automatic import of python-modin

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+/modin-0.19.0.tar.gz
diff --git a/python-modin.spec b/python-modin.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-modin
+Version:	0.19.0
+Release:	1
+Summary:	Modin: Make your pandas code run faster by changing one line of code.
+License:	Apache 2
+URL:		https://github.com/modin-project/modin
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f0/21/d8756af2ce7441a043415ff65e08d7ed28af213dfff8a918c99dfd356af4/modin-0.19.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-pandas
+Requires:	python3-packaging
+Requires:	python3-numpy
+Requires:	python3-fsspec
+Requires:	python3-psutil
+Requires:	python3-dask
+Requires:	python3-distributed
+Requires:	python3-ray[default]
+Requires:	python3-pyarrow
+Requires:	python3-unidist[mpi]
+Requires:	python3-rpyc
+Requires:	python3-cloudpickle
+Requires:	python3-boto3
+Requires:	python3-modin-spreadsheet
+Requires:	python3-dask
+Requires:	python3-distributed
+Requires:	python3-ray[default]
+Requires:	python3-pyarrow
+Requires:	python3-rpyc
+Requires:	python3-cloudpickle
+Requires:	python3-boto3
+Requires:	python3-modin-spreadsheet
+Requires:	python3-dfsql
+Requires:	python3-pyparsing
+Requires:	python3-unidist[mpi]
+
+%description
+<p align="center"><a href="https://modin.readthedocs.io"><img width=77% alt="" src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/MODIN_ver2_hrz.png?raw=true"></a></p>
+<h2 align="center">Scale your pandas workflows by changing one line of code</h2>
+
+<div align="center">
+
+| <h3>Dev Community & Support</h3> | <h3>Forums</h3> | <h3>Socials</h3> | <h3>Docs</h3> |
+|:---: | :---: | :---: | :---: |
+| [![Slack](https://img.shields.io/badge/Slack-4A154B?style=for-the-badge&logo=slack&logoColor=white)](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA) | [![Stack Overflow](https://img.shields.io/badge/-Stackoverflow-FE7A16?style=for-the-badge&logo=stack-overflow&logoColor=white)](https://stackoverflow.com/questions/tagged/modin) | <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/modin_project?style=social" height=28 align="center"> | <a href="https://modin.readthedocs.io/en/latest/?badge=latest"><img alt="" src="https://readthedocs.org/projects/modin/badge/?version=latest" height=28 align="center"></a> |
+
+</div>
+
+<p align="center">
+<a href="https://pepy.tech/project/modin"><img src="https://static.pepy.tech/personalized-badge/modin?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads" align="center"></a>
+<a href="https://codecov.io/gh/modin-project/modin"><img src="https://codecov.io/gh/modin-project/modin/branch/master/graph/badge.svg" align="center"/></a>
+<a href="https://github.com/modin-project/modin/actions"><img src="https://github.com/modin-project/modin/workflows/master/badge.svg" align="center"></a>
+<a href="https://pypi.org/project/modin/"><img src="https://badge.fury.io/py/modin.svg" alt="PyPI version" align="center"></a>
+<a href="https://modin.org/modin-bench/#/"><img src="https://img.shields.io/badge/benchmarked%20by-asv-blue.svg" align="center"></a>
+</p>
+
+### What is Modin?
+
+Modin is a drop-in replacement for [pandas](https://github.com/pandas-dev/pandas). While pandas is
+single-threaded, Modin lets you instantly speed up your workflows by scaling pandas so it uses all of your
+cores. Modin works especially well on larger datasets, where pandas becomes painfully slow or runs
+[out of memory](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html).
+
+By simply replacing the import statement, Modin offers users effortless speed and scale for their pandas workflows:
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Import.gif" style="display: block;margin-left: auto;margin-right: auto;" width="100%"></img>
+
+In the GIFs below, Modin (left) and pandas (right) perform *the same pandas operations* on a 2GB dataset. The only difference between the two notebook examples is the import statement. 
