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BLD: move metadata to setup.cfg (pandas-dev#38852)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,11 +1,65 @@ | ||
| [metadata] | ||
| name = pandas | ||
| description = Powerful data structures for data analysis, time series, and statistics | ||
| long_description = file: README.md | ||
| long_description_content_type = text/markdown | ||
| url = https://pandas.pydata.org | ||
| author = The Pandas Development Team | ||
| author_email = [email protected] | ||
| license = BSD-3-Clause | ||
| license_file = LICENSE | ||
| platforms = any | ||
| classifiers = | ||
| Development Status :: 5 - Production/Stable | ||
| Environment :: Console | ||
| Intended Audience :: Science/Research | ||
| License :: OSI Approved :: BSD License | ||
| Operating System :: OS Independen | ||
| Programming Language :: Cython | ||
| Programming Language :: Python | ||
| Programming Language :: Python :: 3 | ||
| Programming Language :: Python :: 3 :: Only | ||
| Programming Language :: Python :: 3.7 | ||
| Programming Language :: Python :: 3.8 | ||
| Programming Language :: Python :: 3.9 | ||
| Topic :: Scientific/Engineering | ||
| project_urls = | ||
| Bug Tracker = https://github.com/pandas-dev/pandas/issues | ||
| Documentation = https://pandas.pydata.org/pandas-docs/stable | ||
| Source Code = https://github.com/pandas-dev/pandas | ||
|
|
||
| [options] | ||
| packages = find: | ||
| install_requires = | ||
| numpy>=1.16.5 | ||
| python-dateutil>=2.7.3 | ||
| pytz>=2017.3 | ||
| python_requires = >=3.7.1 | ||
| include_package_data = True | ||
| zip_safe = False | ||
|
|
||
| [options.entry_points] | ||
| pandas_plotting_backends = | ||
| matplotlib = pandas:plotting._matplotlib | ||
|
|
||
| [options.extras_require] | ||
| test = | ||
| hypothesis>=3.58 | ||
| pytest>=5.0.1 | ||
| pytest-xdist | ||
|
|
||
| [options.package_data] | ||
| * = templates/*, _libs/**/*.dll | ||
|
|
||
| [build_ext] | ||
| inplace = 1 | ||
| inplace = True | ||
|
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||
| [options.packages.find] | ||
| include = pandas, pandas.* | ||
|
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||
| # See the docstring in versioneer.py for instructions. Note that you must | ||
| # re-run 'versioneer.py setup' after changing this section, and commit the | ||
| # resulting files. | ||
|
|
||
| [versioneer] | ||
| VCS = git | ||
| style = pep440 | ||
|
|
@@ -38,16 +92,16 @@ bootstrap = | |
| import pandas as pd | ||
| np # avoiding error when importing again numpy or pandas | ||
| pd # (in some cases we want to do it to show users) | ||
| ignore = E203, # space before : (needed for how black formats slicing) | ||
| E402, # module level import not at top of file | ||
| W503, # line break before binary operator | ||
| # Classes/functions in different blocks can generate those errors | ||
| E302, # expected 2 blank lines, found 0 | ||
| E305, # expected 2 blank lines after class or function definition, found 0 | ||
| # We use semicolon at the end to avoid displaying plot objects | ||
| E703, # statement ends with a semicolon | ||
| E711, # comparison to none should be 'if cond is none:' | ||
|
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||
| ignore = | ||
| E203, # space before : (needed for how black formats slicing) | ||
| E402, # module level import not at top of file | ||
| W503, # line break before binary operator | ||
| # Classes/functions in different blocks can generate those errors | ||
| E302, # expected 2 blank lines, found 0 | ||
| E305, # expected 2 blank lines after class or function definition, found 0 | ||
| # We use semicolon at the end to avoid displaying plot objects | ||
| E703, # statement ends with a semicolon | ||
| E711, # comparison to none should be 'if cond is none:' | ||
| exclude = | ||
| doc/source/development/contributing_docstring.rst, | ||
| # work around issue of undefined variable warnings | ||
|
|
@@ -64,18 +118,18 @@ xfail_strict = True | |
| filterwarnings = | ||
| error:Sparse:FutureWarning | ||
| error:The SparseArray:FutureWarning | ||
| junit_family=xunit2 | ||
| junit_family = xunit2 | ||
|
|
||
| [codespell] | ||
| ignore-words-list=ba,blocs,coo,hist,nd,ser | ||
