Gain insights into using Python Pandas for efficient data analysis and manipulation. Delve into detailed functions, parameters, and methods to elevate your data handling skills.

A Comprehensive Practical Guide to Python Pandas_ .png

There are several exercises that focus on how to use a particular function and method. The functions are covered in detail by explaining the important parameters and how to use them. By completing this course, you will be able to do data analysis and manipulation with Pandas easily and efficiently.

Mastering Data Analysis with Python Pandas

## Merging `DataFrame`s

The `merge` function is another way to combine data from multiple resources. The `concat` function compares column names or row indexes when combining `DataFrame`s, whereas the `merge` function compares values in the given column or columns to check matching rows.

The `merge` function merges two `DataFrame`s based on the common values in shared columns. Suppose we have a `product` and a `sales DataFrame` as shown below:




# Merging `DataFrame`s

The `merge` function is another way to combine data from multiple resources. The `concat` function compares column names or row indexes when combining `DataFrame`s, whereas the `merge` function compares values in the given column or columns to check matching rows.

The `merge` function merges two `DataFrame`s based on the common values in shared columns. Suppose we have a `product` and a `sales DataFrame` as shown below:




Learn how to use the merge function of Pandas.

Course Introduction

Pandas Data Structures

Creating a Data Frame

Exploring a Data Frame

Filtering a Data Frame

String Manipulation with Pandas

Date Manipulation with Pandas

Handling Missing Values with Pandas

Data Analysis with Pandas

Data Visualization with Pandas

Combining DataFrames with Pandas

Final Challenge and Quiz

The Merge Function

Merging `DataFrame`s