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

It's very common to do some operations on existing columns and append these columns to the current DataFrame.

## Use apply function

You can use `apply()` to send a function or lambda to a single column, which allows the user to pass a function and apply it to every single value of the pandas Series, then return the result. 


It's very common to do some operations on existing columns and append these columns to the current DataFrame.

# Use apply function

You can use `apply()` to send a function or lambda to a single column, which allows the user to pass a function and apply it to every single value of the pandas Series, then return the result. 


pandas provides the apply() function to perform operations on entire columns of a Data Frame. Let’s see how we use it.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Apply Function to a DataFrame

Use apply function