#!/usr/bin/env python
"""
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 os
import sys
import shutil
import warnings
# may need to work around setuptools bug by providing a fake Pyrex
try:
import Cython
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "fake_pyrex"))
except ImportError:
pass
# try bootstrapping setuptools if it doesn't exist
try:
import pkg_resources
try:
pkg_resources.require("setuptools>=0.6c5")
except pkg_resources.VersionConflict:
from ez_setup import use_setuptools
use_setuptools(version="0.6c5")
from setuptools import setup, Command
_have_setuptools = True
except ImportError:
# no setuptools installed
from distutils.core import setup, Command
_have_setuptools = False
setuptools_kwargs = {}
if sys.version_info[0] >= 3:
setuptools_kwargs = {'use_2to3': True,
'zip_safe': False,
'install_requires': ['python-dateutil >= 2',
'pytz',
'numpy >= 1.4'],
'use_2to3_exclude_fixers': ['lib2to3.fixes.fix_next',
],
}
if not _have_setuptools:
sys.exit("need setuptools/distribute for Py3k"
"\n$ pip install distribute")
else:
setuptools_kwargs = {
'install_requires': ['python-dateutil < 2',
'pytz',
'numpy >= 1.6'],
'zip_safe' : False,
}
if not _have_setuptools:
try:
import numpy
import dateutil
setuptools_kwargs = {}
except ImportError:
sys.exit("install requires: 'python-dateutil < 2','numpy'."
" use pip or easy_install."
"\n $ pip install 'python-dateutil < 2' 'numpy'")
try:
import numpy as np
except ImportError:
nonumpy_msg = ("# numpy needed to finish setup. run:\n\n"
" $ pip install numpy # or easy_install numpy\n")
sys.exit(nonumpy_msg)
if np.__version__ < '1.6.1':
msg = "pandas requires NumPy >= 1.6 due to datetime64 dependency"
sys.exit(msg)
from distutils.extension import Extension
from distutils.command.build import build
from distutils.command.build_ext import build_ext
from distutils.command.sdist import sdist
from os.path import splitext, basename, join as pjoin
DESCRIPTION = ("Powerful data structures for data analysis, time series,"
"and statistics")
LONG_DESCRIPTION = """
**pandas** is a Python package providing 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 <http://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, moving window linear regressions,
date shifting and lagging, etc.
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.
Note
----
Windows binaries built against NumPy 1.6.1
"""
DISTNAME = 'pandas'
LICENSE = 'BSD'
AUTHOR = "The PyData Development Team"
EMAIL = "pydata@googlegroups.com"
URL = "http://pandas.pydata.org"
DOWNLOAD_URL = ''
CLASSIFIERS = [
'Development Status :: 4 - Beta',
'Environment :: Console',
'Operating System :: OS Independent',
'Intended Audience :: Science/Research',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3',
'Programming Language :: Cython',
'Topic :: Scientific/Engineering',
]
MAJOR = 0
MINOR = 8
MICRO = 1
ISRELEASED = False
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
QUALIFIER = ''
FULLVERSION = VERSION
if not ISRELEASED:
FULLVERSION += '.dev'
try:
import subprocess
try:
pipe = subprocess.Popen(["git", "rev-parse", "--short", "HEAD"],
stdout=subprocess.PIPE).stdout
except OSError:
# msysgit compatibility
pipe = subprocess.Popen(["git.cmd", "rev-parse", "--short", "HEAD"],
stdout=subprocess.PIPE).stdout
rev = pipe.read().strip()
# makes distutils blow up on Python 2.7
if sys.version_info[0] >= 3:
rev = rev.decode('ascii')
FULLVERSION += "-%s" % rev
except:
warnings.warn("WARNING: Couldn't get git revision")
else:
FULLVERSION += QUALIFIER
def write_version_py(filename=None):
cnt = """\
version = '%s'
short_version = '%s'
"""
if not filename:
filename = os.path.join(os.path.dirname(__file__), 'pandas', 'version.py')
a = open(filename, 'w')
try:
a.write(cnt % (FULLVERSION, VERSION))
finally:
a.close()
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 = []
self._clean_exclude = ['np_datetime.c',
'np_datetime_strings.c',
'period.c']
for root, dirs, files in list(os.walk('pandas')):
for f in files:
if f in self._clean_exclude:
continue
if os.path.splitext(f)[-1] in ('.pyc', '.so', '.o',
'.pyd', '.c'):
self._clean_me.append(pjoin(root, f))
for d in dirs:
if d == '__pycache__':
self._clean_trees.append(pjoin(root, d))
for d in ('build',):
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 Exception:
pass
for clean_tree in self._clean_trees:
try:
shutil.rmtree(clean_tree)
except Exception:
pass
class CheckSDist(sdist):
"""Custom sdist that ensures Cython has compiled all pyx files to c."""
