forked from pandas-dev/pandas
-
Notifications
You must be signed in to change notification settings - Fork 0
/
setup.py
executable file
·730 lines (624 loc) · 27.5 KB
/
setup.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
#!/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
import re
import platform
from distutils.version import LooseVersion
def is_platform_windows():
return sys.platform == 'win32' or sys.platform == 'cygwin'
def is_platform_linux():
return sys.platform == 'linux2'
def is_platform_mac():
return sys.platform == 'darwin'
# versioning
import versioneer
cmdclass = versioneer.get_cmdclass()
min_cython_ver = '0.23'
try:
import Cython
ver = Cython.__version__
_CYTHON_INSTALLED = ver >= LooseVersion(min_cython_ver)
except ImportError:
_CYTHON_INSTALLED = False
try:
import pkg_resources
from setuptools import setup, Command
_have_setuptools = True
except ImportError:
# no setuptools installed
from distutils.core import setup, Command
_have_setuptools = False
setuptools_kwargs = {}
min_numpy_ver = '1.9.0'
if sys.version_info[0] >= 3:
setuptools_kwargs = {
'zip_safe': False,
'install_requires': ['python-dateutil >= 2',
'pytz >= 2011k',
'numpy >= %s' % min_numpy_ver],
'setup_requires': ['numpy >= %s' % min_numpy_ver],
}
if not _have_setuptools:
sys.exit("need setuptools/distribute for Py3k"
"\n$ pip install distribute")
else:
setuptools_kwargs = {
'install_requires': ['python-dateutil',
'pytz >= 2011k',
'numpy >= %s' % min_numpy_ver],
'setup_requires': ['numpy >= %s' % min_numpy_ver],
'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'")
from distutils.extension import Extension
from distutils.command.build import build
from distutils.command.build_ext import build_ext as _build_ext
try:
if not _CYTHON_INSTALLED:
raise ImportError('No supported version of Cython installed.')
try:
from Cython.Distutils.old_build_ext import old_build_ext as _build_ext
except ImportError:
# Pre 0.25
from Cython.Distutils import build_ext as _build_ext
cython = True
except ImportError:
cython = False
if cython:
try:
try:
from Cython import Tempita as tempita
except ImportError:
import tempita
except ImportError:
raise ImportError('Building pandas requires Tempita: '
'pip install Tempita')
from os.path import join as pjoin
_pxi_dep_template = {
'algos': ['_libs/algos_common_helper.pxi.in',
'_libs/algos_take_helper.pxi.in', '_libs/algos_rank_helper.pxi.in'],
'groupby': ['_libs/groupby_helper.pxi.in'],
'join': ['_libs/join_helper.pxi.in', '_libs/join_func_helper.pxi.in'],
'reshape': ['_libs/reshape_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):
def build_extensions(self):
# if builing from c files, don't need to
# generate template output
if cython:
for pxifile in _pxifiles:
# build pxifiles first, template extention 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)
numpy_incl = pkg_resources.resource_filename('numpy', 'core/include')
for ext in self.extensions:
if hasattr(ext, 'include_dirs') and not numpy_incl in ext.include_dirs:
ext.include_dirs.append(numpy_incl)
_build_ext.build_extensions(self)
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 1.8.1
"""
DISTNAME = 'pandas'
LICENSE = 'BSD'
AUTHOR = "The PyData Development Team"
EMAIL = "[email protected]"
URL = "http://pandas.pydata.org"
DOWNLOAD_URL = ''
CLASSIFIERS = [
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Operating System :: OS Independent',
'Intended Audience :: Science/Research',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'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')
dt = pjoin(base,'datetime')
src = base
