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 values in the salary column of the `staff` (`DataFrame`) are stored as a string. We need to convert it to a numerical format to make calculations.

There are two issues that we need to solve before converting it to a numerical data type. 

* We need to remove the “$” sign at the beginning.
* We need to remove the “,” used as the thousands separator.


# Problem statement

The values in the salary column of the `staff` (`DataFrame`) are stored as a string. We need to convert it to a numerical format to make calculations.

There are two issues that we need to solve before converting it to a numerical data type. 

* We need to remove the “$” sign at the beginning.
* We need to remove the “,” used as the thousands separator.


Learn how to combine multiple operations 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: Make the Salary Column Proper

Problem statement