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

## Create a DataFrame from a dictionary of series

Obviously, the DataFrame can be created from a list of series or a dictionary of series. 

As mentioned in the last lesson, if two series objects have different indexes, they wouldn’t be joined by an index, like in example two below.

# Create a DataFrame from a dictionary of series

Obviously, the DataFrame can be created from a list of series or a dictionary of series. 

As mentioned in the last lesson, if two series objects have different indexes, they wouldn’t be joined by an index, like in example two below.

Let's try to create a DataFrame object from the series.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Creating a DataFrame From a Series

Create a DataFrame from a dictionary of series