Basics of Pandas

Pandas is a popular open-source library in Python that is widely used for data manipulation and analysis. It provides easy-to-use data structures and data analysis tools for handling structured data, including data from tables, databases, and spreadsheets.

With Pandas, you can easily load, manipulate, and analyze data in a variety of formats, including CSV, Excel, SQL databases, and more. It offers powerful data manipulation tools like filtering, grouping, joining, and merging data, as well as time series functionality.

Pandas is built on top of the NumPy library and integrates well with other Python libraries, such as Matplotlib for data visualization and Scikit-learn for machine learning tasks.

Here is an easy example:

DataFrame is used to create a data table, and it can be displayed with labels.

Also, features can be accessed.

Next is Series. Series can create a column without its label.

Other than that, you can take advantage of built-in math functions.