Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python HTML C Shell C++ R Other
Latest commit 2fa33fb Dec 23, 2016 @Dr-Irv Dr-Irv committed with jreback BUG: Incorrect index label displayed on MultiIndex DataFrame
closes #14882
closes #14975
Permalink
Failed to load latest commit information.
.github COMPAT: Require a problem description in issues Nov 18, 2016
LICENSES DOC: Update old Google Code and SourceForge links (#13534) Jul 5, 2016
asv_bench PERF: Cythonize Groupby.cummin/cummax (#15048) Jan 11, 2017
bench CLN: Removed SparsePanel Jul 26, 2016
ci CI: have 34_SLOW build use matplotlib latest from conda-forge (#15170) Jan 19, 2017
conda.recipe CI: remove leading v from built versions Mar 10, 2016
doc BUG: Incorrect index label displayed on MultiIndex DataFrame Jan 21, 2017
pandas BUG: Incorrect index label displayed on MultiIndex DataFrame Jan 21, 2017
scripts BLD: move + update build script (#14991) Dec 26, 2016
vb_suite DOC: pydata/pandas -> pandas-dev/pandas (#14409) Oct 13, 2016
.binstar.yml update conda recipe to make import only tests Sep 6, 2015
.coveragerc TST: Omit tests folders from coverage Mar 31, 2016
.gitattributes CI: use versioneer, for PEP440 version strings #9518 Jul 6, 2015
.gitignore MAINT: Ignore .pxi files Nov 25, 2016
.travis.yml DOC/BLD: doc building with python 3 (#15012) Dec 30, 2016
LICENSE RLS: Version 0.10.0 final Dec 17, 2012
MANIFEST.in CI: use versioneer, for PEP440 version strings #9518 Jul 6, 2015
Makefile BLD: spring cleaning on Makefile Apr 6, 2014
README.md DOC: update readme for repo move (#14470) Oct 22, 2016
RELEASE.md DOC: Update GitHub org from pydata to pandas-dev (#14575) Nov 4, 2016
appveyor.yml CI: fix conda version on appveyor Jan 14, 2017
codecov.yml BUG: Correct KeyError from matplotlib when processing Series yerr May 13, 2016
release_stats.sh add args to release_stats.sh Nov 20, 2015
setup.cfg PEP: pandas/core round 2 with yapf and add to setup.cfg Jan 16, 2016
setup.py ENH: Create and propagate UInt64Index Jan 17, 2017
test.bat TST: add windows test.bat Sep 3, 2015
test.sh micro + nanosecond time support Sep 30, 2013
test_fast.sh TST: test_fast.sh and test_multi.sh should skip network tests Oct 19, 2013
test_multi.sh TST: test_fast.sh and test_multi.sh should skip network tests Oct 19, 2013
test_perf.sh BLD: make test_perf.sh work on OSX too Sep 9, 2013
test_rebuild.sh TST: pass cmd line args to test scripts so can append -v etc May 7, 2012
tox.ini COMPAT: drop suppport for python 2.6, #7718 Jan 7, 2016
versioneer.py CI: use versioneer, for PEP440 version strings #9518 Jul 6, 2015

README.md



pandas: powerful Python data analysis toolkit

Latest Release latest release
latest release
Package Status status
License license
Build Status travis build status
appveyor build status
Coverage coverage
Conda conda downloads
PyPI pypi downloads

https://gitter.im/pydata/pandas

What is it

pandas is a Python 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.

Main Features

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.

Where to get it

The source code is currently hosted on GitHub at: http://github.com/pandas-dev/pandas

Binary installers for the latest released version are available at the Python package index and on conda.

# conda
conda install pandas
# or PyPI
pip install pandas

Dependencies

See the full installation instructions for recommended and optional dependencies.

Installation from sources

To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from pypi:

pip install cython

In the pandas directory (same one where you found this file after cloning the git repo), execute:

python setup.py install

or for installing in development mode:

python setup.py develop

Alternatively, you can use pip if you want all the dependencies pulled in automatically (the -e option is for installing it in development mode):

pip install -e .

On Windows, you will need to install MinGW and execute:

python setup.py build --compiler=mingw32
python setup.py install

See http://pandas.pydata.org/ for more information.

License

BSD

Documentation

The official documentation is hosted on PyData.org: http://pandas.pydata.org/

The Sphinx documentation should provide a good starting point for learning how to use the library. Expect the docs to continue to expand as time goes on.

Background

Work on pandas started at AQR (a quantitative hedge fund) in 2008 and has been under active development since then.

Discussion and Development

Since pandas development is related to a number of other scientific Python projects, questions are welcome on the scipy-user mailing list. Specialized discussions or design issues should take place on the PyData mailing list / Google group:

https://groups.google.com/forum/#!forum/pydata