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|
%global _empty_manifest_terminate_build 0
Name: python-pyoptimus
Version: 23.5.0b0
Release: 1
Summary: Optimus is the missing framework for cleaning and pre-processing data in a distributed fashion.
License: APACHE
URL: https://github.com/hi-primus/optimus/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4e/74/09cc31d0598263899d35084eafd5804de42f9bfbb516647f754433ed1006/pyoptimus-23.5.0b0.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-nltk
Requires: python3-requests
Requires: python3-fastnumbers
Requires: python3-setuptools
Requires: python3-Jinja2
Requires: python3-humanize
Requires: python3-deepdiff
Requires: python3-glom
Requires: python3-statsmodels
Requires: python3-fast-histogram
Requires: python3-rply
Requires: python3-tabulate
Requires: python3-matplotlib
Requires: python3-seaborn
Requires: python3-jellyfish
Requires: python3-Metaphone
Requires: python3-num2words
Requires: python3-hi-dateinfer
Requires: python3-hi-urlparser
Requires: python3-yellowbrick
Requires: python3-openpyxl
Requires: python3-xlrd
Requires: python3-fastavro
Requires: python3-pandavro
Requires: python3-pybigquery
Requires: python3-snappy
Requires: python3-s3fs
Requires: python3-aiobotocore[boto3]
Requires: python3-tensorflow
Requires: python3-keras
Requires: python3-nltk
Requires: python3-pyspark
Requires: python3-findspark
Requires: python3-koalas
Requires: python3-pandas
Requires: python3-dask[complete]
Requires: python3-distributed
Requires: python3-dask-ml
Requires: python3-pyarrow
Requires: python3-coiled
Requires: python3-psutil
Requires: python3-vaex
Requires: python3-gputil
Requires: python3-numpy
Requires: python3-tensorflow
Requires: python3-keras
Requires: python3-nltk
Requires: python3-sqlalchemy
Requires: python3-flask
Requires: python3-flask
Requires: python3-gputil
Requires: python3-numpy
Requires: python3-distributed
Requires: python3-dask[complete]
Requires: python3-dask[complete]
Requires: python3-distributed
Requires: python3-dask-ml
Requires: python3-pyarrow
Requires: python3-coiled
Requires: python3-psutil
Requires: python3-sqlalchemy
Requires: python3-sphinx
Requires: python3-pytest
Requires: python3-mock
Requires: python3-nose
Requires: python3-pep8
Requires: python3-pyflakes
Requires: python3-pandas
Requires: python3-pyspark
Requires: python3-findspark
Requires: python3-koalas
Requires: python3-pytest
Requires: python3-mock
Requires: python3-nose
Requires: python3-vaex
%description
# Optimus
[](https://hi-optimus.com)
[](https://github.com/hi-primus/optimus/actions/workflows/main.yml)
[](https://hub.docker.com/r/hiprimus/optimus)
[](https://pypi.org/project/pyoptimus/)
[](https://github.com/hi-primus/optimus/releases)
[](http://calver.org)
[](https://pepy.tech/project/pyoptimus)
[](https://pepy.tech/project/pyoptimus/month)
[](https://pepy.tech/project/pyoptimus/week)
[](https://github.com/bulutyazilim/awesome-datascience)
[](https://communityinviter.com/apps/hi-bumblebee/welcome)
# Overview
Optimus is an opinionated python library to easily load, process, plot and create ML models that run over pandas, Dask, cuDF, dask-cuDF, Vaex or Spark.
Some amazing things Optimus can do for you:
* Process using a simple API, making it easy to use for newcomers.
* More than 100 functions to handle strings, process dates, urls and emails.
* Easily plot data from any size.
* Out of box functions to explore and fix data quality.
* Use the same code to process your data in your laptop or in a remote cluster of GPUs.
