213 lines
12 KiB
Plaintext
213 lines
12 KiB
Plaintext
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Metadata-Version: 2.1
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Name: pandas
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Version: 1.5.2
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Summary: Powerful data structures for data analysis, time series, and statistics
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Home-page: https://pandas.pydata.org
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Author: The Pandas Development Team
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Author-email: pandas-dev@python.org
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License: BSD-3-Clause
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Project-URL: Bug Tracker, https://github.com/pandas-dev/pandas/issues
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Project-URL: Documentation, https://pandas.pydata.org/pandas-docs/stable
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Project-URL: Source Code, https://github.com/pandas-dev/pandas
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Platform: any
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Environment :: Console
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Classifier: Intended Audience :: Science/Research
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Classifier: License :: OSI Approved :: BSD License
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Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: Cython
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3 :: Only
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Classifier: Programming Language :: Python :: 3.8
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Programming Language :: Python :: 3.11
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Classifier: Topic :: Scientific/Engineering
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Requires-Python: >=3.8
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Description-Content-Type: text/markdown
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Requires-Dist: python-dateutil (>=2.8.1)
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Requires-Dist: pytz (>=2020.1)
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Requires-Dist: numpy (>=1.20.3) ; python_version < "3.10"
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Requires-Dist: numpy (>=1.21.0) ; python_version >= "3.10"
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Requires-Dist: numpy (>=1.23.2) ; python_version >= "3.11"
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Provides-Extra: test
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Requires-Dist: hypothesis (>=5.5.3) ; extra == 'test'
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Requires-Dist: pytest (>=6.0) ; extra == 'test'
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Requires-Dist: pytest-xdist (>=1.31) ; extra == 'test'
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<div align="center">
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<img src="https://pandas.pydata.org/static/img/pandas.svg"><br>
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</div>
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-----------------
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# pandas: powerful Python data analysis toolkit
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[](https://pypi.org/project/pandas/)
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[](https://anaconda.org/anaconda/pandas/)
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[](https://doi.org/10.5281/zenodo.3509134)
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[](https://pypi.org/project/pandas/)
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[](https://github.com/pandas-dev/pandas/blob/main/LICENSE)
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[](https://codecov.io/gh/pandas-dev/pandas)
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[](https://pepy.tech/project/pandas)
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[](https://gitter.im/pydata/pandas)
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[](https://numfocus.org)
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[](https://github.com/psf/black)
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[](https://pycqa.github.io/isort/)
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## What is it?
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**pandas** is a Python package that provides fast, flexible, and expressive data
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structures designed to make working with "relational" or "labeled" data both
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easy and intuitive. It aims to be the fundamental high-level building block for
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doing practical, **real world** data analysis in Python. Additionally, it has
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the broader goal of becoming **the most powerful and flexible open source data
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analysis / manipulation tool available in any language**. It is already well on
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its way towards this goal.
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## Main Features
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Here are just a few of the things that pandas does well:
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- Easy handling of [**missing data**][missing-data] (represented as
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`NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
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- Size mutability: columns can be [**inserted and
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deleted**][insertion-deletion] from DataFrame and higher dimensional
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objects
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- Automatic and explicit [**data alignment**][alignment]: objects can
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be explicitly aligned to a set of labels, or the user can simply
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ignore the labels and let `Series`, `DataFrame`, etc. automatically
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align the data for you in computations
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- Powerful, flexible [**group by**][groupby] functionality to perform
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split-apply-combine operations on data sets, for both aggregating
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and transforming data
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- Make it [**easy to convert**][conversion] ragged,
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differently-indexed data in other Python and NumPy data structures
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into DataFrame objects
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- Intelligent label-based [**slicing**][slicing], [**fancy
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indexing**][fancy-indexing], and [**subsetting**][subsetting] of
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large data sets
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- Intuitive [**merging**][merging] and [**joining**][joining] data
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sets
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- Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
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data sets
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- [**Hierarchical**][mi] labeling of axes (possible to have multiple
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labels per tick)
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- Robust IO tools for loading data from [**flat files**][flat-files]
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(CSV and delimited), [**Excel files**][excel], [**databases**][db],
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and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
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- [**Time series**][timeseries]-specific functionality: date range
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generation and frequency conversion, moving window statistics,
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date shifting and lagging
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[missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
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[insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
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[alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
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[groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
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[conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
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[slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
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[fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
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[subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
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[merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
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[joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
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[reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
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[pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
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[mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
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[flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
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[excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
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[db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
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[hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
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[timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality
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## Where to get it
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The source code is currently hosted on GitHub at:
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https://github.com/pandas-dev/pandas
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Binary installers for the latest released version are available at the [Python
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Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).
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```sh
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# conda
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conda install pandas
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```
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```sh
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# or PyPI
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pip install pandas
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```
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## Dependencies
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- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
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- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
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- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)
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See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.
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## Installation from sources
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To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
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dependencies above. Cython can be installed from PyPI:
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```sh
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pip install cython
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```
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In the `pandas` directory (same one where you found this file after
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cloning the git repo), execute:
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```sh
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python setup.py install
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```
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or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
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```sh
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python -m pip install -e . --no-build-isolation --no-use-pep517
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```
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If you have `make`, you can also use `make develop` to run the same command.
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or alternatively
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```sh
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python setup.py develop
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```
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See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source).
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## License
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[BSD 3](LICENSE)
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## Documentation
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The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable
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## Background
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Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
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has been under active development since then.
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## Getting Help
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For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).
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Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
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## Discussion and Development
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Most development discussions take place on GitHub in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions.
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## Contributing to pandas [](https://www.codetriage.com/pandas-dev/pandas)
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All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
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A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
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If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out.
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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 to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas).
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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!
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Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Gitter](https://gitter.im/pydata/pandas).
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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 Conduct](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)
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