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(src,'period_helper.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_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', '.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 Exception:
pass
for clean_tree in self._clean_trees:
try:
shutil.rmtree(clean_tree)
except Exception:
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/period.pyx',
'pandas/_libs/index.pyx',
'pandas/_libs/algos.pyx',
'pandas/_libs/join.pyx',
'pandas/_libs/interval.pyx',
'pandas/_libs/hashing.pyx',
'pandas/_libs/testing.pyx',
'pandas/_libs/window.pyx',
'pandas/_libs/sparse.pyx',
'pandas/_libs/parsers.pyx',
'pandas/io/sas/sas.pyx']
def initialize_options(self):
sdist_class.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_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("{}: -> [{}]".format(ext.name, ext.sources))
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)
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})
try:
from wheel.bdist_wheel import bdist_wheel
class BdistWheel(bdist_wheel):
def get_tag(self):
tag = bdist_wheel.get_tag(self)
repl = 'macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64'
if tag[2] == 'macosx_10_6_intel':
tag = (tag[0], tag[1], repl)
return tag
cmdclass['bdist_wheel'] = BdistWheel
except ImportError:
pass
if cython:
suffix = '.pyx'
cmdclass['build_ext'] = CheckingBuildExt
cmdclass['cython'] = CythonCommand
else:
suffix = '.c'
cmdclass['build_src'] = DummyBuildSrc
cmdclass['build_ext'] = CheckingBuildExt
lib_depends = ['reduce', 'inference', 'properties']
def srcpath(name=None, suffix='.pyx', subdir='src'):
return pjoin('pandas', subdir, name + suffix)
if suffix == '.pyx':
lib_depends = [srcpath(f, suffix='.pyx', subdir='_libs/src') for f in lib_depends]
lib_depends.append('pandas/_libs/src/util.pxd')
else:
lib_depends = []
plib_depends = []
common_include = ['pandas/_libs/src/klib', 'pandas/_libs/src']
def pxd(name):
return os.path.abspath(pjoin('pandas', name + '.pxd'))
# args to ignore warnings
if is_platform_windows():
extra_compile_args=[]
else:
extra_compile_args=['-Wno-unused-function']
lib_depends = lib_depends + ['pandas/_libs/src/numpy_helper.h',
'pandas/_libs/src/parse_helper.h',
'pandas/_libs/src/compat_helper.h']
tseries_depends = ['pandas/_libs/src/datetime/np_datetime.h',
'pandas/_libs/src/datetime/np_datetime_strings.h',
'pandas/_libs/src/period_helper.h',
'pandas/_libs/src/datetime.pxd']
# some linux distros require it
libraries = ['m'] if not is_platform_windows() else []
ext_data = {
'_libs.lib': {'pyxfile': '_libs/lib',
'depends': lib_depends + tseries_depends},
'_libs.hashtable': {'pyxfile': '_libs/hashtable',
'pxdfiles': ['_libs/hashtable'],
'depends': (['pandas/_libs/src/klib/khash_python.h']
+ _pxi_dep['hashtable'])},
'_libs.tslib': {'pyxfile': '_libs/tslib',
'pxdfiles': ['_libs/src/util', '_libs/lib'],
'depends': tseries_depends,
'sources': ['pandas/_libs/src/datetime/np_datetime.c',
'pandas/_libs/src/datetime/np_datetime_strings.c',
'pandas/_libs/src/period_helper.c']},
'_libs.period': {'pyxfile': '_libs/period',
'depends': tseries_depends,
'sources': ['pandas/_libs/src/datetime/np_datetime.c',
'pandas/_libs/src/datetime/np_datetime_strings.c',
'pandas/_libs/src/period_helper.c']},
'_libs.index': {'pyxfile': '_libs/index',
'sources': ['pandas/_libs/src/datetime/np_datetime.c',
'pandas/_libs/src/datetime/np_datetime_strings.c'],
'pxdfiles': ['_libs/src/util', '_libs/hashtable'],
'depends': _pxi_dep['index']},
'_libs.algos': {'pyxfile': '_libs/algos',
'pxdfiles': ['_libs/src/util', '_libs/algos', '_libs/hashtable'],
'depends': _pxi_dep['algos']},
'_libs.groupby': {'pyxfile': '_libs/groupby',
'pxdfiles': ['_libs/src/util', '_libs/algos'],
'depends': _pxi_dep['groupby']},
