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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
[](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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1
- Package Spec generated
|
