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

# Data types

Data doesn’t always come in plain numbers. In addition to integers and floats, we need to deal with strings (textual data), dates, times, and boolean (True and False) data types.

It’s important to use proper data types for two main reasons:

 * Data structures of different data types take up a different amount of memory space. Having proper data types saves us from wasting memory.

 * Some methods and functions can also be used with certain data types. For instance, we need to store data that contains date and time in the data structures of datetime data type to use it.

Let's first check the data types in the `sales`. Then, we'll see how to change data types of columns.

Learn how to find and change the data types of columns in 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

Data Types of Columns

Data types