| ignore-regex=https://(\w+\.)+ | ||
| ignore-words-list = ba,blocs,coo,hist,nd,ser | ||
| ignore-regex = https://(\w+\.)+ | ||
|
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||
| [coverage:run] | ||
| branch = False | ||
| omit = | ||
| */tests/* | ||
| pandas/_typing.py | ||
| pandas/_version.py | ||
| */tests/* | ||
| pandas/_typing.py | ||
| pandas/_version.py | ||
| plugins = Cython.Coverage | ||
|
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| [coverage:report] | ||
|
|
@@ -130,10 +184,10 @@ warn_unused_ignores = True | |
| show_error_codes = True | ||
|
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| [mypy-pandas.tests.*] | ||
| check_untyped_defs=False | ||
| check_untyped_defs = False | ||
|
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||
| [mypy-pandas._version] | ||
| check_untyped_defs=False | ||
| check_untyped_defs = False | ||
|
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||
| [mypy-pandas.io.clipboard] | ||
| check_untyped_defs=False | ||
| check_untyped_defs = False | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -18,7 +18,7 @@ | |
| import sys | ||
|
|
||
| import numpy | ||
| from setuptools import Command, Extension, find_packages, setup | ||
| from setuptools import Command, Extension, setup | ||
| from setuptools.command.build_ext import build_ext as _build_ext | ||
|
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||
| import versioneer | ||
|
|
@@ -34,7 +34,6 @@ def is_platform_mac(): | |
| return sys.platform == "darwin" | ||
|
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| min_numpy_ver = "1.16.5" | ||
| min_cython_ver = "0.29.21" # note: sync with pyproject.toml | ||
|
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| try: | ||
|
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@@ -99,96 +98,6 @@ def build_extensions(self): | |
| super().build_extensions() | ||
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| DESCRIPTION = "Powerful data structures for data analysis, time series, and statistics" | ||
| LONG_DESCRIPTION = """ | ||
| **pandas** is a Python package that provides fast, flexible, and expressive data | ||
| structures designed to make working with structured (tabular, multidimensional, | ||
| potentially heterogeneous) and time series 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 | ||
| toward this goal. | ||
| pandas is well suited for many different kinds of data: | ||
| - Tabular data with heterogeneously-typed columns, as in an SQL table or | ||
| Excel spreadsheet | ||
| - Ordered and unordered (not necessarily fixed-frequency) time series data. | ||
| - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and | ||
| column labels | ||
| - Any other form of observational / statistical data sets. The data actually | ||
| need not be labeled at all to be placed into a pandas data structure | ||
| The two primary data structures of pandas, Series (1-dimensional) and DataFrame | ||
| (2-dimensional), handle the vast majority of typical use cases in finance, | ||
| statistics, social science, and many areas of engineering. For R users, | ||
| DataFrame provides everything that R's ``data.frame`` provides and much | ||
| more. pandas is built on top of `NumPy <https://www.numpy.org>`__ and is | ||
| intended to integrate well within a scientific computing environment with many | ||
| other 3rd party libraries. | ||
| Here are just a few of the things that pandas does well: | ||
| - Easy handling of **missing data** (represented as NaN) in floating point as | ||
| well as non-floating point data | ||
| - Size mutability: columns can be **inserted and deleted** from DataFrame and | ||
| higher dimensional objects | ||
| - Automatic and explicit **data 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** functionality to perform | ||
| split-apply-combine operations on data sets, for both aggregating and | ||
| transforming data | ||
| - Make it **easy to convert** ragged, differently-indexed data in other | ||
| Python and NumPy data structures into DataFrame objects | ||
| - Intelligent label-based **slicing**, **fancy indexing**, and **subsetting** | ||
| of large data sets | ||
| - Intuitive **merging** and **joining** data sets | ||
