Tutorials¶

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

Internal Guides¶

More complex recipes are in the Cookbook.

pandas Cookbook¶

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.

Lessons for new pandas users¶

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

Practical data analysis with Python¶

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:

Exercises for new users¶

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

Modern pandas¶

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

Table Of Contents

Search