automatic import of python-klib

3 files changed, 383 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..ee3ca2c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/klib-1.0.7.tar.gz
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>
+
+[![Flake8 & PyTest](https://github.com/akanz1/klib/workflows/Flake8%20%F0%9F%90%8D%20PyTest%20%20%20%C2%B4/badge.svg)](https://github.com/akanz1/klib)
+[![Language](https://img.shields.io/github/languages/top/akanz1/klib)](https://pypi.org/project/klib/)
+[![Last Commit](https://badgen.net/github/last-commit/akanz1/klib/main)](https://github.com/akanz1/klib/commits/main)
+[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=akanz1_klib&metric=alert_status)](https://sonarcloud.io/dashboard?id=akanz1_klib)
+[![Scrutinizer](https://scrutinizer-ci.com/g/akanz1/klib/badges/quality-score.png?b=main)](https://scrutinizer-ci.com/g/akanz1/klib/)
+[![codecov](https://codecov.io/gh/akanz1/klib/branch/main/graph/badge.svg)](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.
+
+[![PyPI Version](https://img.shields.io/pypi/v/klib)](https://pypi.org/project/klib/)
+[![Downloads](https://pepy.tech/badge/klib/month)](https://pypi.org/project/klib/)
+
+```bash
+pip install -U klib
+```
+
+Alternatively, to install this package with conda run:
+
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/klib)](https://anaconda.org/conda-forge/klib)
+[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/klib.svg)](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
+
+[![Open in Visual Studio Code](https://open.vscode.dev/badges/open-in-vscode.svg)](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>
+
+[![Flake8 & PyTest](https://github.com/akanz1/klib/workflows/Flake8%20%F0%9F%90%8D%20PyTest%20%20%20%C2%B4/badge.svg)](https://github.com/akanz1/klib)
+[![Language](https://img.shields.io/github/languages/top/akanz1/klib)](https://pypi.org/project/klib/)
+[![Last Commit](https://badgen.net/github/last-commit/akanz1/klib/main)](https://github.com/akanz1/klib/commits/main)
+[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=akanz1_klib&metric=alert_status)](https://sonarcloud.io/dashboard?id=akanz1_klib)
+[![Scrutinizer](https://scrutinizer-ci.com/g/akanz1/klib/badges/quality-score.png?b=main)](https://scrutinizer-ci.com/g/akanz1/klib/)
+[![codecov](https://codecov.io/gh/akanz1/klib/branch/main/graph/badge.svg)](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.
+
+[![PyPI Version](https://img.shields.io/pypi/v/klib)](https://pypi.org/project/klib/)
+[![Downloads](https://pepy.tech/badge/klib/month)](https://pypi.org/project/klib/)
+
+```bash
+pip install -U klib
+```
+
+Alternatively, to install this package with conda run:
+
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/klib)](https://anaconda.org/conda-forge/klib)
+[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/klib.svg)](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
+
+[![Open in Visual Studio Code](https://open.vscode.dev/badges/open-in-vscode.svg)](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>
+
+[![Flake8 & PyTest](https://github.com/akanz1/klib/workflows/Flake8%20%F0%9F%90%8D%20PyTest%20%20%20%C2%B4/badge.svg)](https://github.com/akanz1/klib)
+[![Language](https://img.shields.io/github/languages/top/akanz1/klib)](https://pypi.org/project/klib/)
+[![Last Commit](https://badgen.net/github/last-commit/akanz1/klib/main)](https://github.com/akanz1/klib/commits/main)
+[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=akanz1_klib&metric=alert_status)](https://sonarcloud.io/dashboard?id=akanz1_klib)
+[![Scrutinizer](https://scrutinizer-ci.com/g/akanz1/klib/badges/quality-score.png?b=main)](https://scrutinizer-ci.com/g/akanz1/klib/)
+[![codecov](https://codecov.io/gh/akanz1/klib/branch/main/graph/badge.svg)](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.
+
+[![PyPI Version](https://img.shields.io/pypi/v/klib)](https://pypi.org/project/klib/)
+[![Downloads](https://pepy.tech/badge/klib/month)](https://pypi.org/project/klib/)
+
+```bash
+pip install -U klib
+```
+
+Alternatively, to install this package with conda run:
+
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/klib)](https://anaconda.org/conda-forge/klib)
+[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/klib.svg)](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
+
+[![Open in Visual Studio Code](https://open.vscode.dev/badges/open-in-vscode.svg)](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
diff --git a/sources b/sources
new file mode 100644
index 0000000..8856b9b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+2f8a108b6b26a1708b548ccb121a6e07  klib-1.0.7.tar.gz