+
+<table class="tg">
+<thead>
+  <tr>
+    <th class="tg-0lax" style="text-align: center;"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/MODIN_ver2_hrz.png?raw=True" height="35px"></th>
+    <th class="tg-0lax" style="text-align: center;"><img src="https://pandas.pydata.org/static/img/pandas.svg" height="50px"></img></th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-0lax"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Modin.gif"></img></td>
+    <td class="tg-0lax"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Pandas.gif"></img></td>
+  </tr>
+</tbody>
+</table>
+
+The charts below show the speedup you get by replacing pandas with Modin based on the examples above. The example notebooks can be found [here](examples/jupyter). To learn more about the speedups you could get with Modin and try out some examples on your own, check out our [10-minute quickstart guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html) to try out some examples on your own!
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Modin_Speedup.svg" style="display: block;margin-left: auto;margin-right: auto;" width="100%"></img>
+
+### Installation
+
+#### From PyPI
+
+Modin can be installed with `pip` on Linux, Windows and MacOS:
+
+```bash
+pip install modin[all] # (Recommended) Install Modin with all of Modin's currently supported engines.
+```
+
+If you want to install Modin with a specific engine, we recommend:
+
+```bash
+pip install modin[ray] # Install Modin dependencies and Ray.
+pip install modin[dask] # Install Modin dependencies and Dask.
+pip install modin[unidist] # Install Modin dependencies and Unidist to run on Unidist
+```
+
+Modin automatically detects which engine(s) you have installed and uses that for scheduling computation.
+
+#### From conda-forge
+
+Installing from [conda forge](https://github.com/conda-forge/modin-feedstock) using `modin-all`
+will install Modin and four engines: [Ray](https://github.com/ray-project/ray), [Dask](https://github.com/dask/dask),
+[Unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
+
+```bash
+conda install -c conda-forge modin-all
+```
+
+Each engine can also be installed individually (and also as a combination of several engines):
+
+```bash
+conda install -c conda-forge modin-ray  # Install Modin dependencies and Ray.
+conda install -c conda-forge modin-dask # Install Modin dependencies and Dask.
+conda install -c conda-forge modin-unidist # Install Modin dependencies and Unidist.
+conda install -c conda-forge modin-hdk # Install Modin dependencies and HDK.
+```
+
+To speed up conda installation we recommend using libmamba solver. To do this install it in a base environment:
+
+```bash
+conda install -n base conda-libmamba-solver
+```
+
+and then use it during istallation either like:
+
+```bash
+conda install -c conda-forge modin-ray modin-hdk --experimental-solver=libmamba
+```
+
+or starting from conda 22.11 and libmamba solver 22.12 versions:
+
+```bash
+conda install -c conda-forge modin-ray modin-hdk --solver=libmamba
+```
+
+#### Choosing a Compute Engine
+
+If you want to choose a specific compute engine to run on, you can set the environment
+variable `MODIN_ENGINE` and Modin will do computation with that engine:
+
+```bash
+export MODIN_ENGINE=ray  # Modin will use Ray
+export MODIN_ENGINE=dask  # Modin will use Dask
+export MODIN_ENGINE=unidist # Modin will use Unidist
+```
+
+If you want to choose the Unidist engine, you should set the additional environment 
+variable ``UNIDIST_BACKEND``, because currently Modin only supports Unidist on MPI:
+
+```bash
+export UNIDIST_BACKEND=mpi # Unidist will use MPI backend
+```
+
+This can also be done within a notebook/interpreter before you import Modin:
+
+```python
+import modin.config as modin_cfg
+import unidist.config as unidist_cfg
+
+modin_cfg.Engine.put("ray")  # Modin will use Ray
+modin_cfg.Engine.put("dask")  # Modin will use Dask
+
+modin_cfg.Engine.put('unidist') # Modin will use Unidist
+unidist_cfg.Backend.put('mpi') # Unidist will use MPI backend
+```
+
+Check [this Modin docs section](https://modin.readthedocs.io/en/latest/development/using_hdk.html) for HDK engine setup.
+
+_Note: You should not change the engine after your first operation with Modin as it will result in undefined behavior._
+
+#### Which engine should I use?