_pyxfiles = ['pandas/src/tseries.pyx'
'pandas/src/sparse.pyx']
def initialize_options(self):
sdist.initialize_options(self)
'''
self._pyxfiles = []
for root, dirs, files in os.walk('pandas'):
for f in files:
if f.endswith('.pyx'):
self._pyxfiles.append(pjoin(root, f))
'''
def run(self):
if 'cython' in cmdclass:
self.run_command('cython')
else:
for pyxfile in self._pyxfiles:
cfile = pyxfile[:-3]+'c'
msg = "C-source file '%s' not found."%(cfile)+\
" Run 'setup.py cython' before sdist."
assert os.path.isfile(cfile), msg
sdist.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):
raise Exception("""Cython-generated file '%s' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
""" % src)
def build_extensions(self):
self.check_cython_extensions(self.extensions)
self.check_extensions_list(self.extensions)
for ext in self.extensions:
self.build_extension(ext)
cmdclass = {'clean': CleanCommand,
'build': build}
try:
from Cython.Distutils import build_ext
#from Cython.Distutils import Extension # to get pyrex debugging symbols
cython=True
except ImportError:
cython=False
suffix = '.c'
cmdclass['build_ext'] = CheckingBuildExt
else:
suffix = '.pyx'
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['build_src'] = DummyBuildSrc
cmdclass['cython'] = CythonCommand
cmdclass['build_ext'] = build_ext
cmdclass['sdist'] = CheckSDist
tseries_depends = ['reindex', 'groupby', 'skiplist', 'moments',
'reduce', 'stats', 'datetime',
'hashtable', 'inference', 'properties', 'join', 'engines']
plib_depends = ['plib']
def srcpath(name=None, suffix='.pyx', subdir='src'):
return pjoin('pandas', subdir, name+suffix)
if suffix == '.pyx':
tseries_depends = [srcpath(f, suffix='.pyx')
for f in tseries_depends]
tseries_depends.append('pandas/src/util.pxd')
plib_depends = [srcpath(f, suffix='.pyx')
for f in plib_depends]
plib_depends.append('pandas/src/util.pxd')
else:
tseries_depends = []
plib_depends = []
algos_ext = Extension('pandas._algos',
sources=[srcpath('generated', suffix=suffix)],
include_dirs=[np.get_include()],
)
lib_ext = Extension('pandas.lib',
depends=tseries_depends + ['pandas/src/numpy_helper.h'],
sources=[srcpath('tseries', suffix=suffix),
'pandas/src/datetime/np_datetime.c',
'pandas/src/datetime/np_datetime_strings.c'],
include_dirs=[np.get_include()],
# pyrex_gdb=True,
# extra_compile_args=['-Wconversion']
)
period_ext = Extension('pandas._period',
depends=plib_depends + ['pandas/src/numpy_helper.h',
'pandas/src/period.h'],
sources=[srcpath('plib', suffix=suffix),
'pandas/src/datetime/np_datetime.c',
'pandas/src/period.c'],
include_dirs=[np.get_include()])
sparse_ext = Extension('pandas._sparse',
sources=[srcpath('sparse', suffix=suffix)],
include_dirs=[np.get_include()])
sandbox_ext = Extension('pandas._sandbox',
sources=[srcpath('sandbox', suffix=suffix)],
include_dirs=[np.get_include()])
cppsandbox_ext = Extension('pandas._cppsandbox',
language='c++',
sources=[srcpath('cppsandbox', suffix=suffix)],
include_dirs=[np.get_include()])
extensions = [algos_ext, lib_ext, period_ext, sparse_ext]
if not ISRELEASED:
extensions.extend([sandbox_ext])
# if _have_setuptools:
# setuptools_kwargs["test_suite"] = "nose.collector"
write_version_py()
setup(name=DISTNAME,
version=FULLVERSION,
maintainer=AUTHOR,
packages=['pandas',
'pandas.compat',
'pandas.core',
'pandas.io',
'pandas.rpy',
'pandas.sandbox',
'pandas.sparse',
'pandas.sparse.tests',
'pandas.stats',
'pandas.util',
'pandas.tests',
'pandas.tools',
'pandas.tools.tests',
'pandas.tseries',
'pandas.tseries.tests',
'pandas.io.tests',
'pandas.stats.tests',
],
package_data={'pandas.io' : ['tests/*.h5',
'tests/*.csv',
'tests/*.xls',
'tests/*.xlsx',
'tests/*.table'],
'pandas.tests' : ['data/*.pickle',
'data/*.csv'],
'pandas.tseries.tests' : ['data/*.pickle',
'data/*.csv']
},
ext_modules=extensions,
maintainer_email=EMAIL,
description=DESCRIPTION,
license=LICENSE,
cmdclass = cmdclass,
url=URL,
download_url=DOWNLOAD_URL,
long_description=LONG_DESCRIPTION,
classifiers=CLASSIFIERS,
platforms='any',
**setuptools_kwargs)