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

## Delete a row

The pandas `drop()` function is used to delete data rows or columns. The default operation takes `axis=0`, which indicates that we are referring to a row.

> **Notice**: The drop operation is **not in place**. The result is returned from the function.

# Delete a row

The pandas `drop()` function is used to delete data rows or columns. The default operation takes `axis=0`, which indicates that we are referring to a row.

> **Notice**: The drop operation is **not in place**. The result is returned from the function.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Deletion in a DataFrame

Delete a row