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diff --git a/python-klib.spec b/python-klib.spec new file mode 100644 index 0000000..15e480f --- /dev/null +++ b/python-klib.spec @@ -0,0 +1,381 @@ +%global _empty_manifest_terminate_build 0 +Name: python-klib +Version: 1.0.7 +Release: 1 +Summary: Customized data preprocessing functions for frequent tasks. +License: MIT +URL: https://pypi.org/project/klib/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/fb/a5/fb9d8c6629bb46881e81c7bb2db4b3af526a414bf887abe33bd5c3170c5d/klib-1.0.7.tar.gz +BuildArch: noarch + +Requires: python3-Jinja2 +Requires: python3-matplotlib +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scipy +Requires: python3-seaborn + +%description +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/header.png" alt="klib Header" width="859" height="304"></p> + +[](https://github.com/akanz1/klib) +[](https://pypi.org/project/klib/) +[](https://github.com/akanz1/klib/commits/main) +[](https://sonarcloud.io/dashboard?id=akanz1_klib) +[](https://scrutinizer-ci.com/g/akanz1/klib/) +[](https://codecov.io/gh/akanz1/klib) + +**klib** is a Python library for importing, cleaning, analyzing and preprocessing data. Explanations on key functionalities can be found on [Medium / TowardsDataScience](https://medium.com/@akanz) and in the [examples](examples) section. Additionally, there are great introductions and overviews of the functionality on [PythonBytes](https://pythonbytes.fm/episodes/show/240/this-is-github-your-pilot-speaking) or on [YouTube (Data Professor)](https://www.youtube.com/watch?v=URjJVEeZxxU). + +## Installation + +Use the package manager [pip](https://pip.pypa.io/en/stable/) to install klib. + +[](https://pypi.org/project/klib/) +[](https://pypi.org/project/klib/) + +```bash +pip install -U klib +``` + +Alternatively, to install this package with conda run: + +[](https://anaconda.org/conda-forge/klib) +[](https://anaconda.org/conda-forge/klib) + +```bash +conda install -c conda-forge klib +``` + +## Usage + +```python +import klib +import pandas as pd + +df = pd.DataFrame(data) + +# klib.describe - functions for visualizing datasets +- klib.cat_plot(df) # returns a visualization of the number and frequency of categorical features +- klib.corr_mat(df) # returns a color-encoded correlation matrix +- klib.corr_plot(df) # returns a color-encoded heatmap, ideal for correlations +- klib.dist_plot(df) # returns a distribution plot for every numeric feature +- klib.missingval_plot(df) # returns a figure containing information about missing values + +# klib.clean - functions for cleaning datasets +- klib.data_cleaning(df) # performs datacleaning (drop duplicates & empty rows/cols, adjust dtypes,...) +- klib.clean_column_names(df) # cleans and standardizes column names, also called inside data_cleaning() +- klib.convert_datatypes(df) # converts existing to more efficient dtypes, also called inside data_cleaning() +- klib.drop_missing(df) # drops missing values, also called in data_cleaning() +- klib.mv_col_handling(df) # drops features with high ratio of missing vals based on informational content +- klib.pool_duplicate_subsets(df) # pools subset of cols based on duplicates with min. loss of information +``` + +## Examples + +Find all available examples as well as applications of the functions in **klib.clean()** with detailed descriptions <a href="https://github.com/akanz1/klib/tree/main/examples">here</a>. + +```python +klib.missingval_plot(df) # default representation of missing values in a DataFrame, plenty of settings are available +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_mv_plot.png" alt="Missingvalue Plot Example" width="1000" height="1091"></p> + +```python +klib.corr_plot(df, split='pos') # displaying only positive correlations, other settings include threshold, cmap... +klib.corr_plot(df, split='neg') # displaying only negative correlations +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_corr_plot.png" alt="Corr Plot Example" width="720" height="338"></p> + +```python +klib.corr_plot(df, target='wine') # default representation of correlations with the feature column +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_target_corr_plot.png" alt="Target Corr Plot Example" width="720" height="600"></p> + +```python +klib.dist_plot(df) # default representation of a distribution plot, other settings include fill_range, histogram, ... +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_dist_plot.png" alt="Dist Plot Example" width="910" height="130"></p> + +```python +klib.cat_plot(data, top=4, bottom=4) # representation of the 4 most & least common values in each categorical column +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_cat_plot.png" alt="Cat Plot Example" width="1000" height="1000"></p> + +Further examples, as well as applications of the functions in **klib.clean()** can be found <a href="https://github.com/akanz1/klib/tree/main/examples#data-cleaning-and-aggretation">here</a>. + +## Contributing + +[](https://open.vscode.dev/akanz1/klib) + +Pull requests and ideas, especially for further functions are welcome. For major changes or feedback, please open an issue first to discuss what you would like to change. + +## License + +[MIT](https://choosealicense.com/licenses/mit/) + + +%package -n python3-klib +Summary: Customized data preprocessing functions for frequent tasks. +Provides: python-klib +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-klib +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/header.png" alt="klib Header" width="859" height="304"></p> + +[](https://github.com/akanz1/klib) +[](https://pypi.org/project/klib/) +[](https://github.com/akanz1/klib/commits/main) +[](https://sonarcloud.io/dashboard?id=akanz1_klib) +[](https://scrutinizer-ci.com/g/akanz1/klib/) +[](https://codecov.io/gh/akanz1/klib) + +**klib** is a Python library for importing, cleaning, analyzing and preprocessing data. Explanations on key functionalities can be found on [Medium / TowardsDataScience](https://medium.com/@akanz) and in the [examples](examples) section. Additionally, there are great introductions and overviews of the functionality on [PythonBytes](https://pythonbytes.fm/episodes/show/240/this-is-github-your-pilot-speaking) or on [YouTube (Data Professor)](https://www.youtube.com/watch?v=URjJVEeZxxU). + +## Installation + +Use the package manager [pip](https://pip.pypa.io/en/stable/) to install klib. + +[](https://pypi.org/project/klib/) +[](https://pypi.org/project/klib/) + +```bash +pip install -U klib +``` + +Alternatively, to install this package with conda run: + +[](https://anaconda.org/conda-forge/klib) +[](https://anaconda.org/conda-forge/klib) + +```bash +conda install -c conda-forge klib +``` + +## Usage + +```python +import klib +import pandas as pd + +df = pd.DataFrame(data) + +# klib.describe - functions for visualizing datasets +- klib.cat_plot(df) # returns a visualization of the number and frequency of categorical features +- klib.corr_mat(df) # returns a color-encoded correlation matrix +- klib.corr_plot(df) # returns a color-encoded heatmap, ideal for correlations +- klib.dist_plot(df) # returns a distribution plot for every numeric feature +- klib.missingval_plot(df) # returns a figure containing information about missing values + +# klib.clean - functions for cleaning datasets +- klib.data_cleaning(df) # performs datacleaning (drop duplicates & empty rows/cols, adjust dtypes,...) +- klib.clean_column_names(df) # cleans and standardizes column names, also called inside data_cleaning() +- klib.convert_datatypes(df) # converts existing to more efficient dtypes, also called inside data_cleaning() +- klib.drop_missing(df) # drops missing values, also called in data_cleaning() +- klib.mv_col_handling(df) # drops features with high ratio of missing vals based on informational content +- klib.pool_duplicate_subsets(df) # pools subset of cols based on duplicates with min. loss of information +``` + +## Examples + +Find all available examples as well as applications of the functions in **klib.clean()** with detailed descriptions <a href="https://github.com/akanz1/klib/tree/main/examples">here</a>. + +```python +klib.missingval_plot(df) # default representation of missing values in a DataFrame, plenty of settings are available +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_mv_plot.png" alt="Missingvalue Plot Example" width="1000" height="1091"></p> + +```python +klib.corr_plot(df, split='pos') # displaying only positive correlations, other settings include threshold, cmap... +klib.corr_plot(df, split='neg') # displaying only negative correlations +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_corr_plot.png" alt="Corr Plot Example" width="720" height="338"></p> + +```python +klib.corr_plot(df, target='wine') # default representation of correlations with the feature column +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_target_corr_plot.png" alt="Target Corr Plot Example" width="720" height="600"></p> + +```python +klib.dist_plot(df) # default representation of a distribution plot, other settings include fill_range, histogram, ... +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_dist_plot.png" alt="Dist Plot Example" width="910" height="130"></p> + +```python +klib.cat_plot(data, top=4, bottom=4) # representation of the 4 most & least common values in each categorical column +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_cat_plot.png" alt="Cat Plot Example" width="1000" height="1000"></p> + +Further examples, as well as applications of the functions in **klib.clean()** can be found <a href="https://github.com/akanz1/klib/tree/main/examples#data-cleaning-and-aggretation">here</a>. + +## Contributing + +[](https://open.vscode.dev/akanz1/klib) + +Pull requests and ideas, especially for further functions are welcome. For major changes or feedback, please open an issue first to discuss what you would like to