Utility functions used in the DataCamp Statistical Thinking courses.
- Statistical Thinking in Python Part I
- Statistical Thinking in Python Part II
- Case Studies in Statistical Thinking
dc_stat_think may be installed by running the following command.
pip install dc_stat_think
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.
>>> 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]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.randommodule, dc_stat_think functions are more optimized for speed, in particular using Numba. 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.
>>> 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]This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template and then modified.