In your Journey to learning Machine learning or Data Science, you must have heard about “Pandas”, which is an open-source library in Python. You will be dealing with Pandas library mostly when you are dealing with data, so Pandas is a library widely used for Data Analysis, Data Cleaning, Data Exploration, and more.
Soon, we will explore pandas from scratch, so by reading this, and further tutorials, you will learn about Pandas, but just don’t read it. Try to Implement by yourself, so that you can benefit from this.
Introduction to Pandas
What is Pandas?
When you are not connected to the world of Machine Learning and Data Science, you might think about Panda as a black-and-white lazy animal. But here, “Pandas” refers to “Panel Data”, or “Python Data Analysis”.
So, Pandas is an open-source Python Library, which is used in Data Analysis, Data cleaning, Data manipulation, and more. When you visualize the datasets here, you would see it similar to an Excel Table.
The Pandas Library is built on top of NumPy, which is another incredible library, which lets us work on large, multi-dimensional arrays. Pandas are pretty fast and have high performance (contrary to a super lazy panda), and it lets us do a lot of things with the data (as you will explore later)
Why Use Pandas?
Now that you know this “Pandas” are different from “Panda” in the forests, let us understand why should we use Pandas. Well, if you are going into Machine Learning or Data Science, we can say that Pandas can be one of the crucial tools at your disposal, which you can use. Here are some of the reasons why should we use Pandas.
- It is fast in terms of data analysis and data manipulation.
- It provides time series functionality.
- You can load data from different files (XLSX, CSV, JSON, etc)
- It gives you a lot of functions for doing different operations.
- With Pandas, we get something called a data frame, which is something very important.
So, it is important for us, to understand the importance of relevant data, and using Pandas for data analysis and manipulation. Anyways, when you will learn more about it later, you will find it more and more relevant using Pandas for Data Analysis.
We will soon get started with Pandas, starting from the installation to other basic things, like exploring Series, DataFrame, and much more. But glad that you started learning Pandas. The further lectures would help you understand more concepts related to Pandas. I would recommend you implement the things yourself, which are going to be taught in further lectures so that you can benefit more from them.