[See Documentation](https://docs.hi-optimus.com/en/latest/)
## Try Optimus
To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges:
[](https://mybinder.org/v2/gh/hi-primus/optimus/develop-23.5?filepath=https%3A%2F%2Fraw.githubusercontent.com%2Fhi-primus%2Foptimus%2Fdevelop-23.5%2Fexamples%2F10_min_to_optimus.ipynb)
[](https://colab.research.google.com/github/hi-primus/optimus/blob/master/examples/10_min_to_optimus_colab.ipynb)
## Installation (pip):
In your terminal just type:
```
pip install pyoptimus
```
By default Optimus install Pandas as the default engine, to install other engines you can use the following commands:
| Engine | Command |
|-----------|----------------------------------------|
| Dask | ```pip install pyoptimus[dask]``` |
| cuDF | ```pip install pyoptimus[cudf]``` |
| Dask-cuDF | ```pip install pyoptimus[dask-cudf]``` |
| Vaex | ```pip install pyoptimus[vaex]``` |
| Spark | ```pip install pyoptimus[spark]``` |
To install from the repo:
```
pip install git+https://github.com/hi-primus/optimus.git@develop-23.5
```
To install other engines:
```
pip install git+https://github.com/hi-primus/optimus.git@develop-23.5#egg=pyoptimus[dask]
```
### Requirements
* Python 3.7 or 3.8
## Examples
You can go to [10 minutes to Optimus](https://github.com/hi-primus/optimus/blob/develop-23.5/examples/10_min_to_optimus.ipynb) where you can find the basics to start working in a notebook.
Also you can go to the [Examples](https://github.com/hi-primus/optimus/tree/develop-23.5/examples/examples.md) section and find specific notebooks about data cleaning, data munging, profiling, data enrichment and how to create ML and DL models.
Here's a handy [Cheat Sheet](https://htmlpreview.github.io/?https://github.com/hi-primus/optimus/blob/develop-23.5/docs/cheatsheet/optimus_cheat_sheet.html) with the most common Optimus' operations.
## Start Optimus
Start Optimus using ```"pandas"```, ```"dask"```, ```"cudf"```,```"dask_cudf"```,```"vaex"``` or ```"spark"```.
```python
from optimus import Optimus
op = Optimus("pandas")
```
## Loading data
Now Optimus can load data in csv, json, parquet, avro and excel formats from a local file or from a URL.
```python
#csv
df = op.load.csv("../examples/data/foo.csv")
#json
df = op.load.json("../examples/data/foo.json")
# using a url
df = op.load.json("https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/examples/data/foo.json")
# parquet
df = op.load.parquet("../examples/data/foo.parquet")
# ...or anything else
df = op.load.file("../examples/data/titanic3.xls")
```
Also, you can load data from Oracle, Redshift, MySQL and Postgres databases.
## Saving Data
```python
#csv
df.save.csv("data/foo.csv")
# json
df.save.json("data/foo.json")
# parquet
df.save.parquet("data/foo.parquet")
```
You can also save data to oracle, redshift, mysql and postgres.
## Create dataframes
Also, you can create a dataframe from scratch
```python
df = op.create.dataframe({
'A': ['a', 'b', 'c', 'd'],
'B': [1, 3, 5, 7],
'C': [2, 4, 6, None],
'D': ['1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10']
})
```
Using `display` you have a beautiful way to show your data with extra information like column number, column data type and marked white spaces.
```python
display(df)
```
## Cleaning and Processing
Optimus was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that comes from Pandas.
Optimus expands the standard DataFrame functionality adding `.rows` and `.cols` accessors.
