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    "info": {
        "author": "Keith Goodman",
        "author_email": "bottle-neck@googlegroups.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: C",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering"
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        "description": "Bottleneck is a collection of fast NumPy array functions written in C.\n\nLet's give it a try. Create a NumPy array::\n\n    >>> import numpy as np\n    >>> a = np.array([1, 2, np.nan, 4, 5])\n\nFind the nanmean::\n\n    >>> import bottleneck as bn\n    >>> bn.nanmean(a)\n    3.0\n\nMoving window mean::\n\n    >>> bn.move_mean(a, window=2, min_count=1)\n    array([ 1. ,  1.5,  2. ,  4. ,  4.5])\n\nBenchmark\n=========\n\nBottleneck comes with a benchmark suite::\n\n    >>> bn.bench()\n    Bottleneck performance benchmark\n        Bottleneck 1.3.0.dev0; Numpy 1.12.1\n        Speed is NumPy time divided by Bottleneck time\n        NaN means approx one-fifth NaNs; float64 used\n\n                  no NaN     no NaN      NaN       no NaN      NaN\n                   (100,)  (1000,1000)(1000,1000)(1000,1000)(1000,1000)\n                   axis=0     axis=0     axis=0     axis=1     axis=1\n    nansum         67.3        0.3        0.7        2.5        2.4\n    nanmean       194.8        1.9        2.1        3.4        3.1\n    nanstd        241.5        1.6        2.1        2.7        2.6\n    nanvar        229.7        1.7        2.1        2.7        2.5\n    nanmin         34.1        0.7        1.1        0.8        2.6\n    nanmax         45.6        0.7        2.7        1.0        3.7\n    median        111.0        1.3        5.6        1.0        4.8\n    nanmedian     108.8        5.9        6.7        5.6        6.7\n    ss             16.3        1.1        1.2        1.6        1.6\n    nanargmin      89.2        2.9        5.1        2.2        5.6\n    nanargmax     107.4        3.0        5.4        2.2        5.8\n    anynan         19.4        0.3       35.0        0.5       29.9\n    allnan         39.9      146.6      128.3      115.8       75.6\n    rankdata       55.0        2.6        2.3        2.9        2.8\n    nanrankdata    59.8        2.8        2.2        3.2        2.5\n    partition       4.4        1.2        1.6        1.0        1.4\n    argpartition    3.5        1.1        1.4        1.1        1.6\n    replace        17.7        1.4        1.4        1.3        1.4\n    push         3440.0        7.8        9.5       20.0       15.5\n    move_sum     4743.0       75.7      156.1      195.4      211.1\n    move_mean    8760.9      116.2      167.4      252.1      258.8\n    move_std     8979.9       96.1      196.3      144.0      256.3\n    move_var    11216.8      127.3      243.9      225.9      321.4\n    move_min     2245.3       20.6       36.7       23.2       42.1\n    move_max     2223.7       20.5       37.2       24.1       42.4\n    move_argmin  3664.0       48.2       73.3       40.2       83.9\n    move_argmax  3916.9       42.0       75.4       41.5       81.2\n    move_median  2023.3      166.8      173.7      153.8      154.3\n    move_rank    1208.5        1.9        1.9        2.5        2.8\n\nYou can also run a detailed benchmark for a single function using, for\nexample, the command::\n\n    >>> bn.bench_detailed(\"move_median\", fraction_nan=0.3)\n\nOnly arrays with data type (dtype) int32, int64, float32, and float64 are\naccelerated. All other dtypes result in calls to slower, unaccelerated\nfunctions. In the rare case of a byte-swapped input array (e.g. a big-endian\narray on a little-endian operating system) the function will not be\naccelerated regardless of dtype.\n\nWhere\n=====\n\n===================   ========================================================\n download             https://pypi.python.org/pypi/Bottleneck\n docs                 http://berkeleyanalytics.com/bottleneck\n code                 https://github.com/kwgoodman/bottleneck\n mailing list         https://groups.google.com/group/bottle-neck\n===================   ========================================================\n\nLicense\n=======\n\nBottleneck is distributed under a Simplified BSD license. See the LICENSE file\nfor details.\n\nInstall\n=======\n\nRequirements:\n\n======================== ====================================================\nBottleneck               Python 2.7, 3.5, 3.6; NumPy 1.12.1\nCompile                  gcc, clang, MinGW or MSVC\nUnit tests               nose\n======================== ====================================================\n\nTo install Bottleneck on GNU/Linux, Mac OS X, et al.::\n\n    $ sudo python setup.py install\n\nTo install bottleneck on Windows, first install MinGW and add it to your\nsystem path. Then install Bottleneck with the commands::\n\n    python setup.py install --compiler=mingw32\n\nAlternatively, you can use the Windows binaries created by Christoph Gohlke:\nhttp://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck\n\nUnit tests\n==========\n\nAfter you have installed Bottleneck, run the suite of unit tests::\n\n    >>> import bottleneck as bn\n    >>> bn.test()\n    <snip>\n    Ran 169 tests in 57.205s\n    OK\n    <nose.result.TextTestResult run=169 errors=0 failures=0>",
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