%global _empty_manifest_terminate_build 0 Name: python-dc-stat-think Version: 1.1.1 Release: 1 Summary: Utility functions used in the DataCamp Statistical Thinking courses. License: MIT license URL: https://github.com/justinbois/dc_stat_think Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2f/9c/b824da2a757f12fd92df3d0c6cac361e57135c41ba0cd69b4fce1a44dc8e/dc_stat_think-1.1.1.tar.gz BuildArch: noarch %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. %package -n python3-dc-stat-think Summary: Utility functions used in the DataCamp Statistical Thinking courses. Provides: python-dc-stat-think BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dc-stat-think # 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. %package help Summary: Development documents and examples for dc-stat-think Provides: python3-dc-stat-think-doc %description help # 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. %prep %autosetup -n dc_stat_think-1.1.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-dc-stat-think -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 1.1.1-1 - Package Spec generated