The Pandas DataFrame: Make Working With Data Delightful

The pandas DataFrame: Working With Data Efficiently

Cesar Aguilar
Cesar Aguilar 25 Lessons 2h 10m data-science intermediate
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The pandas DataFrame is a structure that contains two-dimensional data and its corresponding labels. DataFrames are widely used in data science, machine learning, scientific computing, and many other data-intensive fields.

DataFrames are similar to SQL tables or the spreadsheets that you work with in Excel or Calc. In many cases, DataFrames are faster, easier to use, and more powerful than tables or spreadsheets because they’re an integral part of the Python and NumPy ecosystems.

In this course, you’ll learn:

  • What a pandas DataFrame is and how to create one
  • How to access, modify, add, sort, filter, and delete data
  • How to handle missing values
  • How to work with time-series data
  • How to quickly visualize data

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The pandas DataFrame: Working With Data Efficiently

5 Lessons 24m

1. The pandas DataFrame (Overview) 05:45

2. Setting Up Your Environment: Anaconda 02:00

3. Setting Up Your Environment: venv 05:13

4. Getting to Know pandas DataFrames 06:36

5. Working With Rows and Columns in DataFrames 04:28

Creating pandas DataFrames

3 Lessons 20m

1. Creating DataFrames 08:39

2. Creating DataFrames From CSV Files 03:56

3. Understanding DataFrame Attributes 08:02

Accessing and Modifying Data

6 Lessons 31m

1. Modifying Values in DataFrames: Label Indices 04:44

2. Accessing Values in DataFrames 03:55

3. Modifying Values in DataFrames: Accessor Methods 05:43

4. Deleting and Inserting Rows in a DataFrame 05:21

5. Deleting and Inserting Columns in a DataFrame 05:16

6. Creating Columns With Arithmetic Operations and NumPy 06:20

Sorting, Filtering, and Handling Missing Data

6 Lessons 36m

1. Sorting DataFrames 05:28

2. Filtering With Operators 05:25

3. Filtering With .where() and .filter() 07:35

4. Using Statistical Methods on DataFrames 01:53

5. Handling Missing Data 08:16

6. Iterating Over Rows and Columns in DataFrames 07:37

Working With Time Series and Plotting Data

5 Lessons 18m

1. Working With Time Series 03:23

2. Slicing DataFrames Using Datetime Indices 04:30

3. Doing Rolling-Window Analysis 04:31

4. Visualizing DataFrames With Matplotlib 04:35

5. The pandas DataFrame (Summary) 01:35

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About Cesar Aguilar

Cesar Aguilar Cesar Aguilar

Cesar is an avid Pythonista and records video tutorials for Real Python. He enjoys teaching programming to anyone who listens and seeing in others their programming "eureka" moment.

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Each tutorial at Real Python is created by a team of developers so that it meets our high quality standards. The team members who worked on this tutorial are:

Christopher Bailey

Christopher
Martin Breuss

Martin
Sadie Parker

Sadie
Tappan Moore

Tappan
Mirko Stojiljković

Mirko
Geir Arne Hjelle

Geir Arne
Kyle Stratis

Kyle
Joanna Jablonski

Joanna
Aldren Santos

Aldren
Jacob Schmitt

Jacob

Participant Comments

Aldir on Feb. 25, 2022

Well done Cesar! Excelent introduction to Pandas DataFrames. Thank you!

clytn579 on Oct. 3, 2021

This was a great overview of pandas and based on what I have learned, I know where to go for more details to get even deeper knowledge.

aniketbarphe on Sept. 8, 2021

Excellent Course! 10 stars out of 10 stars. Recommended to everyone!

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