Metadata-Version: 2.1
Name: dc-stat-think
Version: 1.1.1
Summary: Utility functions used in the DataCamp Statistical Thinking courses.
Home-page: https://github.com/justinbois/dc_stat_think
Author: Justin Bois
Author-email: bois@caltech.edu
License: MIT license
Description: # DataCamp Statistical Thinking utilities
        
        [![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) 
        
        Utility functions used in the DataCamp Statistical Thinking courses.
        - [Statistical Thinking in Python Part I](https://www.datacamp.com/courses/statistical-thinking-in-python-part-1/)
        - [Statistical Thinking in Python Part II](https://www.datacamp.com/courses/statistical-thinking-in-python-part-2/)
        - [Case Studies in Statistical Thinking](https://www.datacamp.com/courses/case-studies-in-statistical-thinking/)
        
        
        ## Installation
        dc_stat_think may be installed by running the following command.
        ```
        pip install dc_stat_think
        ```
        
        ## Usage
        Upon 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.
        
        ```python
        >>> import numpy as np
        >>> import dc_stat_think as dcst
        >>> 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])
        >>> bs_reps = dcst.draw_bs_reps(data, np.mean, size=10000)
        >>> conf_int = np.percentile(bs_reps, [2.5, 97.5])
        >>> print(conf_int)
        [ 2.21818182  3.60909091]
        ```
        
        ## Implementation
        The functions include in dc_stat_think are not *exactly* like those students wrote in the DataCamp Statistical Thinking courses. Notable differences are listed below.
        
        + The doc strings in dc_stat_think are much more complete.
        + The dc_stat_think module has error checking of inputs.
        + 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.
        
        If you do want to use functions *exactly* as written in the Statistical Thinking courses, you can use the `dc_stat_think.original` submodule.
        
        ```python
        >>> import numpy as np
        >>> import dc_stat_think.original
        >>> 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])
        >>> bs_reps = dc_stat_think.original.draw_bs_reps(data, np.mean, size=10000)
        >>> conf_int = np.percentile(bs_reps, [2.5, 97.5])
        >>> print(conf_int)
        [ 2.20909091  3.59090909]
        ```
        
        ## Credits
        This 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.
        
Keywords: dc_stat_think
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
