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

Let's say we want to group the products in a `DataFrame` called `df` by the `color` and `brand` columns. What is the correct syntax for the `groupby` function for this case?

Let’s say we want to group the products in a <code>DataFrame</code> called <code>df</code> by the <code>color</code> and <code>brand</code> columns. What is the correct syntax for the <code>groupby</code> function for this case?

When using the `pivot_table` function to create a pivot table, which parameter is used to show the row and column subtotals?

When using the <code>pivot_table</code> function to create a pivot table, which parameter is used to show the row and column subtotals?

Which Pandas function is used to divide a column with continuous values into bins or categories with an approximately equal number of values?

Which Pandas function is used to divide a column with continuous values into bins or categories with an approximately equal number of values?

Which parameter is used to assign names to the categories or bins created by the `cut` and `qcut` functions?

Which parameter is used to assign names to the categories or bins created by the <code>cut</code> and <code>qcut</code> functions?

Data analysis with Pandas

Data analysis with Pandas

Let's take a short quiz on what we’ve learned about data analysis with 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

Quiz Yourself: Data Analysis with Pandas