| - Flexible **reshaping** and pivoting of data sets | ||
| - **Hierarchical** labeling of axes (possible to have multiple labels per | ||
| tick) | ||
| - Robust IO tools for loading data from **flat files** (CSV and delimited), | ||
| Excel files, databases, and saving / loading data from the ultrafast **HDF5 | ||
| format** | ||
| - **Time series**-specific functionality: date range generation and frequency | ||
| conversion, moving window statistics, date shifting and lagging. | ||
| Many of these principles are here to address the shortcomings frequently | ||
| experienced using other languages / scientific research environments. For data | ||
| scientists, working with data is typically divided into multiple stages: | ||
| munging and cleaning data, analyzing / modeling it, then organizing the results | ||
| of the analysis into a form suitable for plotting or tabular display. pandas is | ||
| the ideal tool for all of these tasks. | ||
| """ | ||
|
|
||
| DISTNAME = "pandas" | ||
| LICENSE = "BSD" | ||
| AUTHOR = "The PyData Development Team" | ||
| EMAIL = "[email protected]" | ||
| URL = "https://pandas.pydata.org" | ||
| DOWNLOAD_URL = "" | ||
| PROJECT_URLS = { | ||
| "Bug Tracker": "https://github.com/pandas-dev/pandas/issues", | ||
| "Documentation": "https://pandas.pydata.org/pandas-docs/stable/", | ||
| "Source Code": "https://github.com/pandas-dev/pandas", | ||
| } | ||
| CLASSIFIERS = [ | ||
| "Development Status :: 5 - Production/Stable", | ||
| "Environment :: Console", | ||
| "Operating System :: OS Independent", | ||
| "Intended Audience :: Science/Research", | ||
| "Programming Language :: Python", | ||
| "Programming Language :: Python :: 3", | ||
| "Programming Language :: Python :: 3.7", | ||
| "Programming Language :: Python :: 3.8", | ||
| "Programming Language :: Python :: 3.9", | ||
| "Programming Language :: Cython", | ||
| "Topic :: Scientific/Engineering", | ||
| ] | ||
|
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|
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| class CleanCommand(Command): | ||
| """Custom distutils command to clean the .so and .pyc files.""" | ||
|
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@@ -711,51 +620,11 @@ def srcpath(name=None, suffix=".pyx", subdir="src"): | |
| # ---------------------------------------------------------------------- | ||
|
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|
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| def setup_package(): | ||
| setuptools_kwargs = { | ||
| "install_requires": [ | ||
| "python-dateutil >= 2.7.3", | ||
| "pytz >= 2017.3", | ||
| f"numpy >= {min_numpy_ver}", | ||
| ], | ||
| "setup_requires": [f"numpy >= {min_numpy_ver}"], | ||
| "zip_safe": False, | ||
| } | ||
|
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| if __name__ == "__main__": | ||
| # Freeze to support parallel compilation when using spawn instead of fork | ||
| multiprocessing.freeze_support() | ||
| setup( | ||
| name=DISTNAME, | ||
| maintainer=AUTHOR, | ||
| version=versioneer.get_version(), | ||
| packages=find_packages(include=["pandas", "pandas.*"]), | ||
| package_data={"": ["templates/*", "_libs/**/*.dll"]}, | ||
| ext_modules=maybe_cythonize(extensions, compiler_directives=directives), | ||
| maintainer_email=EMAIL, | ||
| description=DESCRIPTION, | ||
| license=LICENSE, | ||
| cmdclass=cmdclass, | ||
| url=URL, | ||
| download_url=DOWNLOAD_URL, | ||
| project_urls=PROJECT_URLS, | ||
| long_description=LONG_DESCRIPTION, | ||
| classifiers=CLASSIFIERS, | ||
| platforms="any", | ||
| python_requires=">=3.7.1", | ||
| extras_require={ | ||
| "test": [ | ||
| # sync with setup.cfg minversion & install.rst | ||
| "pytest>=5.0.1", | ||
| "pytest-xdist", | ||
| "hypothesis>=3.58", | ||
| ] | ||
| }, | ||
| entry_points={ | ||
| "pandas_plotting_backends": ["matplotlib = pandas:plotting._matplotlib"] | ||
| }, | ||
| **setuptools_kwargs, | ||
| ) | ||
|
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|
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| if __name__ == "__main__": | ||
| # Freeze to support parallel compilation when using spawn instead of fork | ||
| multiprocessing.freeze_support() | ||
| setup_package() | ||