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

The missing values problem is very common in the real world. For example, suppose you are trying to collect information from a company. There is a field for a *company address*. Many people want to keep their privacy and leave this field empty. If the data is loaded by pandas, those empty fields are listed as **missing values**. `NaN` is the default missing value in pandas.

Let's see how to deal with a missing value.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Deal with Missing Value