'_libs.join': {'pyxfile': '_libs/join',
'pxdfiles': ['_libs/src/util', '_libs/hashtable'],
'depends': _pxi_dep['join']},
'_libs.reshape': {'pyxfile': '_libs/reshape',
'depends': _pxi_dep['reshape']},
'_libs.interval': {'pyxfile': '_libs/interval',
'pxdfiles': ['_libs/hashtable'],
'depends': _pxi_dep['interval']},
'_libs.window': {'pyxfile': '_libs/window',
'pxdfiles': ['_libs/src/skiplist', '_libs/src/util'],
'depends': ['pandas/_libs/src/skiplist.pyx',
'pandas/_libs/src/skiplist.h']},
'_libs.parsers': {'pyxfile': '_libs/parsers',
'depends': ['pandas/_libs/src/parser/tokenizer.h',
'pandas/_libs/src/parser/io.h',
'pandas/_libs/src/numpy_helper.h'],
'sources': ['pandas/_libs/src/parser/tokenizer.c',
'pandas/_libs/src/parser/io.c']},
'_libs.sparse': {'pyxfile': '_libs/sparse',
'depends': (['pandas/_libs/sparse.pyx'] +
_pxi_dep['sparse'])},
'_libs.testing': {'pyxfile': '_libs/testing',
'depends': ['pandas/_libs/testing.pyx']},
'_libs.hashing': {'pyxfile': '_libs/hashing',
'depends': ['pandas/_libs/hashing.pyx']},
'io.sas._sas': {'pyxfile': 'io/sas/sas'},
}
extensions = []
for name, data in ext_data.items():
sources = [srcpath(data['pyxfile'], suffix=suffix, subdir='')]
pxds = [pxd(x) for x in data.get('pxdfiles', [])]
if suffix == '.pyx' and pxds:
sources.extend(pxds)
sources.extend(data.get('sources', []))
include = data.get('include', common_include)
obj = Extension('pandas.%s' % name,
sources=sources,
depends=data.get('depends', []),
include_dirs=include,
extra_compile_args=extra_compile_args)
extensions.append(obj)
#----------------------------------------------------------------------
# msgpack
if sys.byteorder == 'big':
macros = [('__BIG_ENDIAN__', '1')]
else:
macros = [('__LITTLE_ENDIAN__', '1')]
packer_ext = Extension('pandas.io.msgpack._packer',
depends=['pandas/_libs/src/msgpack/pack.h',
'pandas/_libs/src/msgpack/pack_template.h'],
sources = [srcpath('_packer',
suffix=suffix if suffix == '.pyx' else '.cpp',
subdir='io/msgpack')],
language='c++',
include_dirs=['pandas/_libs/src/msgpack'] + common_include,
define_macros=macros,
extra_compile_args=extra_compile_args)
unpacker_ext = Extension('pandas.io.msgpack._unpacker',
depends=['pandas/_libs/src/msgpack/unpack.h',
'pandas/_libs/src/msgpack/unpack_define.h',
'pandas/_libs/src/msgpack/unpack_template.h'],
sources = [srcpath('_unpacker',
suffix=suffix if suffix == '.pyx' else '.cpp',
subdir='io/msgpack')],
language='c++',
include_dirs=['pandas/_libs/src/msgpack'] + common_include,
define_macros=macros,
extra_compile_args=extra_compile_args)
extensions.append(packer_ext)
extensions.append(unpacker_ext)
#----------------------------------------------------------------------
# ujson
if suffix == '.pyx' and 'setuptools' in sys.modules:
# 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/numpy_helper.h'],
sources=['pandas/_libs/src/ujson/python/ujson.c',
'pandas/_libs/src/ujson/python/objToJSON.c',
'pandas/_libs/src/ujson/python/JSONtoObj.c',
'pandas/_libs/src/ujson/lib/ultrajsonenc.c',
'pandas/_libs/src/ujson/lib/ultrajsondec.c',
'pandas/_libs/src/datetime/np_datetime.c',
'pandas/_libs/src/datetime/np_datetime_strings.c'],
include_dirs=['pandas/_libs/src/ujson/python',
'pandas/_libs/src/ujson/lib',
'pandas/_libs/src/datetime'] + common_include,
extra_compile_args=['-D_GNU_SOURCE'] + extra_compile_args)
extensions.append(ujson_ext)
#----------------------------------------------------------------------
# util
# extension for pseudo-safely moving bytes into mutable buffers
_move_ext = Extension('pandas.util._move',
depends=[],
sources=['pandas/util/move.c'])
extensions.append(_move_ext)
if _have_setuptools:
setuptools_kwargs["test_suite"] = "nose.collector"
