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author jpayne
date Tue, 18 Mar 2025 17:55:14 -0400
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1 Metadata-Version: 2.1
2 Name: pandas
3 Version: 2.0.3
4 Summary: Powerful data structures for data analysis, time series, and statistics
5 Author-email: The Pandas Development Team <pandas-dev@python.org>
6 License: BSD 3-Clause License
7
8 Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
9 All rights reserved.
10
11 Copyright (c) 2011-2023, Open source contributors.
12
13 Redistribution and use in source and binary forms, with or without
14 modification, are permitted provided that the following conditions are met:
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16 * Redistributions of source code must retain the above copyright notice, this
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19 * Redistributions in binary form must reproduce the above copyright notice,
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27 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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38 Project-URL: homepage, https://pandas.pydata.org
39 Project-URL: documentation, https://pandas.pydata.org/docs/
40 Project-URL: repository, https://github.com/pandas-dev/pandas
41 Classifier: Development Status :: 5 - Production/Stable
42 Classifier: Environment :: Console
43 Classifier: Intended Audience :: Science/Research
44 Classifier: License :: OSI Approved :: BSD License
45 Classifier: Operating System :: OS Independent
46 Classifier: Programming Language :: Cython
47 Classifier: Programming Language :: Python
48 Classifier: Programming Language :: Python :: 3
49 Classifier: Programming Language :: Python :: 3 :: Only
50 Classifier: Programming Language :: Python :: 3.8
51 Classifier: Programming Language :: Python :: 3.9
52 Classifier: Programming Language :: Python :: 3.10
53 Classifier: Programming Language :: Python :: 3.11
54 Classifier: Topic :: Scientific/Engineering
55 Requires-Python: >=3.8
56 Description-Content-Type: text/markdown
57 License-File: LICENSE
58 License-File: AUTHORS.md
59 Requires-Dist: python-dateutil (>=2.8.2)
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61 Requires-Dist: tzdata (>=2022.1)
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154 Provides-Extra: test
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159 Provides-Extra: xml
160 Requires-Dist: lxml (>=4.6.3) ; extra == 'xml'
161
162 <div align="center">
163 <img src="https://pandas.pydata.org/static/img/pandas.svg"><br>
164 </div>
165
166 -----------------
167
168 # pandas: powerful Python data analysis toolkit
169 [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/)
170 [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/)
171 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134)
172 [![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/)
173 [![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE)
174 [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas)
175 [![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)
176 [![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)
177 [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)
178 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
179 [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
180
181 ## What is it?
182
183 **pandas** is a Python package that provides fast, flexible, and expressive data
184 structures designed to make working with "relational" or "labeled" data both
185 easy and intuitive. It aims to be the fundamental high-level building block for
186 doing practical, **real world** data analysis in Python. Additionally, it has
187 the broader goal of becoming **the most powerful and flexible open source data
188 analysis / manipulation tool available in any language**. It is already well on
189 its way towards this goal.
190
191 ## Main Features
192 Here are just a few of the things that pandas does well:
193
194 - Easy handling of [**missing data**][missing-data] (represented as
195 `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
196 - Size mutability: columns can be [**inserted and
197 deleted**][insertion-deletion] from DataFrame and higher dimensional
198 objects
199 - Automatic and explicit [**data alignment**][alignment]: objects can
200 be explicitly aligned to a set of labels, or the user can simply
201 ignore the labels and let `Series`, `DataFrame`, etc. automatically
202 align the data for you in computations
203 - Powerful, flexible [**group by**][groupby] functionality to perform
204 split-apply-combine operations on data sets, for both aggregating
205 and transforming data
206 - Make it [**easy to convert**][conversion] ragged,
207 differently-indexed data in other Python and NumPy data structures
208 into DataFrame objects
209 - Intelligent label-based [**slicing**][slicing], [**fancy
210 indexing**][fancy-indexing], and [**subsetting**][subsetting] of
211 large data sets
212 - Intuitive [**merging**][merging] and [**joining**][joining] data
213 sets
214 - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
215 data sets
216 - [**Hierarchical**][mi] labeling of axes (possible to have multiple
217 labels per tick)
218 - Robust IO tools for loading data from [**flat files**][flat-files]
219 (CSV and delimited), [**Excel files**][excel], [**databases**][db],
220 and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
221 - [**Time series**][timeseries]-specific functionality: date range
222 generation and frequency conversion, moving window statistics,
223 date shifting and lagging
224
225
226 [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
227 [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
228 [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
229 [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
230 [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
231 [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
232 [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
233 [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
234 [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
235 [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
236 [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
237 [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
238 [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
239 [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
240 [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
241 [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
242 [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
243 [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality
244
245 ## Where to get it
246 The source code is currently hosted on GitHub at:
247 https://github.com/pandas-dev/pandas
248
249 Binary installers for the latest released version are available at the [Python
250 Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).
251
252 ```sh
253 # conda
254 conda install pandas
255 ```
256
257 ```sh
258 # or PyPI
259 pip install pandas
260 ```
261
262 ## Dependencies
263 - [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
264 - [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
265 - [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)
266
267 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.
268
269 ## Installation from sources
270 To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
271 dependencies above. Cython can be installed from PyPI:
272
273 ```sh
274 pip install cython
275 ```
276
277 In the `pandas` directory (same one where you found this file after
278 cloning the git repo), execute:
279
280 ```sh
281 python setup.py install
282 ```
283
284 or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
285
286
287 ```sh
288 python -m pip install -e . --no-build-isolation --no-use-pep517
289 ```
290
291 or alternatively
292
293 ```sh
294 python setup.py develop
295 ```
296
297 See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source).
298
299 ## License
300 [BSD 3](LICENSE)
301
302 ## Documentation
303 The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable
304
305 ## Background
306 Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
307 has been under active development since then.
308
309 ## Getting Help
310
311 For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).
312 Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
313
314 ## Discussion and Development
315 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.
316
317 ## Contributing to pandas [![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas)
318
319 All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
320
321 A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
322
323 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.
324
325 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).
326
327 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!
328
329 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).
330
331 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)