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.. pandas documentation master file, created by
*********************************************
pandas: powerful Python data analysis toolkit
*********************************************
`PDF Version <pandas.pdf>`__
`Zipped HTML <pandas.zip>`__
.. module:: pandas
**Date**: |today| **Version**: |version|
**Binary Installers:** http://pypi.python.org/pypi/pandas
**Source Repository:** http://github.com/pydata/pandas
**Issues & Ideas:** https://github.com/pydata/pandas/issues
**Q&A Support:** http://stackoverflow.com/questions/tagged/pandas
**Developer Mailing List:** http://groups.google.com/group/pydata
**pandas** is a `Python <http://www.python.org>`__ package providing fast,
flexible, and expressive data structures designed to make working with
"relational" or "labeled" data both easy and intuitive. It aims to be the
fundamental high-level building block for doing practical, **real world** data
analysis in Python. Additionally, it has the broader goal of becoming **the
most powerful and flexible open source data analysis / manipulation tool
available in any language**. It is already well on its way toward this goal.
pandas is well suited for many different kinds of data:
- Tabular data with heterogeneously-typed columns, as in an SQL table or
Excel spreadsheet
- Ordered and unordered (not necessarily fixed-frequency) time series data.
- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and
column labels
- Any other form of observational / statistical data sets. The data actually
need not be labeled at all to be placed into a pandas data structure
The two primary data structures of pandas, :class:`Series` (1-dimensional)
and :class:`DataFrame` (2-dimensional), handle the vast majority of typical use
cases in finance, statistics, social science, and many areas of
engineering. For R users, :class:`DataFrame` provides everything that R's
``data.frame`` provides and much more. pandas is built on top of `NumPy
<http://www.numpy.org>`__ and is intended to integrate well within a scientific
computing environment with many other 3rd party libraries.
Here are just a few of the things that pandas does well:
- Easy handling of **missing data** (represented as NaN) in floating point as
well as non-floating point data
- Size mutability: columns can be **inserted and deleted** from DataFrame and
higher dimensional objects
- Automatic and explicit **data alignment**: objects can be explicitly
aligned to a set of labels, or the user can simply ignore the labels and
let `Series`, `DataFrame`, etc. automatically align the data for you in
computations
- Powerful, flexible **group by** functionality to perform
split-apply-combine operations on data sets, for both aggregating and
transforming data
- Make it **easy to convert** ragged, differently-indexed data in other
Python and NumPy data structures into DataFrame objects
- Intelligent label-based **slicing**, **fancy indexing**, and **subsetting**
of large data sets
- Intuitive **merging** and **joining** data sets
- Flexible **reshaping** and pivoting of data sets
- **Hierarchical** labeling of axes (possible to have multiple labels per
tick)
- Robust IO tools for loading data from **flat files** (CSV and delimited),
Excel files, databases, and saving / loading data from the ultrafast **HDF5
format**
- **Time series**-specific functionality: date range generation and frequency
conversion, moving window statistics, moving window linear regressions,
date shifting and lagging, etc.
Many of these principles are here to address the shortcomings frequently
experienced using other languages / scientific research environments. For data
scientists, working with data is typically divided into multiple stages:
munging and cleaning data, analyzing / modeling it, then organizing the results
of the analysis into a form suitable for plotting or tabular display. pandas
is the ideal tool for all of these tasks.
Some other notes
- pandas is **fast**. Many of the low-level algorithmic bits have been
extensively tweaked in `Cython <http://cython.org>`__ code. However, as with
anything else generalization usually sacrifices performance. So if you focus
on one feature for your application you may be able to create a faster
specialized tool.
- pandas is a dependency of `statsmodels
<http://statsmodels.sourceforge.net>`__, making it an important part of the
statistical computing ecosystem in Python.
- pandas has been used extensively in production in financial applications.
.. note::
This documentation assumes general familiarity with NumPy. If you haven't
used NumPy much or at all, do invest some time in `learning about NumPy
<http://docs.scipy.org>`__ first.
See the package overview for more detail about what's in the library.
.. toctree::
:maxdepth: 3
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whatsnew
install
faq
overview
10min
tutorials
cookbook
dsintro
basics
options
indexing
computation
missing_data
groupby
merging
reshaping
timeseries
visualization
rplot
io
remote_data
enhancingperf
sparse
gotchas
r_interface
ecosystem
comparison_with_r
comparison_with_sql
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api
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contributing
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