.. 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

    {% if single -%}
    {{ single }}
    {% endif -%}
    {%if not single -%}
    whatsnew
    install
    faq
    overview
    10min
    tutorials
    cookbook
    dsintro
    basics
    text
    options
    indexing
    advanced
    computation
    missing_data
    groupby
    merging
    reshaping
    timeseries
    timedeltas
    categorical
    visualization
    io
    remote_data
    enhancingperf
    sparse
    gotchas
    r_interface
    ecosystem
    comparison_with_r
    comparison_with_sql
    {% endif -%}
    {% if api -%}
    api
    {% endif -%}
    {%if not single -%}
    contributing
    internals
    release
    {% endif -%}
