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
We have the following `DataFrame` with two columns and ten rows. We want to fill the missing values in the `measurement` column with their previous values, but we’re only allowed to fill two consecutive values with this method. The remaining ones need to be filled with the average value of the column.



# Problem statement
We have the following `DataFrame` with two columns and ten rows. We want to fill the missing values in the `measurement` column with their previous values, but we’re only allowed to fill two consecutive values with this method. The remaining ones need to be filled with the average value of the column.



Learn how to fill the missing values in a Pandas DataFrame.

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: Filling the Missing Values

Problem statement