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

There are two ways to store strings in pandas, `object` and `StringDtype`. In the older version, before 1.0, `object` is the default option. Since the release of pandas 1.0, we are now able to specify dedicated types.

Let's go through these important methods to manipulate strings provided by pandas. If you are familiar with basic Python, you'll find out that most methods are quite the same as Python native string functions. The only difference is that the method of pandas is working on Series or DataFrame.

In this section, we focus on the Series.

In this lesson, we introduce the string type.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

String Type