Package: pandas
pandas: powerful Python data analysis toolkitTestingPackageMetaWhat is it?pandasis a Python package that provides 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 worlddata analysis in Python. Additionally, it has
the broader goal of becomingthe most powerful and flexible open source data
analysis / manipulation tool available in any language. It is already well on
its way towards this goal.Table of ContentsMain FeaturesWhere to get itDependenciesInstallation from sourcesLicenseDocumentationBackgroundGetting HelpDiscussion and DevelopmentContributing to pandasMain FeaturesHere are just a few of the things that pandas does well:Easy handling ofmissing data(represented asNaN,NA, orNaT) in floating point as well as non-floating point dataSize mutability: columns can beinserted and
deletedfrom DataFrame and higher dimensional
objectsAutomatic and explicitdata alignment: objects can
be explicitly aligned to a set of labels, or the user can simply
ignore the labels and letSeries,DataFrame, etc. automatically
align the data for you in computationsPowerful, flexiblegroup byfunctionality to perform
split-apply-combine operations on data sets, for both aggregating
and transforming dataMake iteasy to convertragged,
differently-indexed data in other Python and NumPy data structures
into DataFrame objectsIntelligent label-basedslicing,fancy
indexing, andsubsettingof
large data setsIntuitivemergingandjoiningdata
setsFlexiblereshapingandpivotingof
data setsHierarchicallabeling of axes (possible to have multiple
labels per tick)Robust IO tools for loading data fromflat files(CSV and delimited),Excel files,databases,
and saving/loading data from the ultrafastHDF5 formatTime series-specific functionality: date range
generation and frequency conversion, moving window statistics,
date shifting and laggingWhere to get itThe source code is currently hosted on GitHub at:https://github.com/pandas-dev/pandasBinary installers for the latest released version are available at thePython
Package Index (PyPI)and onConda.# condacondainstall-cconda-forgepandas# or PyPIpipinstallpandasThe list of changes to pandas between each release can be foundhere. For full
details, see the commit logs athttps://github.com/pandas-dev/pandas.DependenciesNumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrayspython-dateutil - Provides powerful extensions to the standard datetime modulepytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculationsSee thefull installation instructionsfor minimum supported versions of required, recommended and optional dependencies.Installation from sourcesTo install pandas from source you needCythonin addition to the normal
dependencies above. Cython can be installed from PyPI:pipinstallcythonIn thepandasdirectory (same one where you found this file after
cloning the git repo), execute:pipinstall.or for installing indevelopment mode:python-mpipinstall-ve.--no-build-isolation--config-settings=editable-verbose=trueSee the full instructions forinstalling from source.LicenseBSD 3DocumentationThe official documentation is hosted onPyData.org.BackgroundWork onpandasstarted atAQR(a quantitative hedge fund) in 2008 and
has been under active development since then.Getting HelpFor usage questions, the best place to go to isStackOverflow.
Further, general questions and discussions can also take place on thepydata mailing list.Discussion and DevelopmentMost development discussions take place on GitHub in this repo, via theGitHub issue tracker.Further, thepandas-dev mailing listcan also be used for specialized discussions or design issues, and aSlack channelis available for quick development related questions.There are also frequentcommunity meetingsfor project maintainers open to the community as well as monthlynew contributor meetingsto help support new contributors.Additional information on the communication channels can be found on thecontributor communitypage.Contributing to pandasAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.A detailed overview on how to contribute can be found in thecontributing guide.If you are simply looking to start working with the pandas codebase, navigate to theGitHub "issues" taband start looking through interesting issues. There are a number of issues listed underDocsandgood first issuewhere you could start out.You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is tosubscribe to pandas on CodeTriage.Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!Feel free to ask questions on themailing listor onSlack.As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at:Contributor Code of ConductGo to Top