For example you can load data from a url, transform and apply some predefined cleaning functions:
```python
new_df = df\
.rows.sort("rank", "desc")\
.cols.lower(["names", "function"])\
.cols.date_format("date arrival", "yyyy/MM/dd", "dd-MM-YYYY")\
.cols.years_between("date arrival", "dd-MM-YYYY", output_cols="from arrival")\
.cols.normalize_chars("names")\
.cols.remove_special_chars("names")\
.rows.drop(df["rank"]>8)\
.cols.rename("*", str.lower)\
.cols.trim("*")\
.cols.unnest("japanese name", output_cols="other names")\
.cols.unnest("last position seen", separator=",", output_cols="pos")\
.cols.drop(["last position seen", "japanese name", "date arrival", "cybertronian", "nulltype"])
```
# Need help? 🛠️
## Feedback
Feedback is what drive Optimus future, so please take a couple of minutes to help shape the Optimus' Roadmap: http://bit.ly/optimus_survey
Also if you want to a suggestion or feature request use https://github.com/hi-primus/optimus/issues
## Troubleshooting
If you have issues, see our [Troubleshooting Guide](https://github.com/hi-primus/optimus/tree/develop-23.5/troubleshooting.md)
# Contributing to Optimus 💡
Contributions go far beyond pull requests and commits. We are very happy to receive any kind of contributions
including:
* [Documentation](https://docs.hi-optimus.com/en/latest/) updates, enhancements, designs, or bugfixes.
* Spelling or grammar fixes.
* README.md corrections or redesigns.
* Adding unit, or functional [tests](https://github.com/hi-primus/optimus/tree/develop-23.5/tests)
* Triaging GitHub issues -- especially determining whether an issue still persists or is reproducible.
* [Blogging, speaking about, or creating tutorials](https://hioptimus.com/category/blog/) about Optimus and its many features.
* Helping others on our official chats
# Backers and Sponsors
Become a [backer](https://opencollective.com/optimus#backer) or a [sponsor](https://opencollective.com/optimus#sponsor) and get your image on our README on Github with a link to your site.
[](#backers) [](#sponsors)
%package -n python3-pyoptimus
Summary: Optimus is the missing framework for cleaning and pre-processing data in a distributed fashion.
Provides: python-pyoptimus
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pyoptimus
# Optimus
[](https://hi-optimus.com)
[](https://github.com/hi-primus/optimus/actions/workflows/main.yml)
[](https://hub.docker.com/r/hiprimus/optimus)
[](https://pypi.org/project/pyoptimus/)
[](https://github.com/hi-primus/optimus/releases)
[](http://calver.org)
[](https://pepy.tech/project/pyoptimus)
[](https://pepy.tech/project/pyoptimus/month)
[](https://pepy.tech/project/pyoptimus/week)
[](https://github.com/bulutyazilim/awesome-datascience)
[](https://communityinviter.com/apps/hi-bumblebee/welcome)
# Overview
Optimus is an opinionated python library to easily load, process, plot and create ML models that run over pandas, Dask, cuDF, dask-cuDF, Vaex or Spark.
Some amazing things Optimus can do for you:
* Process using a simple API, making it easy to use for newcomers.
* More than 100 functions to handle strings, process dates, urls and emails.
* Easily plot data from any size.
* Out of box functions to explore and fix data quality.
* Use the same code to process your data in your laptop or in a remote cluster of GPUs.
[See Documentation](https://docs.hi-optimus.com/en/latest/)
## Try Optimus
To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges:
[](https://mybinder.org/v2/gh/hi-primus/optimus/develop-23.5?filepath=https%3A%2F%2Fraw.githubusercontent.com%2Fhi-primus%2Foptimus%2Fdevelop-23.5%2Fexamples%2F10_min_to_optimus.ipynb)
[](https://colab.research.google.com/github/hi-primus/optimus/blob/master/examples/10_min_to_optimus_colab.ipynb)
## Installation (pip):
In your terminal just type:
```
pip install pyoptimus
```
By default Optimus install Pandas as the default engine, to install other engines you can use the following commands:
| Engine | Command |
|-----------|----------------------------------------|
| Dask | ```pip install pyoptimus[dask]``` |
| cuDF | ```pip install pyoptimus[cudf]``` |
| Dask-cuDF | ```pip install pyoptimus[dask-cudf]``` |
| Vaex | ```pip install pyoptimus[vaex]``` |
| Spark | ```pip install pyoptimus[spark]``` |
To install from the repo:
```
pip install git+https://github.com/hi-primus/optimus.git@develop-23.5
```
To install other engines:
```
pip install git+https://github.com/hi-primus/optimus.git@develop-23.5#egg=pyoptimus[dask]
```
### Requirements
* Python 3.7 or 3.8
## Examples
You can go to [10 minutes to Optimus](https://github.com/hi-primus/optimus/blob/develop-23.5/examples/10_min_to_optimus.ipynb) where you can find the basics to start working in a notebook.