+
+On Linux, MacOS, and Windows you can install and use either Ray, Dask or Unidist. There is no knowledge required
+to use either of these engines as Modin abstracts away all of the complexity, so feel
+free to pick either!
+
+On Linux you also can choose [HDK](https://modin.readthedocs.io/en/latest/development/using_hdk.html), which is an experimental
+engine based on [HDK](https://github.com/intel-ai/hdk) and included in the
+[Intel® Distribution of Modin](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/distribution-of-modin.html),
+which is a part of [Intel® oneAPI AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html).
+
+### Pandas API Coverage
+
+<p align="center">
+
+| pandas Object     | Modin's Ray Engine Coverage                                                          | Modin's Dask Engine Coverage | Modin's Unidist Engine Coverage |
+|-------------------|:------------------------------------------------------------------------------------:|:---------------:|:---------------:|
+| `pd.DataFrame`    | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> |
+| `pd.Series`       | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> 
+| `pd.read_csv`     | ✅                                               | ✅ | ✅ |
+| `pd.read_table`   | ✅                                               | ✅ | ✅ |
+| `pd.read_parquet` | ✅                                               | ✅ | ✅ |
+| `pd.read_sql`     | ✅                                               | ✅ | ✅ |
+| `pd.read_feather` | ✅                                               | ✅ | ✅ |
+| `pd.read_excel`   | ✅                                               | ✅ | ✅ |
+| `pd.read_json`    | [✳️](https://github.com/modin-project/modin/issues/554)                                         | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) |
+| `pd.read_<other>` | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) |
+
+</p>
+Some pandas APIs are easier to implement than others, so if something is missing feel
+free to open an issue!
+
+### More about Modin
+
+For the complete documentation on Modin, visit our [ReadTheDocs](https://modin.readthedocs.io/en/latest/index.html) page.
+
+#### Scale your pandas workflow by changing a single line of code.
+
+_Note: In local mode (without a cluster), Modin will create and manage a local (Dask or Ray) cluster for the execution._
+
+To use Modin, you do not need to specify how to distribute the data, or even know how many
+cores your system has. In fact, you can continue using your previous
+pandas notebooks while experiencing a considerable speedup from Modin, even on a single
+machine. Once you've changed your import statement, you're ready to use Modin just like
+you would with pandas!
+
+#### Faster pandas, even on your laptop
+
+<img align="right" style="display:inline;" height="350" width="300" src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/read_csv_benchmark.png?raw=true"></a>
+
+The `modin.pandas` DataFrame is an extremely light-weight parallel DataFrame.
+Modin transparently distributes the data and computation so that you can continue using the same pandas API
+while working with more data faster. Because it is so light-weight,
+Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.
+
+In pandas, you are only able to use one core at a time when you are doing computation of
+any kind. With Modin, you are able to use all of the CPU cores on your machine. Even with a
+traditionally synchronous task like `read_csv`, we see large speedups by efficiently
+distributing the work across your entire machine.
+
+```python
+import modin.pandas as pd
+
+df = pd.read_csv("my_dataset.csv")
+```
+
+#### Modin can handle the datasets that pandas can't 
+
+Often data scientists have to switch between different tools
+for operating on datasets of different sizes. Processing large dataframes with pandas
+is slow, and pandas does not support working with dataframes that are too large to fit
+into the available memory. As a result, pandas workflows that work well
+for prototyping on a few MBs of data do not scale to tens or hundreds of GBs (depending on the size
+of your machine). Modin supports operating on data that does not fit in memory, so that you can comfortably
+work with hundreds of GBs without worrying about substantial slowdown or memory errors.
+With [cluster](https://modin.readthedocs.io/en/latest/getting_started/using_modin/using_modin_cluster.html)
+and [out of core](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html)
+support, Modin is a DataFrame library with both great single-node performance and high
+scalability in a cluster.
