#!/usr/bin/env python3 """ Parts of this file were taken from the pyzmq project (https://github.com/zeromq/pyzmq) which have been permitted for use under the BSD license. Parts are from lxml (https://github.com/lxml/lxml) """ import argparse from distutils.sysconfig import get_config_vars from distutils.version import LooseVersion import multiprocessing import os from os.path import join as pjoin import platform import shutil import sys import pkg_resources from setuptools import Command, find_packages, setup # versioning import versioneer cmdclass = versioneer.get_cmdclass() def is_platform_windows(): return sys.platform == "win32" or sys.platform == "cygwin" def is_platform_mac(): return sys.platform == "darwin" min_numpy_ver = "1.15.4" min_cython_ver = "0.29.21" # note: sync with pyproject.toml try: import Cython _CYTHON_VERSION = Cython.__version__ from Cython.Build import cythonize _CYTHON_INSTALLED = _CYTHON_VERSION >= LooseVersion(min_cython_ver) except ImportError: _CYTHON_VERSION = None _CYTHON_INSTALLED = False cythonize = lambda x, *args, **kwargs: x # dummy func # The import of Extension must be after the import of Cython, otherwise # we do not get the appropriately patched class. # See https://cython.readthedocs.io/en/latest/src/userguide/source_files_and_compilation.html # noqa from distutils.extension import Extension # noqa: E402 isort:skip from distutils.command.build import build # noqa: E402 isort:skip if _CYTHON_INSTALLED: from Cython.Distutils.old_build_ext import old_build_ext as _build_ext cython = True from Cython import Tempita as tempita else: from distutils.command.build_ext import build_ext as _build_ext cython = False _pxi_dep_template = { "algos": ["_libs/algos_common_helper.pxi.in", "_libs/algos_take_helper.pxi.in"], "hashtable": [ "_libs/hashtable_class_helper.pxi.in", "_libs/hashtable_func_helper.pxi.in", ], "index": ["_libs/index_class_helper.pxi.in"], "sparse": ["_libs/sparse_op_helper.pxi.in"], "interval": ["_libs/intervaltree.pxi.in"], } _pxifiles = [] _pxi_dep = {} for module, files in _pxi_dep_template.items(): pxi_files = [pjoin("pandas", x) for x in files] _pxifiles.extend(pxi_files) _pxi_dep[module] = pxi_files class build_ext(_build_ext): @classmethod def render_templates(cls, pxifiles): for pxifile in pxifiles: # build pxifiles first, template extension must be .pxi.in assert pxifile.endswith(".pxi.in") outfile = pxifile[:-3] if ( os.path.exists(outfile) and os.stat(pxifile).st_mtime < os.stat(outfile).st_mtime ): # if .pxi.in is not updated, no need to output .pxi continue with open(pxifile, "r") as f: tmpl = f.read() pyxcontent = tempita.sub(tmpl) with open(outfile, "w") as f: f.write(pyxcontent) def build_extensions(self): # if building from c files, don't need to # generate template output if cython: self.render_templates(_pxifiles) super().build_extensions() 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 `__ 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 = "pydata@googlegroups.com" 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 :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Cython", "Topic :: Scientific/Engineering", ] class CleanCommand(Command): """Custom distutils command to clean the .so and .pyc files.""" user_options = [("all", "a", "")] def initialize_options(self): self.all = True self._clean_me = [] self._clean_trees = [] base = pjoin("pandas", "_libs", "src") tsbase = pjoin("pandas", "_libs", "tslibs", "src") dt = pjoin(tsbase, "datetime") util = pjoin("pandas", "util") parser = pjoin(base, "parser") ujson_python = pjoin(base, "ujson", "python") ujson_lib = pjoin(base, "ujson", "lib") self._clean_exclude = [ pjoin(dt, "np_datetime.c"), pjoin(dt, "np_datetime_strings.c"), pjoin(parser, "tokenizer.c"), pjoin(parser, "io.c"), pjoin(ujson_python, "ujson.c"), pjoin(ujson_python, "objToJSON.c"), pjoin(ujson_python, "JSONtoObj.c"), pjoin(ujson_python, "date_conversions.c"), pjoin(ujson_lib, "ultrajsonenc.c"), pjoin(ujson_lib, "ultrajsondec.c"), pjoin(util, "move.c"), ] for root, dirs, files in os.walk("pandas"): for f in files: filepath = pjoin(root, f) if filepath in self._clean_exclude: continue if os.path.splitext(f)[-1] in ( ".pyc", ".so", ".o", ".pyo", ".pyd", ".c", ".cpp", ".orig", ): self._clean_me.append(filepath) for d in dirs: if d == "__pycache__": self._clean_trees.append(pjoin(root, d)) # clean the generated pxi files for pxifile in _pxifiles: pxifile = pxifile.replace(".pxi.in", ".pxi") self._clean_me.append(pxifile) for d in ("build", "dist"): if os.path.exists(d): self._clean_trees.append(d) def finalize_options(self): pass def run(self): for clean_me in self._clean_me: try: os.unlink(clean_me) except OSError: pass for clean_tree in self._clean_trees: try: shutil.rmtree(clean_tree) except OSError: pass # we need to inherit from the versioneer # class as it encodes the version info sdist_class = cmdclass["sdist"] class CheckSDist(sdist_class): """Custom sdist that ensures Cython has compiled all pyx files to c.""" _pyxfiles = [ "pandas/_libs/lib.pyx", "pandas/_libs/hashtable.pyx", "pandas/_libs/tslib.pyx", "pandas/_libs/index.pyx", "pandas/_libs/internals.pyx", "pandas/_libs/algos.pyx", "pandas/_libs/join.pyx", "pandas/_libs/indexing.pyx", "pandas/_libs/interval.pyx", "pandas/_libs/hashing.pyx", "pandas/_libs/missing.pyx", "pandas/_libs/reduction.pyx", "pandas/_libs/testing.pyx", "pandas/_libs/sparse.pyx", "pandas/_libs/ops.pyx", "pandas/_libs/parsers.pyx", "pandas/_libs/tslibs/base.pyx", "pandas/_libs/tslibs/ccalendar.pyx", "pandas/_libs/tslibs/dtypes.pyx", "pandas/_libs/tslibs/period.pyx", "pandas/_libs/tslibs/strptime.pyx", "pandas/_libs/tslibs/np_datetime.pyx", "pandas/_libs/tslibs/timedeltas.pyx", "pandas/_libs/tslibs/timestamps.pyx", "pandas/_libs/tslibs/timezones.pyx", "pandas/_libs/tslibs/conversion.pyx", "pandas/_libs/tslibs/fields.pyx", "pandas/_libs/tslibs/offsets.pyx", "pandas/_libs/tslibs/parsing.pyx", "pandas/_libs/tslibs/tzconversion.pyx", "pandas/_libs/tslibs/vectorized.pyx", "pandas/_libs/window/indexers.pyx", "pandas/_libs/writers.pyx", "pandas/io/sas/sas.pyx", ] _cpp_pyxfiles = [ "pandas/_libs/window/aggregations.pyx", ] def initialize_options(self): sdist_class.initialize_options(self) def run(self): if "cython" in cmdclass: self.run_command("cython") else: # If we are not running cython then # compile the extensions correctly pyx_files = [(self._pyxfiles, "c"), (self._cpp_pyxfiles, "cpp")] for pyxfiles, extension in pyx_files: for pyxfile in pyxfiles: sourcefile = pyxfile[:-3] + extension msg = ( f"{extension}-source file '{sourcefile}' not found.\n" "Run 'setup.py cython' before sdist." ) assert os.path.isfile(sourcefile), msg sdist_class.run(self) class CheckingBuildExt(build_ext): """ Subclass build_ext to get clearer report if Cython is necessary. """ def check_cython_extensions(self, extensions): for ext in extensions: for src in ext.sources: if not os.path.exists(src): print(f"{ext.name}: -> [{ext.sources}]") raise Exception( f"""Cython-generated file '{src}' not found. Cython is required to compile pandas from a development branch. Please install Cython or download a release package of pandas. """ ) def build_extensions(self): self.check_cython_extensions(self.extensions) build_ext.build_extensions(self) class CythonCommand(build_ext): """ Custom distutils command subclassed from Cython.Distutils.build_ext to compile pyx->c, and stop there. All this does is