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@@ -0,0 +1 @@ +/dc_stat_think-1.1.1.tar.gz diff --git a/python-dc-stat-think.spec b/python-dc-stat-think.spec new file mode 100644 index 0000000..1b97c47 --- /dev/null +++ b/python-dc-stat-think.spec @@ -0,0 +1,231 @@ +%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 + +[](https://pypi.python.org/pypi/dc_stat_think) [](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 + +[](https://pypi.python.org/pypi/dc_stat_think) [](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 + +[](https://pypi.python.org/pypi/dc_stat_think) [](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 +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1 +- Package Spec generated @@ -0,0 +1 @@ +e6c4dbe462e48f5e645b96b4779bede2 dc_stat_think-1.1.1.tar.gz |