+
+#### Modin Architecture
+
+We designed [Modin's architecture](https://modin.readthedocs.io/en/latest/development/architecture.html)
+to be modular so we can plug in different components as they develop and improve:
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/modin_architecture.png" alt="Modin's architecture" width="75%"></img>
+
+### Other Resources
+
+#### Getting Started with Modin
+
+- [Documentation](https://modin.readthedocs.io/en/latest/)
+- [10-min Quickstart Guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html)
+- [Examples and Tutorials](https://modin.readthedocs.io/en/latest/getting_started/examples.html)
+- [Videos and Blogposts](https://modin.readthedocs.io/en/latest/getting_started/examples.html#talks-podcasts)
+- [Benchmarking Modin](https://modin.readthedocs.io/en/latest/usage_guide/benchmarking.html)
+
+#### Modin Community
+
+- [Slack](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA)
+- [Discourse](https://discuss.modin.org)
+- [Twitter](https://twitter.com/modin_project)
+- [Mailing List](https://groups.google.com/g/modin-dev)
+- [GitHub Issues](https://github.com/modin-project/modin/issues)
+- [StackOverflow](https://stackoverflow.com/questions/tagged/modin)
+
+#### Learn More about Modin
+
+- [Frequently Asked Questions (FAQs)](https://modin.readthedocs.io/en/latest/getting_started/faq.html)
+- [Troubleshooting Guide](https://modin.readthedocs.io/en/latest/getting_started/troubleshooting.html)
+- [Development Guide](https://modin.readthedocs.io/en/latest/development/index.html)
+- Modin is built on many years of research and development at UC Berkeley. Check out these selected papers to learn more about how Modin works:
+  - [Flexible Rule-Based Decomposition and Metadata Independence in Modin](https://people.eecs.berkeley.edu/~totemtang/paper/Modin.pdf) (VLDB 2021)
+  - [Dataframe Systems: Theory, Architecture, and Implementation](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-193.pdf) (PhD Dissertation 2021)
+  - [Towards Scalable Dataframe Systems](https://arxiv.org/pdf/2001.00888.pdf) (VLDB 2020)
+
+#### Getting Involved
+
+***`modin.pandas` is currently under active development. Requests and contributions are welcome!***
+
+For more information on how to contribute to Modin, check out the
+[Modin Contribution Guide](https://modin.readthedocs.io/en/latest/development/contributing.html).
+
+### License
+
+[Apache License 2.0](LICENSE)
+
+
+
+
+%package -n python3-modin
+Summary:	Modin: Make your pandas code run faster by changing one line of code.
+Provides:	python-modin
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-modin
+<p align="center"><a href="https://modin.readthedocs.io"><img width=77% alt="" src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/MODIN_ver2_hrz.png?raw=true"></a></p>
+<h2 align="center">Scale your pandas workflows by changing one line of code</h2>
+
+<div align="center">
+
+| <h3>Dev Community & Support</h3> | <h3>Forums</h3> | <h3>Socials</h3> | <h3>Docs</h3> |
+|:---: | :---: | :---: | :---: |
+| [![Slack](https://img.shields.io/badge/Slack-4A154B?style=for-the-badge&logo=slack&logoColor=white)](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA) | [![Stack Overflow](https://img.shields.io/badge/-Stackoverflow-FE7A16?style=for-the-badge&logo=stack-overflow&logoColor=white)](https://stackoverflow.com/questions/tagged/modin) | <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/modin_project?style=social" height=28 align="center"> | <a href="https://modin.readthedocs.io/en/latest/?badge=latest"><img alt="" src="https://readthedocs.org/projects/modin/badge/?version=latest" height=28 align="center"></a> |
+
+</div>
+
+<p align="center">
+<a href="https://pepy.tech/project/modin"><img src="https://static.pepy.tech/personalized-badge/modin?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads" align="center"></a>
+<a href="https://codecov.io/gh/modin-project/modin"><img src="https://codecov.io/gh/modin-project/modin/branch/master/graph/badge.svg" align="center"/></a>
+<a href="https://github.com/modin-project/modin/actions"><img src="https://github.com/modin-project/modin/workflows/master/badge.svg" align="center"></a>
+<a href="https://pypi.org/project/modin/"><img src="https://badge.fury.io/py/modin.svg" alt="PyPI version" align="center"></a>
+<a href="https://modin.org/modin-bench/#/"><img src="https://img.shields.io/badge/benchmarked%20by-asv-blue.svg" align="center"></a>
+</p>
+
+### What is Modin?