Also you can go to the [Examples](https://github.com/hi-primus/optimus/tree/develop-23.5/examples/examples.md) section and find specific notebooks about data cleaning, data munging, profiling, data enrichment and how to create ML and DL models.
Here's a handy [Cheat Sheet](https://htmlpreview.github.io/?https://github.com/hi-primus/optimus/blob/develop-23.5/docs/cheatsheet/optimus_cheat_sheet.html) with the most common Optimus' operations.
## Start Optimus
Start Optimus using ```"pandas"```, ```"dask"```, ```"cudf"```,```"dask_cudf"```,```"vaex"``` or ```"spark"```.
```python
from optimus import Optimus
op = Optimus("pandas")
```
## Loading data
Now Optimus can load data in csv, json, parquet, avro and excel formats from a local file or from a URL.
```python
#csv
df = op.load.csv("../examples/data/foo.csv")
#json
df = op.load.json("../examples/data/foo.json")
# using a url
df = op.load.json("https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/examples/data/foo.json")
# parquet
df = op.load.parquet("../examples/data/foo.parquet")
# ...or anything else
df = op.load.file("../examples/data/titanic3.xls")
```
Also, you can load data from Oracle, Redshift, MySQL and Postgres databases.
## Saving Data
```python
#csv
df.save.csv("data/foo.csv")
# json
df.save.json("data/foo.json")
# parquet
df.save.parquet("data/foo.parquet")
```
You can also save data to oracle, redshift, mysql and postgres.
## Create dataframes
Also, you can create a dataframe from scratch
```python
df = op.create.dataframe({
'A': ['a', 'b', 'c', 'd'],
'B': [1, 3, 5, 7],
'C': [2, 4, 6, None],
'D': ['1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10']
})
```
Using `display` you have a beautiful way to show your data with extra information like column number, column data type and marked white spaces.
```python
display(df)
```
## Cleaning and Processing
Optimus was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that comes from Pandas.
Optimus expands the standard DataFrame functionality adding `.rows` and `.cols` accessors.
For example you can load data from a url, transform and apply some predefined cleaning functions:
```python
new_df = df\
.rows.sort("rank", "desc")\
.cols.lower(["names", "function"])\
.cols.date_format("date arrival", "yyyy/MM/dd", "dd-MM-YYYY")\
.cols.years_between("date arrival", "dd-MM-YYYY", output_cols="from arrival")\
.cols.normalize_chars("names")\
.cols.remove_special_chars("names")\
.rows.drop(df["rank"]>8)\
.cols.rename("*", str.lower)\
.cols.trim("*")\
.cols.unnest("japanese name", output_cols="other names")\
.cols.unnest("last position seen", separator=",", output_cols="pos")\
.cols.drop(["last position seen", "japanese name", "date arrival", "cybertronian", "nulltype"])
```
# Need help? 🛠️
## Feedback
Feedback is what drive Optimus future, so please take a couple of minutes to help shape the Optimus' Roadmap: http://bit.ly/optimus_survey
Also if you want to a suggestion or feature request use https://github.com/hi-primus/optimus/issues
## Troubleshooting
If you have issues, see our [Troubleshooting Guide](https://github.com/hi-primus/optimus/tree/develop-23.5/troubleshooting.md)
# Contributing to Optimus 💡
Contributions go far beyond pull requests and commits. We are very happy to receive any kind of contributions
including:
* [Documentation](https://docs.hi-optimus.com/en/latest/) updates, enhancements, designs, or bugfixes.
