Learn about DataFrames in the pandas library and how we can perform different operations on them.

Pandas: Python for Data Analysis

In this module, we’ll learn about DataFrames and their functions in detail. We’ll also learn to perform data manipulation and data visualization using pandas. We’ll solve several exercises that focus on how to use a DataFrame and its methods. By the end of this module, we’ll be able to do data analysis and manipulation with pandas easily and efficiently.

A Comprehensive Practical Guide to Python Pandas

## Problem statement

The `grocery` contains the prices of groceries. Based on the prices, we need to divide the groceries into three categories and label them as cheap, mid-priced, or expensive.

We'll then calculate the average price for each category using the `groupby` function.


# Problem statement

The `grocery` contains the prices of groceries. Based on the prices, we need to divide the groceries into three categories and label them as cheap, mid-priced, or expensive.

We'll then calculate the average price for each category using the `groupby` function.


Learn how to categorize rows by using a column with continuous values with Pandas.

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

Conclusion

Challenge: Categorize Groceries Based on Price

Problem statement