+
+Modin is a drop-in replacement for [pandas](https://github.com/pandas-dev/pandas). While pandas is
+single-threaded, Modin lets you instantly speed up your workflows by scaling pandas so it uses all of your
+cores. Modin works especially well on larger datasets, where pandas becomes painfully slow or runs
+[out of memory](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html).
+
+By simply replacing the import statement, Modin offers users effortless speed and scale for their pandas workflows:
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Import.gif" style="display: block;margin-left: auto;margin-right: auto;" width="100%"></img>
+
+In the GIFs below, Modin (left) and pandas (right) perform *the same pandas operations* on a 2GB dataset. The only difference between the two notebook examples is the import statement. 
+
+<table class="tg">
+<thead>
+  <tr>
+    <th class="tg-0lax" style="text-align: center;"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/MODIN_ver2_hrz.png?raw=True" height="35px"></th>
+    <th class="tg-0lax" style="text-align: center;"><img src="https://pandas.pydata.org/static/img/pandas.svg" height="50px"></img></th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-0lax"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Modin.gif"></img></td>
+    <td class="tg-0lax"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Pandas.gif"></img></td>
+  </tr>
+</tbody>
+</table>
+
+The charts below show the speedup you get by replacing pandas with Modin based on the examples above. The example notebooks can be found [here](examples/jupyter). To learn more about the speedups you could get with Modin and try out some examples on your own, check out our [10-minute quickstart guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html) to try out some examples on your own!
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Modin_Speedup.svg" style="display: block;margin-left: auto;margin-right: auto;" width="100%"></img>
+
+### Installation
+
+#### From PyPI
+
+Modin can be installed with `pip` on Linux, Windows and MacOS:
+
+```bash
+pip install modin[all] # (Recommended) Install Modin with all of Modin's currently supported engines.
+```
+
+If you want to install Modin with a specific engine, we recommend:
+
+```bash
+pip install modin[ray] # Install Modin dependencies and Ray.
+pip install modin[dask] # Install Modin dependencies and Dask.
+pip install modin[unidist] # Install Modin dependencies and Unidist to run on Unidist
+```
+
+Modin automatically detects which engine(s) you have installed and uses that for scheduling computation.
+
+#### From conda-forge
+
+Installing from [conda forge](https://github.com/conda-forge/modin-feedstock) using `modin-all`
+will install Modin and four engines: [Ray](https://github.com/ray-project/ray), [Dask](https://github.com/dask/dask),
+[Unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
+
+```bash
+conda install -c conda-forge modin-all
+```
+
+Each engine can also be installed individually (and also as a combination of several engines):
+
+```bash
+conda install -c conda-forge modin-ray  # Install Modin dependencies and Ray.
+conda install -c conda-forge modin-dask # Install Modin dependencies and Dask.
+conda install -c conda-forge modin-unidist # Install Modin dependencies and Unidist.
+conda install -c conda-forge modin-hdk # Install Modin dependencies and HDK.
+```
+
+To speed up conda installation we recommend using libmamba solver. To do this install it in a base environment:
+
+```bash
+conda install -n base conda-libmamba-solver
+```
+
+and then use it during istallation either like:
+
+```bash
+conda install -c conda-forge modin-ray modin-hdk --experimental-solver=libmamba
+```
+
+or starting from conda 22.11 and libmamba solver 22.12 versions:
+
+```bash
+conda install -c conda-forge modin-ray modin-hdk --solver=libmamba
+```
+
+#### Choosing a Compute Engine
+
+If you want to choose a specific compute engine to run on, you can set the environment
+variable `MODIN_ENGINE` and Modin will do computation with that engine:
+
+```bash
+export MODIN_ENGINE=ray  # Modin will use Ray
+export MODIN_ENGINE=dask  # Modin will use Dask
+export MODIN_ENGINE=unidist # Modin will use Unidist
+```
+
+If you want to choose the Unidist engine, you should set the additional environment 
+variable ``UNIDIST_BACKEND``, because currently Modin only supports Unidist on MPI:
+
+```bash
+export UNIDIST_BACKEND=mpi # Unidist will use MPI backend
+```
+
+This can also be done within a notebook/interpreter before you import Modin:
+
+```python
+import modin.config as modin_cfg
+import unidist.config as unidist_cfg
+
+modin_cfg.Engine.put("ray")  # Modin will use Ray
+modin_cfg.Engine.put("dask")  # Modin will use Dask
+
+modin_cfg.Engine.put('unidist') # Modin will use Unidist
+unidist_cfg.Backend.put('mpi') # Unidist will use MPI backend
+```
+
+Check [this Modin docs section](https://modin.readthedocs.io/en/latest/development/using_hdk.html) for HDK engine setup.
