You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
Jeremy Gray 12b3264d17 DOC: typo: comverted -> converted (#15977) 5 years ago
.github Convert readthedocs links for their .org -> .io migration for hosted projects (#14406) 6 years ago
LICENSES DOC: Update old Google Code and SourceForge links (#13534) 6 years ago
asv_bench cache and remove boxing (#14931) 6 years ago
bench CLN: Removed SparsePanel 6 years ago
ci BLD: cleaner 3.6 deps install 6 years ago
conda.recipe CI: remove leading v from built versions 6 years ago
doc Changed pandas-qt python2/3 friendly qtpandas. (#14818) 6 years ago
pandas DOC: typo: comverted -> converted (#15977) 5 years ago
scripts DOC: pydata/pandas -> pandas-dev/pandas (#14409) 6 years ago
vb_suite DOC: pydata/pandas -> pandas-dev/pandas (#14409) 6 years ago
.binstar.yml update conda recipe to make import only tests 7 years ago
.coveragerc TST: Omit tests folders from coverage 6 years ago
.gitattributes CI: use versioneer, for PEP440 version strings #9518 7 years ago
.gitignore [Backport #14723] MAINT: Ignore .pxi files 6 years ago
.travis.yml BLD: use correct path for travis 6 years ago
LICENSE RLS: Version 0.10.0 final 10 years ago
MANIFEST.in CI: use versioneer, for PEP440 version strings #9518 7 years ago
Makefile BLD: spring cleaning on Makefile 8 years ago
README.md DOC: small update to install.rst page (#14115) 6 years ago
RELEASE.md DOC: update RELEASE.md to point to stable whatsnew 8 years ago
appveyor.yml BLD: add in build conflict resolution to appeveyor.yml 6 years ago
build_dist.sh BLD: edit release script 6 years ago
codecov.yml BUG: Correct KeyError from matplotlib when processing Series yerr 6 years ago
release_stats.sh add args to release_stats.sh 7 years ago
setup.cfg PEP: pandas/core round 2 with yapf and add to setup.cfg 7 years ago
setup.py BLD: clean .pxi when cleaning (#14766) 6 years ago
test.bat TST: add windows test.bat 7 years ago
test.sh micro + nanosecond time support 9 years ago
test_fast.sh TST: test_fast.sh and test_multi.sh should skip network tests 9 years ago
test_multi.sh TST: test_fast.sh and test_multi.sh should skip network tests 9 years ago
test_perf.sh BLD: make test_perf.sh work on OSX too 9 years ago
test_rebuild.sh TST: pass cmd line args to test scripts so can append -v etc 10 years ago
tox.ini COMPAT: drop suppport for python 2.6, #7718 7 years ago
versioneer.py CI: use versioneer, for PEP440 version strings #9518 7 years ago

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/pydata/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