%global _empty_manifest_terminate_build 0 Name: python-pandas Version: 2.0.0 Release: 1 Summary: Powerful data structures for data analysis, time series, and statistics License: BSD 3-Clause License Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team All rights reserved. Copyright (c) 2011-2023, Open source contributors. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. URL: https://pypi.org/project/pandas/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/9f/12/0b6bdd627b99cb10816956c1047b0733ef33b61a84e3420faf4d3202df06/pandas-2.0.0.tar.gz Requires: python3-dateutil Requires: python3-pytz Requires: python3-tzdata Requires: python3-numpy Requires: python3-numpy Requires: python3-numpy Requires: python3-beautifulsoup4 Requires: python3-bottleneck Requires: python3-brotlipy Requires: python3-fastparquet Requires: python3-fsspec Requires: python3-gcsfs Requires: python3-html5lib Requires: python3-hypothesis Requires: python3-jinja2 Requires: python3-lxml Requires: python3-matplotlib Requires: python3-numba Requires: python3-numexpr Requires: python3-odfpy Requires: python3-openpyxl Requires: python3-pandas-gbq Requires: python3-psycopg2 Requires: python3-pyarrow Requires: python3-pymysql Requires: python3-PyQt5 Requires: python3-pyreadstat Requires: python3-pytest Requires: python3-pytest-xdist Requires: python3-pytest-asyncio Requires: python3-snappy Requires: python3-pyxlsb Requires: python3-qtpy Requires: python3-scipy Requires: python3-s3fs Requires: python3-SQLAlchemy Requires: python3-tables Requires: python3-tabulate Requires: python3-xarray Requires: python3-xlrd Requires: python3-xlsxwriter Requires: python3-zstandard Requires: python3-s3fs Requires: python3-PyQt5 Requires: python3-qtpy Requires: python3-brotlipy Requires: python3-snappy Requires: python3-zstandard Requires: python3-scipy Requires: python3-xarray Requires: python3-odfpy Requires: python3-openpyxl Requires: python3-pyxlsb Requires: python3-xlrd Requires: python3-xlsxwriter Requires: python3-pyarrow Requires: python3-fsspec Requires: python3-gcsfs Requires: python3-pandas-gbq Requires: python3-tables Requires: python3-beautifulsoup4 Requires: python3-html5lib Requires: python3-lxml Requires: python3-SQLAlchemy Requires: python3-pymysql Requires: python3-jinja2 Requires: python3-tabulate Requires: python3-pyarrow Requires: python3-bottleneck Requires: python3-numba Requires: python3-numexpr Requires: python3-matplotlib Requires: python3-SQLAlchemy Requires: python3-psycopg2 Requires: python3-pyreadstat Requires: python3-SQLAlchemy Requires: python3-hypothesis Requires: python3-pytest Requires: python3-pytest-xdist Requires: python3-pytest-asyncio Requires: python3-lxml %description # pandas: powerful Python data analysis toolkit [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/) [![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas) [![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) [![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) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) ## What is it? **pandas** is 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 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 towards this goal. ## Main Features Here are just a few of the things that pandas does well: - Easy handling of [**missing data**][missing-data] (represented as `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data - Size mutability: columns can be [**inserted and deleted**][insertion-deletion] from DataFrame and higher dimensional objects - Automatic and explicit [**data alignment**][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**][groupby] functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data - Make it [**easy to convert**][conversion] ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects - Intelligent label-based [**slicing**][slicing], [**fancy indexing**][fancy-indexing], and [**subsetting**][subsetting] of large data sets - Intuitive [**merging**][merging] and [**joining**][joining] data sets - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of data sets - [**Hierarchical**][mi] labeling of axes (possible to have multiple labels per tick) - Robust IO tools for loading data from [**flat files**][flat-files] (CSV and delimited), [**Excel files**][excel], [**databases**][db], and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] - [**Time series**][timeseries]-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality ## Where to get it The source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas Binary installers for the latest released version are available at the [Python Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/). ```sh # conda conda install pandas ``` ```sh # or PyPI pip install pandas ``` ## Dependencies - [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org) - [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html) - [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz) 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. ## Installation from sources To install pandas from source you need [Cython](https://cython.org/) in addition to the normal dependencies above. Cython can be installed from PyPI: ```sh pip install cython ``` In the `pandas` directory (same one where you found this file after cloning the git repo), execute: ```sh python setup.py install ``` or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable): ```sh python -m pip install -e . --no-build-isolation --no-use-pep517 ``` or alternatively ```sh python setup.py develop ``` See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source). ## License [BSD 3](LICENSE) ## Documentation The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable ## Background Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and has been under active development since then. ## Getting Help For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas). Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). ## Discussion and Development 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. ## Contributing to pandas [![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas) All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**. 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. 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). 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 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). 