Gain insights into Pandas' powerful tools for data analysis. Learn about Series, DataFrames, and optimize performance for data operations, filtering, and file uploads in data science and machine learning.

Pandas is a very popular Python library that provides powerful, flexible and high-performance tools to process data.

If you’re looking to get into data science, machine learning, or you just want to brush up on your analytics skills, then look no further. In this course, you’ll learn how to process and analyze data with Pandas. 

You’ll start out by covering the basic data structures in Pandas: Series and dataframes. In the latter half of the course, you’ll learn how to perform operations on different data structures, and how to optimize performance for file uploads, data filtering, and more.

Data Analysis & Processing with Pandas

Pandas provides two types of sorting, by index or by values. There is a third option, which combines both index and value.

# Sort DataFrame by index

Most of the time, our index is meaningless and starts from zero. However, in some cases, the index has its own meaning, which can be used to sort the data.

The default sorting order is ascending. If you need a descending order, pass the `ascending=False` to the function.

> **Notice**: The sorting is not in place. The result is returned by the function **sort_index**.


Pandas provides functions to sort data in different ways. Let’s see how this can be done in this lesson.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Sorting a DataFrame