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        "author": "Justin Bois",
        "author_email": "bois@caltech.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# DataCamp Statistical Thinking utilities\n\n[![version](https://img.shields.io/pypi/v/dc_stat_think.svg)](https://pypi.python.org/pypi/dc_stat_think) [![build status](https://img.shields.io/travis/justinbois/dc_stat_think.svg)](https://travis-ci.org/justinbois/dc_stat_think) \n\nUtility functions used in the DataCamp Statistical Thinking courses.\n- [Statistical Thinking in Python Part I](https://www.datacamp.com/courses/statistical-thinking-in-python-part-1/)\n- [Statistical Thinking in Python Part II](https://www.datacamp.com/courses/statistical-thinking-in-python-part-2/)\n- [Case Studies in Statistical Thinking](https://www.datacamp.com/courses/case-studies-in-statistical-thinking/)\n\n\n## Installation\ndc_stat_think may be installed by running the following command.\n```\npip install dc_stat_think\n```\n\n## Usage\nUpon importing the module, functions from the DataCamp Statistical Thinking courses are available. For example, you can compute a 95% confidence interval of the mean of some data using the `draw_bs_reps()` function.\n\n```python\n>>> import numpy as np\n>>> import dc_stat_think as dcst\n>>> data = np.array([1.2, 3.3, 2.7, 2.4, 5.6, \n                     3.4, 1.3, 3.9, 2.9, 2.1, 2.7])\n>>> bs_reps = dcst.draw_bs_reps(data, np.mean, size=10000)\n>>> conf_int = np.percentile(bs_reps, [2.5, 97.5])\n>>> print(conf_int)\n[ 2.21818182  3.60909091]\n```\n\n## Implementation\nThe functions include in dc_stat_think are not *exactly* like those students wrote in the DataCamp Statistical Thinking courses. Notable differences are listed below.\n\n+ The doc strings in dc_stat_think are much more complete.\n+ The dc_stat_think module has error checking of inputs.\n+ In most cases, especially those involving bootstrapping or other uses of the `np.random` module, dc_stat_think functions are more optimized for speed, in particular using [Numba](http://numba.pydata.org). Note, though, that dc_stat_think does not take advantage of any parallel computing.\n\nIf you do want to use functions *exactly* as written in the Statistical Thinking courses, you can use the `dc_stat_think.original` submodule.\n\n```python\n>>> import numpy as np\n>>> import dc_stat_think.original\n>>> data = np.array([1.2, 3.3, 2.7, 2.4, 5.6, 3.4, 1.3, 3.9, 2.9, 2.1, 2.7])\n>>> bs_reps = dc_stat_think.original.draw_bs_reps(data, np.mean, size=10000)\n>>> conf_int = np.percentile(bs_reps, [2.5, 97.5])\n>>> print(conf_int)\n[ 2.20909091  3.59090909]\n```\n\n## Credits\nThis package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template and then modified.\n\n\n=======\nHistory\n=======\n\n0.1.0 (2017-07-20)\n0.1.1 (2017-07-20)\n0.1.2 (2017-07-24)\n0.1.4 (2017-07-26)\n0.1.5 (2017-08-17)\n1.0.0 (2017-08-28)\n------------------\n\n\n",
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