The Pandas DataFrame: Make Working With Data Delightful

The pandas DataFrame: Working With Data Efficiently

Cesar Aguilar
Cesar Aguilar 25 Lessons 2h 10m intermediate data-science
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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. 1. The pandas DataFrame (Overview)05:45
  2. 2. Setting Up Your Environment: Anaconda02:00
  3. 3. Setting Up Your Environment: venv05:13
  4. 4. Getting to Know pandas DataFrames06:36
  5. 5. Working With Rows and Columns in DataFrames04:28

Creating pandas DataFrames

3 Lessons 20m

  1. 1. Creating DataFrames08:39
  2. 2. Creating DataFrames From CSV Files03:56
  3. 3. Understanding DataFrame Attributes08:02

Accessing and Modifying Data

6 Lessons 31m

  1. 1. Modifying Values in DataFrames: Label Indices04:44
  2. 2. Accessing Values in DataFrames03:55
  3. 3. Modifying Values in DataFrames: Accessor Methods05:43
  4. 4. Deleting and Inserting Rows in a DataFrame05:21
  5. 5. Deleting and Inserting Columns in a DataFrame05:16
  6. 6. Creating Columns With Arithmetic Operations and NumPy06:20

Sorting, Filtering, and Handling Missing Data

6 Lessons 36m

  1. 1. Sorting DataFrames05:28
  2. 2. Filtering With Operators05:25
  3. 3. Filtering With .where() and .filter()07:35
  4. 4. Using Statistical Methods on DataFrames01:53
  5. 5. Handling Missing Data08:16
  6. 6. Iterating Over Rows and Columns in DataFrames07:37

Working With Time Series and Plotting Data

5 Lessons 18m

  1. 1. Working With Time Series03:23
  2. 2. Slicing DataFrames Using Datetime Indices04:30
  3. 3. Doing Rolling-Window Analysis04:31
  4. 4. Visualizing DataFrames With Matplotlib04:35
  5. 5. The pandas DataFrame (Summary)01:35
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About 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

Martin

Sadie

Tappan

Mirko

Geir Arne

Kyle

Joanna

Aldren

Jacob

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Pandas: How to Read and Write Files

Reading and Writing Files With pandas

Participant Comments

Avatar image for Aldir

Aldir on Feb. 25, 2022

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

Avatar image for clytn579

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.

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aniketbarphe on Sept. 8, 2021

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

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