override the C-compile method build_extension() with a no-op. """ def build_extension(self, ext): pass class DummyBuildSrc(Command): """ numpy's build_src command interferes with Cython's build_ext. """ user_options = [] def initialize_options(self): self.py_modules_dict = {} def finalize_options(self): pass def run(self): pass cmdclass.update({"clean": CleanCommand, "build": build}) cmdclass["build_ext"] = CheckingBuildExt if cython: suffix = ".pyx" cmdclass["cython"] = CythonCommand else: suffix = ".c" cmdclass["build_src"] = DummyBuildSrc # ---------------------------------------------------------------------- # Preparation of compiler arguments debugging_symbols_requested = "--with-debugging-symbols" in sys.argv if debugging_symbols_requested: sys.argv.remove("--with-debugging-symbols") if sys.byteorder == "big": endian_macro = [("__BIG_ENDIAN__", "1")] else: endian_macro = [("__LITTLE_ENDIAN__", "1")] extra_compile_args = [] extra_link_args = [] if is_platform_windows(): if debugging_symbols_requested: extra_compile_args.append("/Z7") extra_link_args.append("/DEBUG") else: # PANDAS_CI=1 is set by ci/setup_env.sh if os.environ.get("PANDAS_CI", "0") == "1": extra_compile_args.append("-Werror") if debugging_symbols_requested: extra_compile_args.append("-g") # Build for at least macOS 10.9 when compiling on a 10.9 system or above, # overriding CPython distuitls behaviour which is to target the version that # python was built for. This may be overridden by setting # MACOSX_DEPLOYMENT_TARGET before calling setup.py if is_platform_mac(): if "MACOSX_DEPLOYMENT_TARGET" not in os.environ: current_system = platform.mac_ver()[0] python_target = get_config_vars().get( "MACOSX_DEPLOYMENT_TARGET", current_system ) if ( LooseVersion(python_target) < "10.9" and LooseVersion(current_system) >= "10.9" ): os.environ["MACOSX_DEPLOYMENT_TARGET"] = "10.9" if sys.version_info[:2] == (3, 8): # GH 33239 extra_compile_args.append("-Wno-error=deprecated-declarations") # https://github.com/pandas-dev/pandas/issues/35559 extra_compile_args.append("-Wno-error=unreachable-code") # enable coverage by building cython files by setting the environment variable # "PANDAS_CYTHON_COVERAGE" (with a Truthy value) or by running build_ext # with `--with-cython-coverage`enabled linetrace = os.environ.get("PANDAS_CYTHON_COVERAGE", False) if "--with-cython-coverage" in sys.argv: linetrace = True sys.argv.remove("--with-cython-coverage") # Note: if not using `cythonize`, coverage can be enabled by # pinning `ext.cython_directives = directives` to each ext in extensions. # github.com/cython/cython/wiki/enhancements-compilerdirectives#in-setuppy directives = {"linetrace": False, "language_level": 3} macros = [] if linetrace: # https://pypkg.com/pypi/pytest-cython/f/tests/example-project/setup.py directives["linetrace"] = True macros = [("CYTHON_TRACE", "1"), ("CYTHON_TRACE_NOGIL", "1")] # in numpy>=1.16.0, silence build warnings about deprecated API usage # we can't do anything about these warnings because they stem from # cython+numpy version mismatches. macros.append(("NPY_NO_DEPRECATED_API", "0")) if "-Werror" in extra_compile_args: try: import numpy as np except ImportError: pass else: if np.