# The build cache system does string matching below this point.
# if you change something, be careful.
setup(name=DISTNAME,
maintainer=AUTHOR,
version=versioneer.get_version(),
packages=['pandas',
'pandas.api',
'pandas.api.types',
'pandas.compat',
'pandas.compat.numpy',
'pandas.core',
'pandas.core.dtypes',
'pandas.core.indexes',
'pandas.core.computation',
'pandas.core.reshape',
'pandas.core.sparse',
'pandas.core.tools',
'pandas.core.util',
'pandas.computation',
'pandas.errors',
'pandas.formats',
'pandas.io',
'pandas.io.json',
'pandas.io.sas',
'pandas.io.msgpack',
'pandas.io.formats',
'pandas.io.clipboard',
'pandas._libs',
'pandas.plotting',
'pandas.stats',
'pandas.types',
'pandas.util',
'pandas.tests',
'pandas.tests.api',
'pandas.tests.dtypes',
'pandas.tests.computation',
'pandas.tests.sparse',
'pandas.tests.frame',
'pandas.tests.indexing',
'pandas.tests.indexes',
'pandas.tests.indexes.datetimes',
'pandas.tests.indexes.timedeltas',
'pandas.tests.indexes.period',
'pandas.tests.internals',
'pandas.tests.io',
'pandas.tests.io.json',
'pandas.tests.io.parser',
'pandas.tests.io.sas',
'pandas.tests.io.msgpack',
'pandas.tests.io.formats',
'pandas.tests.groupby',
'pandas.tests.reshape',
'pandas.tests.series',
'pandas.tests.scalar',
'pandas.tests.tseries',
'pandas.tests.plotting',
'pandas.tests.tools',
'pandas.tests.util',
'pandas.tools',
'pandas.tseries',
],
package_data={'pandas.tests': ['data/*.csv'],
'pandas.tests.indexes': ['data/*.pickle'],
'pandas.tests.io': ['data/legacy_hdf/*.h5',
'data/legacy_pickle/*/*.pickle',
'data/legacy_msgpack/*/*.msgpack',
'data/*.csv*',
'data/*.dta',
'data/*.pickle',
'data/*.txt',
'data/*.xls',
'data/*.xlsx',
'data/*.xlsm',
'data/*.table',
'parser/data/*.csv',
'parser/data/*.gz',
'parser/data/*.bz2',
'parser/data/*.txt',
'parser/data/*.tar',
'parser/data/*.tar.gz',
'sas/data/*.csv',
'sas/data/*.xpt',
'sas/data/*.sas7bdat',
'data/*.html',
'data/html_encoding/*.html',
'json/data/*.json'],
'pandas.tests.io.formats': ['data/*.csv'],
'pandas.tests.io.msgpack': ['data/*.mp'],
'pandas.tests.reshape': ['data/*.csv'],
'pandas.tests.tseries': ['data/*.pickle'],
'pandas.io.formats': ['templates/*.tpl']
},
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)