Categorical Data

New in version 0.15. Note

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Enhancing Performance

Cython (Writing C extensions for pandas) For many use cases writing pandas in pure python and numpy is sufficient. In some computationally

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Visualization

We use the standard convention for referencing the matplotlib API: In [1]: import matplotlib.pyplot as plt The plots in

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Working with Text Data

Series and Index are equipped with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods

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Merge, join, and concatenate

pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational

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Reshaping and Pivot Tables

Reshaping by pivoting DataFrame objects Data is often stored in CSV files or databases in so-called ?stacked? or ?record? format:

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Cookbook

This is a repository for short and sweet examples and links for useful pandas recipes. We encourage users to add to this documentation. Adding interesting links and/or

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Working with missing data

In this section, we will discuss missing (also referred to as NA) values in pandas. Note

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Essential Basic Functionality

Here we discuss a lot of the essential functionality common to the pandas data structures. Here?s how to create some of the objects used in the examples from the

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Package overview

pandas consists of the following things A set of labeled array data structures, the primary of which are Series and DataFrame

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