__version__ < LooseVersion("1.16.0"): extra_compile_args.remove("-Werror") # ---------------------------------------------------------------------- # Specification of Dependencies # TODO: Need to check to see if e.g. `linetrace` has changed and possibly # re-compile. def maybe_cythonize(extensions, *args, **kwargs): """ Render tempita templates before calling cythonize. This is skipped for * clean * sdist """ if "clean" in sys.argv or "sdist" in sys.argv: # See https://github.com/cython/cython/issues/1495 return extensions elif not cython: # GH#28836 raise a helfpul error message if _CYTHON_VERSION: raise RuntimeError( f"Cannot cythonize with old Cython version ({_CYTHON_VERSION} " f"installed, needs {min_cython_ver})" ) raise RuntimeError("Cannot cythonize without Cython installed.") numpy_incl = pkg_resources.resource_filename("numpy", "core/include") # TODO: Is this really necessary here? for ext in extensions: if hasattr(ext, "include_dirs") and numpy_incl not in ext.include_dirs: ext.include_dirs.append(numpy_incl) # reuse any parallel arguments provided for compilation to cythonize parser = argparse.ArgumentParser() parser.add_argument("-j", type=int) parser.add_argument("--parallel", type=int) parsed, _ = parser.parse_known_args() nthreads = 0 if parsed.parallel: nthreads = parsed.parallel elif parsed.j: nthreads = parsed.j kwargs["nthreads"] = nthreads build_ext.render_templates(_pxifiles) return cythonize(extensions, *args, **kwargs) def srcpath(name=None, suffix=".pyx", subdir="src"): return pjoin("pandas", subdir, name + suffix) lib_depends = ["pandas/_libs/src/parse_helper.h"] klib_include = ["pandas/_libs/src/klib"] tseries_depends = [ "pandas/_libs/tslibs/src/datetime/np_datetime.h", "pandas/_libs/tslibs/src/datetime/np_datetime_strings.h", ] ext_data = { "_libs.algos": { "pyxfile": "_libs/algos", "include": klib_include, "depends": _pxi_dep["algos"], }, "_libs.groupby": {"pyxfile": "_libs/groupby"}, "_libs.hashing": {"pyxfile": "_libs/hashing", "depends": []}, "_libs.hashtable": { "pyxfile": "_libs/hashtable", "include": klib_include, "depends": (["pandas/_libs/src/klib/khash_python.h"] + _pxi_dep["hashtable"]), }, "_libs.index": { "pyxfile": "_libs/index", "include": klib_include, "depends": _pxi_dep["index"], }, "_libs.indexing": {"pyxfile": "_libs/indexing"}, "_libs.internals": {"pyxfile": "_libs/internals"}, "_libs.interval": { "pyxfile": "_libs/interval", "include": klib_include, "depends": _pxi_dep["interval"], }, "_libs.join": {"pyxfile": "_libs/join", "include": klib_include}, "_libs.lib": { "pyxfile": "_libs/lib", "depends": lib_depends + tseries_depends, "include": klib_include, # due to tokenizer import "sources": ["pandas/_libs/src/parser/tokenizer.c"], }, "_libs.missing": {"pyxfile": "_libs/missing", "depends": tseries_depends}, "_libs.parsers": { "pyxfile": "_libs/parsers", "include": klib_include + ["pandas/_libs/src"], "depends": [ "pandas/_libs/src/parser/tokenizer.h", "pandas/_libs/src/parser/io.h", ], "sources": [ "pandas/_libs/src/parser/tokenizer.c", "pandas/_libs/src/parser/io.c", ], }, "_libs.reduction": {"pyxfile": "_libs/reduction"}, "_libs.ops": {"pyxfile": "_libs/ops"}, "_libs.ops_dispatch": {"pyxfile": "_libs/ops_dispatch"}, "_libs.properties": {"pyxfile": "_libs/properties"}, "_libs.reshape": {"pyxfile": "_libs/reshape", "depends": []}, "_libs.sparse": {"pyxfile": "_libs/sparse", "depends": _pxi_dep["sparse"]}, "_libs.tslib": {"pyxfile": "_libs/tslib", "depends": tseries_depends}, "_libs.tslibs.base": {"pyxfile": "_libs/tslibs/base"}, "_libs.tslibs.ccalendar": {"pyxfile": "_libs/tslibs/ccalendar"}, "_libs.tslibs.dtypes": {"pyxfile": "_libs/tslibs/dtypes"}, "_libs.tslibs.conversion": { "pyxfile": "_libs/tslibs/conversion", "depends": tseries_depends, "sources": ["pandas/_libs/tslibs/src/datetime/np_datetime.c"], }, "_libs.tslibs.fields": { "pyxfile": "_libs/tslibs/fields", "depends": tseries_depends, }, "_libs.tslibs.nattype": {"pyxfile": "_libs/tslibs/nattype"}, "_libs.tslibs.np_datetime": { "pyxfile": "_libs/tslibs/np_datetime", "depends": tseries_depends, "sources": [ "pandas/_libs/tslibs/src/datetime/np_datetime.c", "pandas/_libs/tslibs/src/datetime/np_datetime_strings.c", ], }, "_libs.tslibs.offsets": { "pyxfile": "_libs/tslibs/offsets", "depends": tseries_depends, }, "_libs.tslibs.parsing": { "pyxfile": "_libs/tslibs/parsing", "include": klib_include, "depends": ["pandas/_libs/src/parser/tokenizer.h"], "sources": ["pandas/_libs/src/parser/tokenizer.c"], }, "_libs.tslibs.period": { "pyxfile": "_libs/tslibs/period", "depends": tseries_depends, "sources": ["pandas/_libs/tslibs/src/datetime/np_datetime.c"], }, "_libs.tslibs.strptime": { "pyxfile": "_libs/tslibs/strptime", "depends": tseries_depends, }, "_libs.tslibs.timedeltas": { "pyxfile": "_libs/tslibs/timedeltas", "depends": tseries_depends, }, "_libs.tslibs.timestamps": { "pyxfile": "_libs/tslibs/timestamps", "depends": tseries_depends, }, "_libs.tslibs.timezones": {"pyxfile": "_libs/tslibs/timezones"}, "_libs.tslibs.tzconversion": { "pyxfile": "_libs/tslibs/tzconversion", "depends": tseries_depends, }, "_libs.tslibs.vectorized": {"pyxfile": "_libs/tslibs/vectorized"}, "_libs.testing": {"pyxfile": "_libs/testing"}, "_libs.window.aggregations": { "pyxfile": "_libs/window/aggregations", "language": "c++", "suffix": ".cpp", "depends": ["pandas/_libs/src/skiplist.h"], }, "_libs.window.indexers": {"pyxfile": "_libs/window/indexers"}, "_libs.writers": {"pyxfile": "_libs/writers"}, "io.sas._sas": {"pyxfile": "io/sas/sas"}, } extensions = [] for name, data in ext_data.items(): source_suffix = suffix if suffix == ".pyx" else data.get("suffix", ".c") sources = [srcpath(data["pyxfile"], suffix=source_suffix, subdir="")] sources.extend(data.get("sources", [])) include = data.get("include") obj = Extension( f"pandas.{name}", sources=sources, depends=data.get("depends", []), include_dirs=include, language=data.get("language", "c"), define_macros=data.get("macros", macros), extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, ) extensions.append(obj) # ---------------------------------------------------------------------- # ujson if suffix == ".pyx": # undo dumb setuptools bug clobbering .pyx sources back to .c for ext in extensions: if ext.sources[0].endswith((".c", ".cpp")): root, _ = os.path.splitext(ext.sources[0]) ext.sources[0] = root + suffix ujson_ext = Extension( "pandas._libs.json", depends=[ "pandas/_libs/src/ujson/lib/ultrajson.h", "pandas/_libs/src/ujson/python/date_conversions.h", ], sources=( [ "pandas/_libs/src/ujson/python/ujson.c", "pandas/_libs/src/ujson/python/objToJSON.c", "pandas/_libs/src/ujson/python/date_conversions.c", "pandas/_libs/src/ujson/python/JSONtoObj.c", "pandas/_libs/src/ujson/lib/ultrajsonenc.c", "pandas/_libs/src/ujson/lib/ultrajsondec.c", ] + [ "pandas/_libs/tslibs/src/datetime/np_datetime.c", "pandas/_libs/tslibs/src/datetime/np_datetime_strings.c", ] ), include_dirs=[ "pandas/_libs/src/ujson/python", "pandas/_libs/src/ujson/lib", "pandas/_libs/src/datetime", ], extra_compile_args=(["-D_GNU_SOURCE"] + extra_compile_args), extra_link_args=extra_link_args, define_macros=macros, ) extensions.append(ujson_ext) # ---------------------------------------------------------------------- def setup_package(): setuptools_kwargs = { "install_requires": [ "python-dateutil >= 2.7.3", "pytz >= 2017.2", f"numpy >= {min_numpy_ver}", ], "setup_requires": [f"numpy >= {min_numpy_ver}"], "zip_safe": False, } 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=">=3.6.1", extras_require={ "test": [ # sync with setup.cfg minversion & install.rst "pytest>=4.0.2", "pytest-xdist", "hypothesis>=3.58", ] }, entry_points={ "pandas_plotting_backends": ["matplotlib = pandas:plotting._matplotlib"] }, **setuptools_kwargs, ) if __name__ == "__main__": # Freeze to support parallel compilation when using spawn instead of fork multiprocessing.freeze_support() setup_package()