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

## 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.

# 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`).  



Learn Pandas data types used for dates and times.

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

Pandas Date and Time Data Types

Date and time data types