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) %package -n python3-pandas Summary: Powerful data structures for data analysis, time series, and statistics Provides: python-pandas BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-pandas # pandas: powerful Python data analysis toolkit [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/) [![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas) [![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) [![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) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) ## What is it? **pandas** is 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 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 towards this goal. ## Main Features Here are just a few of the things that pandas does well: - Easy handling of [**missing data**][missing-data] (represented as `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data - Size mutability: columns can be [**inserted and deleted**][insertion-deletion] from DataFrame and higher dimensional objects - Automatic and explicit [**data alignment**][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**][groupby] functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data - Make it [**easy to convert**][conversion] ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects - Intelligent label-based [**slicing**][slicing], [**fancy indexing**][fancy-indexing], and [**subsetting**][subsetting] of large data sets - Intuitive [**merging**][merging] and [**joining**][joining] data sets - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of data sets - [**Hierarchical**][mi] labeling of axes (possible to have multiple labels per tick) - Robust IO tools for loading data from [**flat files**][flat-files] (CSV and delimited), [**Excel files**][excel], [**databases**][db], and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] - [**Time series**][timeseries]-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality ## Where to get it The source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas Binary installers for the latest released version are available at the [Python Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/). ```sh # conda conda install pandas ``` ```sh # or PyPI pip install pandas ``` ## Dependencies - [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org) - [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html) - [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz) 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. ## Installation from sources To install pandas from source you need [Cython](https://cython.org/) in addition to the normal dependencies above. Cython can be installed from PyPI: ```sh pip install cython ``` In the `pandas` directory (same one where you found this file after cloning the git repo), execute: ```sh python setup.py install ``` or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable): ```sh python -m pip install -e . --no-build-isolation --no-use-pep517 ``` or alternatively ```sh python setup.py develop ``` See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source). ## License [BSD 3](LICENSE) ## Documentation The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable ## Background Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and has been under active development since then. ## Getting Help For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas). Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). ## Discussion and Development 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. ## Contributing to pandas [![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas) All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**. 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. 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). 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 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). 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) %package help Summary: Development documents and examples for pandas Provides: python3-pandas-doc %description help # pandas: powerful Python data analysis toolkit [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/) [![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas) [![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) [![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) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) ## What is it? **pandas** is 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 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 towards this goal. ## Main Features Here are just a few of the things that pandas does well: - Easy handling of [**missing data**][missing-data] (represented as `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data - Size mutability: columns can be [**inserted and deleted**][insertion-deletion] from DataFrame and higher dimensional objects - Automatic and explicit [**data alignment**][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**][groupby] functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data - Make it [**easy to convert**][conversion] ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects - Intelligent label-based [**slicing**][slicing], [**fancy indexing**][fancy-indexing], and [**subsetting**][subsetting] of large data sets - Intuitive [**merging**][merging] and [**joining**][joining] data sets - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of data sets - [**Hierarchical**][mi] labeling of axes (possible to have multiple labels per tick) - Robust IO tools for loading data from [**flat files**][flat-files] (CSV and delimited), [**Excel files**][excel], [**databases**][db], and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] - [**Time series**][timeseries]-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality ## Where to get it The source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas Binary installers for the latest released version are available at the [Python Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/). ```sh # conda conda install pandas ``` ```sh # or PyPI pip install pandas ``` ## Dependencies - [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org) - [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html) - [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz) 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. ## Installation from sources To install pandas from source you need [Cython](https://cython.org/) in addition to the normal dependencies above. Cython can be installed from PyPI: ```sh pip install cython ``` In the `pandas` directory (same one where you found this file after cloning the git repo), execute: ```sh python setup.py install ``` or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable): ```sh python -m pip install -e . --no-build-isolation --no-use-pep517 ``` or alternatively ```sh python setup.py develop ``` See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source). ## License [BSD 3](LICENSE) ## Documentation The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable ## Background Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and has been under active development since then. ## Getting Help For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas). Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). ## Discussion and Development 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. ## Contributing to pandas [![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas) All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**. 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. 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). 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 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). 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) %prep %autosetup -n pandas-2.0.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pandas -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 2.0.0-1 - Package Spec generated