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

In many cases, data needs to be exported to a local file for persistent storage. Let's see how to export the data from memory to local files. It not only supports some common text-like format files, such as `CSV` and `JSON`, but also some binary format files, such as `pickle` and `hdf`. In this lesson, we only focus on `CSV` and `Excel`. The usage is similar to other file formats.

Pandas supports exporting data to multiple file formats. Let's see how that works.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Export Data to File