all these task.

[00:12:19]Now let's talk about where pandas is used. So friends, pandas is widely used in machine learning, data science, AI prep-processing, financial analysis, business analytics, research. Almost every AI project uses pandas somewhere. So friends, now let's talk about why AI engineers love pandas.

[00:12:49]because of easy syntax, fast data handling, works with huge data set, powerful data frames and it saves times.

[00:13:01]So without pandas, data prep-processing would become much harder. Friends, now let's talk about pandas versus Excel.

[00:13:12]Now you may ask why not just use Excel?

[00:13:16]Because Excel struggles with very large data sets and pandas can handle millions of rows, complex filtering, automation, AI pipelines much more efficiently. Now let's talk about some common beginner mistakes. So the first mistake is ignoring data cleaning. So friends, bad data means bad AI model. And the second mistake is not understanding data frames properly. Friends, data frames are the heart of pandas.

[00:13:55]And the mistake number three is trying to memorize commands. So friends, instead you need to practice using real data sets. So friends, that's it for day two. Tomorrow in day three we will learn about another powerful library which is scikitlearn and finally start building machine learning models. So don't miss it friends. And if you enjoyed this video like the video, subscribe to the channel and comment. Have you ever used pandas before? See you in day three.

[00:14:32]Till then take care. Bye-bye and thank you so much for watching.