* Spelling or grammar fixes.
* README.md corrections or redesigns.
* Adding unit, or functional [tests](https://github.com/hi-primus/optimus/tree/develop-23.5/tests)
* Triaging GitHub issues -- especially determining whether an issue still persists or is reproducible.
* [Blogging, speaking about, or creating tutorials](https://hioptimus.com/category/blog/) about Optimus and its many features.
* Helping others on our official chats
# Backers and Sponsors
Become a [backer](https://opencollective.com/optimus#backer) or a [sponsor](https://opencollective.com/optimus#sponsor) and get your image on our README on Github with a link to your site.
[](#backers) [](#sponsors)
%package help
Summary: Development documents and examples for pyoptimus
Provides: python3-pyoptimus-doc
%description help
# Optimus
[](https://hi-optimus.com)
[](https://github.com/hi-primus/optimus/actions/workflows/main.yml)
[](https://hub.docker.com/r/hiprimus/optimus)
[](https://pypi.org/project/pyoptimus/)
[](https://github.com/hi-primus/optimus/releases)
[](http://calver.org)
[](https://pepy.tech/project/pyoptimus)
[](https://pepy.tech/project/pyoptimus/month)
[](https://pepy.tech/project/pyoptimus/week)
[](https://github.com/bulutyazilim/awesome-datascience)
[](https://communityinviter.com/apps/hi-bumblebee/welcome)
# Overview
Optimus is an opinionated python library to easily load, process, plot and create ML models that run over pandas, Dask, cuDF, dask-cuDF, Vaex or Spark.
Some amazing things Optimus can do for you:
* Process using a simple API, making it easy to use for newcomers.
* More than 100 functions to handle strings, process dates, urls and emails.
* Easily plot data from any size.
* Out of box functions to explore and fix data quality.
* Use the same code to process your data in your laptop or in a remote cluster of GPUs.
[See Documentation](https://docs.hi-optimus.com/en/latest/)
## Try Optimus
To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges:
[](https://mybinder.org/v2/gh/hi-primus/optimus/develop-23.5?filepath=https%3A%2F%2Fraw.githubusercontent.com%2Fhi-primus%2Foptimus%2Fdevelop-23.5%2Fexamples%2F10_min_to_optimus.ipynb)
[](https://colab.research.google.com/github/hi-primus/optimus/blob/master/examples/10_min_to_optimus_colab.ipynb)
## Installation (pip):
In your terminal just type:
```
pip install pyoptimus
```
By default Optimus install Pandas as the default engine, to install other engines you can use the following commands:
| Engine | Command |
|-----------|----------------------------------------|
| Dask | ```pip install pyoptimus[dask]``` |
| cuDF | ```pip install pyoptimus[cudf]``` |
| Dask-cuDF | ```pip install pyoptimus[dask-cudf]``` |
| Vaex | ```pip install pyoptimus[vaex]``` |
| Spark | ```pip install pyoptimus[spark]``` |
To install from the repo:
```
pip install git+https://github.com/hi-primus/optimus.git@develop-23.5
```
To install other engines:
```
pip install git+https://github.com/hi-primus/optimus.git@develop-23.5#egg=pyoptimus[dask]
```
### Requirements
* Python 3.7 or 3.8
## Examples
You can go to [10 minutes to Optimus](https://github.com/hi-primus/optimus/blob/develop-23.5/examples/10_min_to_optimus.ipynb) where you can find the basics to start working in a notebook.
Also you can go to the [Examples](https://github.com/hi-primus/optimus/tree/develop-23.5/examples/examples.md) section and find specific notebooks about data cleaning, data munging, profiling, data enrichment and how to create ML and DL models.
Here's a handy [Cheat Sheet](https://htmlpreview.github.io/?https://github.com/hi-primus/optimus/blob/develop-23.5/docs/cheatsheet/optimus_cheat_sheet.html) with the most common Optimus' operations.
