Utility functions used in the DataCamp Statistical Thinking courses.
# 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.
0.1.0 (2017-07-20) 0.1.1 (2017-07-20) 0.1.2 (2017-07-24) 0.1.4 (2017-07-26) 0.1.5 (2017-08-17) 1.0.0 (2017-08-28) ——————