automatic import of python-dc-stat-think

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+/dc_stat_think-1.1.1.tar.gz
diff --git a/python-dc-stat-think.spec b/python-dc-stat-think.spec
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+%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
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..c7212cd
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+e6c4dbe462e48f5e645b96b4779bede2  dc_stat_think-1.1.1.tar.gz