## Start Optimus
Start Optimus using ```"pandas"```, ```"dask"```, ```"cudf"```,```"dask_cudf"```,```"vaex"``` or ```"spark"```.
```python
from optimus import Optimus
op = Optimus("pandas")
```
## Loading data
Now Optimus can load data in csv, json, parquet, avro and excel formats from a local file or from a URL.
```python
#csv
df = op.load.csv("../examples/data/foo.csv")
#json
df = op.load.json("../examples/data/foo.json")
# using a url
df = op.load.json("https://raw.githubusercontent.com/hi-primus/optimus/develop-23.5/examples/data/foo.json")
# parquet
df = op.load.parquet("../examples/data/foo.parquet")
# ...or anything else
df = op.load.file("../examples/data/titanic3.xls")
```
Also, you can load data from Oracle, Redshift, MySQL and Postgres databases.
## Saving Data
```python
#csv
df.save.csv("data/foo.csv")
# json
df.save.json("data/foo.json")
# parquet
df.save.parquet("data/foo.parquet")
```
You can also save data to oracle, redshift, mysql and postgres.
## Create dataframes
Also, you can create a dataframe from scratch
```python
df = op.create.dataframe({
'A': ['a', 'b', 'c', 'd'],
'B': [1, 3, 5, 7],
'C': [2, 4, 6, None],
'D': ['1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10']
})
```
Using `display` you have a beautiful way to show your data with extra information like column number, column data type and marked white spaces.
```python
display(df)
```
## Cleaning and Processing
Optimus was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that comes from Pandas.
Optimus expands the standard DataFrame functionality adding `.rows` and `.cols` accessors.
For example you can load data from a url, transform and apply some predefined cleaning functions:
```python
new_df = df\
.rows.sort("rank", "desc")\
.cols.lower(["names", "function"])\
.cols.date_format("date arrival", "yyyy/MM/dd", "dd-MM-YYYY")\
.cols.years_between("date arrival", "dd-MM-YYYY", output_cols="from arrival")\
.cols.normalize_chars("names")\
.cols.remove_special_chars("names")\
.rows.drop(df["rank"]>8)\
.cols.rename("*", str.lower)\
.cols.trim("*")\
.cols.unnest("japanese name", output_cols="other names")\
.cols.unnest("last position seen", separator=",", output_cols="pos")\
.cols.drop(["last position seen", "japanese name", "date arrival", "cybertronian", "nulltype"])
```
# Need help? 🛠️
## Feedback
Feedback is what drive Optimus future, so please take a couple of minutes to help shape the Optimus' Roadmap: http://bit.ly/optimus_survey
Also if you want to a suggestion or feature request use https://github.com/hi-primus/optimus/issues
## Troubleshooting
If you have issues, see our [Troubleshooting Guide](https://github.com/hi-primus/optimus/tree/develop-23.5/troubleshooting.md)
# Contributing to Optimus 💡
Contributions go far beyond pull requests and commits. We are very happy to receive any kind of contributions
including:
* [Documentation](https://docs.hi-optimus.com/en/latest/) updates, enhancements, designs, or bugfixes.
* Spelling or grammar fixes.
* README.md corrections or redesigns.
* Adding unit, or functional [tests](https://github.com/hi-primus/optimus/tree/develop-23.5/tests)
* Triaging GitHub issues -- especially determining whether an issue still persists or is reproducible.
* [Blogging, speaking about, or creating tutorials](https://hioptimus.com/category/blog/) about Optimus and its many features.
* Helping others on our official chats
# Backers and Sponsors
Become a [backer](https://opencollective.com/optimus#backer) or a [sponsor](https://opencollective.com/optimus#sponsor) and get your image on our README on Github with a link to your site.
[](#backers) [](#sponsors)
%prep
%autosetup -n pyoptimus-23.5.0b0
%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-pyoptimus -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 23.5.0b0-1
- Package Spec generated
|
