This is a guide to many pandas tutorials, geared mainly for new users.

pandas’ own 10 Minutes to pandas.

More complex recipes are in the Cookbook.

A handy pandas cheat sheet.

The goal of this 2015 cookbook (by Julia Evans) is to give you some concrete examples for getting started with pandas. These are examples with real-world data, and all the bugs and weirdness that entails.

Here are links to the v0.2 release. For an up-to-date table of contents, see the pandas-cookbook GitHub repository. To run the examples in this tutorial, you’ll need to clone the GitHub repository and get IPython Notebook running. See How to use this cookbook.

- A quick tour of the IPython Notebook: Shows off IPython’s awesome tab completion and magic functions.
- Chapter 1: Reading your data into pandas is pretty much the easiest thing. Even when the encoding is wrong!
- Chapter 2: It’s not totally obvious how to select data from a pandas dataframe. Here we explain the basics (how to take slices and get columns)
- Chapter 3: Here we get into serious slicing and dicing and learn how to filter dataframes in complicated ways, really fast.
- Chapter 4: Groupby/aggregate is seriously my favorite thing about pandas and I use it all the time. You should probably read this.
- Chapter 5: Here you get to find out if it’s cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes.
- Chapter 6: Strings with pandas are great. It has all these vectorized string operations and they’re the best. We will turn a bunch of strings containing “Snow” into vectors of numbers in a trice.
- Chapter 7: Cleaning up messy data is never a joy, but with pandas it’s easier.
- Chapter 8: Parsing Unix timestamps is confusing at first but it turns out to be really easy.
- Chapter 9: Reading data from SQL databases.

For more resources, please visit the main repository.

- 01 - Lesson: - Importing libraries - Creating data sets - Creating data frames - Reading from CSV - Exporting to CSV - Finding maximums - Plotting data
- 02 - Lesson: - Reading from TXT - Exporting to TXT - Selecting top/bottom records - Descriptive statistics - Grouping/sorting data
- 03 - Lesson: - Creating functions - Reading from EXCEL - Exporting to EXCEL - Outliers - Lambda functions - Slice and dice data
- 04 - Lesson: - Adding/deleting columns - Index operations
- 05 - Lesson: - Stack/Unstack/Transpose functions
- 06 - Lesson: - GroupBy function
- 07 - Lesson: - Ways to calculate outliers
- 08 - Lesson: - Read from Microsoft SQL databases
- 09 - Lesson: - Export to CSV/EXCEL/TXT
- 10 - Lesson: - Converting between different kinds of formats
- 11 - Lesson: - Combining data from various sources

This guide is a comprehensive introduction to the data analysis process using the Python data ecosystem and an interesting open dataset. There are four sections covering selected topics as follows:

Practice your skills with real data sets and exercises. For more resources, please visit the main repository.

- 01 - Getting & Knowing Your Data
- 02 - Filtering & Sorting
- 03 - Grouping
- 04 - Apply
- 05 - Merge
- 06 - Stats
- 07 - Visualization
- 08 - Creating Series and DataFrames
- 09 - Time Series
- 10 - Deleting

Tutorial series written in 2016 by Tom Augspurger. The source may be found in the GitHub repository TomAugspurger/effective-pandas.

- Pandas From The Ground Up (2015) (2:24) GitHub repo
- Introduction Into Pandas (2016) (1:28) GitHub repo
- Pandas: .head() to .tail() (2016) (1:26) GitHub repo

- Wes McKinney’s (pandas BDFL) blog
- Statistical analysis made easy in Python with SciPy and pandas DataFrames, by Randal Olson
- Statistical Data Analysis in Python, tutorial videos, by Christopher Fonnesbeck from SciPy 2013
- Financial analysis in Python, by Thomas Wiecki
- Intro to pandas data structures, by Greg Reda
- Pandas and Python: Top 10, by Manish Amde
- Pandas Tutorial, by Mikhail Semeniuk
- Pandas DataFrames Tutorial, by Karlijn Willems
- A concise tutorial with real life examples

© 2008–2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team

Licensed under the 3-clause BSD License.

http://pandas.pydata.org/pandas-docs/version/0.23.4/tutorials.html