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

# Date and time data types
We can express dates and times in three main forms:

* A specific date or time (`2021-12-10`, `2021-11-25 14:59:00`)
* A fixed interval (`hour`, `week`, `month`).
* A duration (`3 weeks`, `4 hours and 30 minutes`).  



The Pandas library has several functions and methods to handle these different forms efficiently and easily. It’s important to use appropriate data types for date and time values. Otherwise, we won’t be able to use Pandas functions for dates and times.

The `datetime64[ns]` data type can be used to express date and time values. The values that have datetime64[ns]` data type are called `Timestamp`. A `Timestamp` contains both date and time information. We can convert a string representing a date to a Timestamp by using the `to_datetime` constructor.

Learn Pandas data types used for dates and times.

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

Data Visualization with Seaborn for Walmart Sales Projection

Final Challenge and Quiz

Pandas Date and Time Data Types

Date and time data types