jpayne@69: Metadata-Version: 2.1 jpayne@69: Name: pandas jpayne@69: Version: 2.0.3 jpayne@69: Summary: Powerful data structures for data analysis, time series, and statistics jpayne@69: Author-email: The Pandas Development Team jpayne@69: License: BSD 3-Clause License jpayne@69: jpayne@69: Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team jpayne@69: All rights reserved. jpayne@69: jpayne@69: Copyright (c) 2011-2023, Open source contributors. jpayne@69: jpayne@69: Redistribution and use in source and binary forms, with or without jpayne@69: modification, are permitted provided that the following conditions are met: jpayne@69: jpayne@69: * Redistributions of source code must retain the above copyright notice, this jpayne@69: list of conditions and the following disclaimer. jpayne@69: jpayne@69: * Redistributions in binary form must reproduce the above copyright notice, jpayne@69: this list of conditions and the following disclaimer in the documentation jpayne@69: and/or other materials provided with the distribution. jpayne@69: jpayne@69: * Neither the name of the copyright holder nor the names of its jpayne@69: contributors may be used to endorse or promote products derived from jpayne@69: this software without specific prior written permission. jpayne@69: jpayne@69: THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" jpayne@69: AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE jpayne@69: IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE jpayne@69: DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE jpayne@69: FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL jpayne@69: DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR jpayne@69: SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER jpayne@69: CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, jpayne@69: OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE jpayne@69: OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. jpayne@69: jpayne@69: Project-URL: homepage, https://pandas.pydata.org jpayne@69: Project-URL: documentation, https://pandas.pydata.org/docs/ jpayne@69: Project-URL: repository, https://github.com/pandas-dev/pandas jpayne@69: Classifier: Development Status :: 5 - Production/Stable jpayne@69: Classifier: Environment :: Console jpayne@69: Classifier: Intended Audience :: Science/Research jpayne@69: Classifier: License :: OSI Approved :: BSD License jpayne@69: Classifier: Operating System :: OS Independent jpayne@69: Classifier: Programming Language :: Cython jpayne@69: Classifier: Programming Language :: Python jpayne@69: Classifier: Programming Language :: Python :: 3 jpayne@69: Classifier: Programming Language :: Python :: 3 :: Only jpayne@69: Classifier: Programming Language :: Python :: 3.8 jpayne@69: Classifier: Programming Language :: Python :: 3.9 jpayne@69: Classifier: Programming Language :: Python :: 3.10 jpayne@69: Classifier: Programming Language :: Python :: 3.11 jpayne@69: Classifier: Topic :: Scientific/Engineering jpayne@69: Requires-Python: >=3.8 jpayne@69: Description-Content-Type: text/markdown jpayne@69: License-File: LICENSE jpayne@69: License-File: AUTHORS.md jpayne@69: Requires-Dist: python-dateutil (>=2.8.2) jpayne@69: Requires-Dist: pytz (>=2020.1) jpayne@69: Requires-Dist: tzdata (>=2022.1) jpayne@69: Requires-Dist: numpy (>=1.20.3) ; 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jpayne@69: jpayne@69: ----------------- jpayne@69: jpayne@69: # pandas: powerful Python data analysis toolkit jpayne@69: [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) jpayne@69: [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/) jpayne@69: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) jpayne@69: [![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/) jpayne@69: [![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) jpayne@69: [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas) jpayne@69: [![Downloads](https://static.pepy.tech/personalized-badge/pandas?period=month&units=international_system&left_color=black&right_color=orange&left_text=PyPI%20downloads%20per%20month)](https://pepy.tech/project/pandas) jpayne@69: [![Slack](https://img.shields.io/badge/join_Slack-information-brightgreen.svg?logo=slack)](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) jpayne@69: [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) jpayne@69: [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) jpayne@69: [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) jpayne@69: jpayne@69: ## What is it? jpayne@69: jpayne@69: **pandas** is a Python package that provides fast, flexible, and expressive data jpayne@69: structures designed to make working with "relational" or "labeled" data both jpayne@69: easy and intuitive. It aims to be the fundamental high-level building block for jpayne@69: doing practical, **real world** data analysis in Python. Additionally, it has jpayne@69: the broader goal of becoming **the most powerful and flexible open source data jpayne@69: analysis / manipulation tool available in any language**. It is already well on jpayne@69: its way towards this goal. jpayne@69: jpayne@69: ## Main Features jpayne@69: Here are just a few of the things that pandas does well: jpayne@69: jpayne@69: - Easy handling of [**missing data**][missing-data] (represented as jpayne@69: `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data jpayne@69: - Size mutability: columns can be [**inserted and jpayne@69: deleted**][insertion-deletion] from DataFrame and higher dimensional jpayne@69: objects jpayne@69: - Automatic and explicit [**data alignment**][alignment]: objects can jpayne@69: be explicitly aligned to a set of labels, or the user can simply jpayne@69: ignore the labels