+
+_Note: You should not change the engine after your first operation with Modin as it will result in undefined behavior._
+
+#### Which engine should I use?
+
+On Linux, MacOS, and Windows you can install and use either Ray, Dask or Unidist. There is no knowledge required
+to use either of these engines as Modin abstracts away all of the complexity, so feel
+free to pick either!
+
+On Linux you also can choose [HDK](https://modin.readthedocs.io/en/latest/development/using_hdk.html), which is an experimental
+engine based on [HDK](https://github.com/intel-ai/hdk) and included in the
+[Intel® Distribution of Modin](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/distribution-of-modin.html),
+which is a part of [Intel® oneAPI AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html).
+
+### Pandas API Coverage
+
+<p align="center">
+
+| pandas Object     | Modin's Ray Engine Coverage                                                          | Modin's Dask Engine Coverage | Modin's Unidist Engine Coverage |
+|-------------------|:------------------------------------------------------------------------------------:|:---------------:|:---------------:|
+| `pd.DataFrame`    | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> |
+| `pd.Series`       | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> 
+| `pd.read_csv`     | ✅                                               | ✅ | ✅ |
+| `pd.read_table`   | ✅                                               | ✅ | ✅ |
+| `pd.read_parquet` | ✅                                               | ✅ | ✅ |
+| `pd.read_sql`     | ✅                                               | ✅ | ✅ |
+| `pd.read_feather` | ✅                                               | ✅ | ✅ |
+| `pd.read_excel`   | ✅                                               | ✅ | ✅ |
+| `pd.read_json`    | [✳️](https://github.com/modin-project/modin/issues/554)                                         | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) |
+| `pd.read_<other>` | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) |
+
+</p>
+Some pandas APIs are easier to implement than others, so if something is missing feel
+free to open an issue!
+
+### More about Modin
+
+For the complete documentation on Modin, visit our [ReadTheDocs](https://modin.readthedocs.io/en/latest/index.html) page.
+
+#### Scale your pandas workflow by changing a single line of code.
+
+_Note: In local mode (without a cluster), Modin will create and manage a local (Dask or Ray) cluster for the execution._
+
+To use Modin, you do not need to specify how to distribute the data, or even know how many
+cores your system has. In fact, you can continue using your previous
+pandas notebooks while experiencing a considerable speedup from Modin, even on a single
+machine. Once you've changed your import statement, you're ready to use Modin just like
+you would with pandas!
+
+#### Faster pandas, even on your laptop
+
+<img align="right" style="display:inline;" height="350" width="300" src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/read_csv_benchmark.png?raw=true"></a>
+
+The `modin.pandas` DataFrame is an extremely light-weight parallel DataFrame.
+Modin transparently distributes the data and computation so that you can continue using the same pandas API
+while working with more data faster. Because it is so light-weight,
+Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.
+
+In pandas, you are only able to use one core at a time when you are doing computation of
+any kind. With Modin, you are able to use all of the CPU cores on your machine. Even with a
+traditionally synchronous task like `read_csv`, we see large speedups by efficiently
+distributing the work across your entire machine.