change. + +## License + +[MIT](https://choosealicense.com/licenses/mit/) + + +%package help +Summary: Development documents and examples for klib +Provides: python3-klib-doc +%description help +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/header.png" alt="klib Header" width="859" height="304"></p> + +[](https://github.com/akanz1/klib) +[](https://pypi.org/project/klib/) +[](https://github.com/akanz1/klib/commits/main) +[](https://sonarcloud.io/dashboard?id=akanz1_klib) +[](https://scrutinizer-ci.com/g/akanz1/klib/) +[](https://codecov.io/gh/akanz1/klib) + +**klib** is a Python library for importing, cleaning, analyzing and preprocessing data. Explanations on key functionalities can be found on [Medium / TowardsDataScience](https://medium.com/@akanz) and in the [examples](examples) section. Additionally, there are great introductions and overviews of the functionality on [PythonBytes](https://pythonbytes.fm/episodes/show/240/this-is-github-your-pilot-speaking) or on [YouTube (Data Professor)](https://www.youtube.com/watch?v=URjJVEeZxxU). + +## Installation + +Use the package manager [pip](https://pip.pypa.io/en/stable/) to install klib. + +[](https://pypi.org/project/klib/) +[](https://pypi.org/project/klib/) + +```bash +pip install -U klib +``` + +Alternatively, to install this package with conda run: + +[](https://anaconda.org/conda-forge/klib) +[](https://anaconda.org/conda-forge/klib) + +```bash +conda install -c conda-forge klib +``` + +## Usage + +```python +import klib +import pandas as pd + +df = pd.DataFrame(data) + +# klib.describe - functions for visualizing datasets +- klib.cat_plot(df) # returns a visualization of the number and frequency of categorical features +- klib.corr_mat(df) # returns a color-encoded correlation matrix +- klib.corr_plot(df) # returns a color-encoded heatmap, ideal for correlations +- klib.dist_plot(df) # returns a distribution plot for every numeric feature +- klib.missingval_plot(df) # returns a figure containing information about missing values + +# klib.clean - functions for cleaning datasets +- klib.data_cleaning(df) # performs datacleaning (drop duplicates & empty rows/cols, adjust dtypes,...) +- klib.clean_column_names(df) # cleans and standardizes column names, also called inside data_cleaning() +- klib.convert_datatypes(df) # converts existing to more efficient dtypes, also called inside data_cleaning() +- klib.drop_missing(df) # drops missing values, also called in data_cleaning() +- klib.mv_col_handling(df) # drops features with high ratio of missing vals based on informational content +- klib.pool_duplicate_subsets(df) # pools subset of cols based on duplicates with min. loss of information +``` + +## Examples + +Find all available examples as well as applications of the functions in **klib.clean()** with detailed descriptions <a href="https://github.com/akanz1/klib/tree/main/examples">here</a>. + +```python +klib.missingval_plot(df) # default representation of missing values in a DataFrame, plenty of settings are available +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_mv_plot.png" alt="Missingvalue Plot Example" width="1000" height="1091"></p> + +```python +klib.corr_plot(df, split='pos') # displaying only positive correlations, other settings include threshold, cmap... +klib.corr_plot(df, split='neg') # displaying only negative correlations +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_corr_plot.png" alt="Corr Plot Example" width="720" height="338"></p> + +```python +klib.corr_plot(df, target='wine') # default representation of correlations with the feature column +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_target_corr_plot.png" alt="Target Corr Plot Example" width="720" height="600"></p> + +```python +klib.dist_plot(df) # default representation of a distribution plot, other settings include fill_range, histogram, ... +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_dist_plot.png" alt="Dist Plot Example" width="910" height="130"></p> + +```python +klib.cat_plot(data, top=4, bottom=4) # representation of the 4 most & least common values in each categorical column +``` + +<p align="center"><img src="https://raw.githubusercontent.com/akanz1/klib/main/examples/images/example_cat_plot.png" alt="Cat Plot Example" width="1000" height="1000"></p> + +Further examples, as well as applications of the functions in **klib.clean()** can be found <a href="https://github.com/akanz1/klib/tree/main/examples#data-cleaning-and-aggretation">here</a>. + +## Contributing + +[](https://open.vscode.dev/akanz1/klib) + +Pull requests and ideas, especially for further functions are welcome. For major changes or feedback, please open an issue first to discuss what you would like to change. + +## License + +[MIT](https://choosealicense.com/licenses/mit/) + + +%prep +%autosetup -n klib-1.0.7 + +%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-klib -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.7-1 +- Package Spec generated |