and let `Series`, `DataFrame`, etc. automatically jpayne@69: align the data for you in computations jpayne@69: - Powerful, flexible [**group by**][groupby] functionality to perform jpayne@69: split-apply-combine operations on data sets, for both aggregating jpayne@69: and transforming data jpayne@69: - Make it [**easy to convert**][conversion] ragged, jpayne@69: differently-indexed data in other Python and NumPy data structures jpayne@69: into DataFrame objects jpayne@69: - Intelligent label-based [**slicing**][slicing], [**fancy jpayne@69: indexing**][fancy-indexing], and [**subsetting**][subsetting] of jpayne@69: large data sets jpayne@69: - Intuitive [**merging**][merging] and [**joining**][joining] data jpayne@69: sets jpayne@69: - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of jpayne@69: data sets jpayne@69: - [**Hierarchical**][mi] labeling of axes (possible to have multiple jpayne@69: labels per tick) jpayne@69: - Robust IO tools for loading data from [**flat files**][flat-files] jpayne@69: (CSV and delimited), [**Excel files**][excel], [**databases**][db], jpayne@69: and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] jpayne@69: - [**Time series**][timeseries]-specific functionality: date range jpayne@69: generation and frequency conversion, moving window statistics, jpayne@69: date shifting and lagging jpayne@69: jpayne@69: jpayne@69: [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html jpayne@69: [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion jpayne@69: [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures jpayne@69: [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine jpayne@69: [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe jpayne@69: [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges jpayne@69: [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced jpayne@69: [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing jpayne@69: [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging jpayne@69: [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index jpayne@69: [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html jpayne@69: [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html jpayne@69: [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex jpayne@69: [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files jpayne@69: [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files jpayne@69: [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries jpayne@69: [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables jpayne@69: [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality jpayne@69: jpayne@69: ## Where to get it jpayne@69: The source code is currently hosted on GitHub at: jpayne@69: https://github.com/pandas-dev/pandas jpayne@69: jpayne@69: Binary installers for the latest released version are available at the [Python jpayne@69: Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/). jpayne@69: jpayne@69: ```sh jpayne@69: # conda jpayne@69: conda install pandas jpayne@69: ``` jpayne@69: jpayne@69: ```sh jpayne@69: # or PyPI jpayne@69: pip install pandas jpayne@69: ``` jpayne@69: jpayne@69: ## Dependencies jpayne@69: - [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org) jpayne@69: - [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html) jpayne@69: - [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz) jpayne@69: jpayne@69: 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. jpayne@69: jpayne@69: ## Installation from sources jpayne@69: To install pandas from source you need [Cython](https://cython.org/) in addition to the normal jpayne@69: dependencies above. Cython can be installed from PyPI: jpayne@69: jpayne@69: ```sh jpayne@69: pip install cython jpayne@69: ``` jpayne@69: jpayne@69: In the `pandas` directory (same one where you found this file after jpayne@69: cloning the git repo), execute: jpayne@69: jpayne@69: ```sh jpayne@69: python setup.py install jpayne@69: ``` jpayne@69: jpayne@69: or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable): jpayne@69: jpayne@69: jpayne@69: ```sh jpayne@69: python -m pip install -e . --no-build-isolation --no-use-pep517 jpayne@69: ``` jpayne@69: jpayne@69: or alternatively jpayne@69: jpayne@69: ```sh jpayne@69: python setup.py develop jpayne@69: ``` jpayne@69: jpayne@69: See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source). jpayne@69: jpayne@69: ## License jpayne@69: [BSD 3](LICENSE) jpayne@69: jpayne@69: ## Documentation jpayne@69: The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable jpayne@69: jpayne@69: ## Background jpayne@69: Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and jpayne@69: has been under active development since then. jpayne@69: jpayne@69: ## Getting Help jpayne@69: jpayne@69: For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas). jpayne@69: Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). jpayne@69: jpayne@69: ## Discussion and Development jpayne@69: 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 [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions. jpayne@69: jpayne@69: ## Contributing to pandas [![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas) jpayne@69: jpayne@69: All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. jpayne@69: jpayne@69: A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**. jpayne@69: jpayne@69: 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. jpayne@69: jpayne@69: 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). jpayne@69: jpayne@69: 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! jpayne@69: jpayne@69: Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack). jpayne@69: jpayne@69: 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)