+
+```python
+import modin.pandas as pd
+
+df = pd.read_csv("my_dataset.csv")
+```
+
+#### Modin can handle the datasets that pandas can't 
+
+Often data scientists have to switch between different tools
+for operating on datasets of different sizes. Processing large dataframes with pandas
+is slow, and pandas does not support working with dataframes that are too large to fit
+into the available memory. As a result, pandas workflows that work well
+for prototyping on a few MBs of data do not scale to tens or hundreds of GBs (depending on the size
+of your machine). Modin supports operating on data that does not fit in memory, so that you can comfortably
+work with hundreds of GBs without worrying about substantial slowdown or memory errors.
+With [cluster](https://modin.readthedocs.io/en/latest/getting_started/using_modin/using_modin_cluster.html)
+and [out of core](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html)
+support, Modin is a DataFrame library with both great single-node performance and high
+scalability in a cluster.
+
+#### Modin Architecture
+
+We designed [Modin's architecture](https://modin.readthedocs.io/en/latest/development/architecture.html)
+to be modular so we can plug in different components as they develop and improve:
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/modin_architecture.png" alt="Modin's architecture" width="75%"></img>
+
+### Other Resources
+
+#### Getting Started with Modin
+
+- [Documentation](https://modin.readthedocs.io/en/latest/)
+- [10-min Quickstart Guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html)
+- [Examples and Tutorials](https://modin.readthedocs.io/en/latest/getting_started/examples.html)
+- [Videos and Blogposts](https://modin.readthedocs.io/en/latest/getting_started/examples.html#talks-podcasts)
+- [Benchmarking Modin](https://modin.readthedocs.io/en/latest/usage_guide/benchmarking.html)
+
+#### Modin Community
+
+- [Slack](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA)
+- [Discourse](https://discuss.modin.org)
+- [Twitter](https://twitter.com/modin_project)
+- [Mailing List](https://groups.google.com/g/modin-dev)
+- [GitHub Issues](https://github.com/modin-project/modin/issues)
+- [StackOverflow](https://stackoverflow.com/questions/tagged/modin)
+
+#### Learn More about Modin
+
+- [Frequently Asked Questions (FAQs)](https://modin.readthedocs.io/en/latest/getting_started/faq.html)
+- [Troubleshooting Guide](https://modin.readthedocs.io/en/latest/getting_started/troubleshooting.html)
+- [Development Guide](https://modin.readthedocs.io/en/latest/development/index.html)
+- Modin is built on many years of research and development at UC Berkeley. Check out these selected papers to learn more about how Modin works:
+  - [Flexible Rule-Based Decomposition and Metadata Independence in Modin](https://people.eecs.berkeley.edu/~totemtang/paper/Modin.pdf) (VLDB 2021)
+  - [Dataframe Systems: Theory, Architecture, and Implementation](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-193.pdf) (PhD Dissertation 2021)
+  - [Towards Scalable Dataframe Systems](https://arxiv.org/pdf/2001.00888.pdf) (VLDB 2020)
+
+#### Getting Involved
+
+***`modin.pandas` is currently under active development. Requests and contributions are welcome!***
+
+For more information on how to contribute to Modin, check out the
+[Modin Contribution Guide](https://modin.readthedocs.io/en/latest/development/contributing.html).
+
+### License
+
+[Apache License 2.0](LICENSE)
+
+
+
+
+%package help
+Summary:	Development documents and examples for modin
+Provides:	python3-modin-doc
+%description help
+<p align="center"><a href="https://modin.readthedocs.io"><img width=77% alt="" src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/MODIN_ver2_hrz.png?raw=true"></a></p>
+<h2 align="center">Scale your pandas workflows by changing one line of code</h2>
+
+<div align="center">
+
+| <h3>Dev Community & Support</h3> | <h3>Forums</h3> | <h3>Socials</h3> | <h3>Docs</h3> |
+|:---: | :---: | :---: | :---: |
+| [![Slack](https://img.shields.io/badge/Slack-4A154B?style=for-the-badge&logo=slack&logoColor=white)](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA) | [![Stack Overflow](https://img.shields.io/badge/-Stackoverflow-FE7A16?style=for-the-badge&logo=stack-overflow&logoColor=white)](https://stackoverflow.com/questions/tagged/modin) | <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/modin_project?style=social" height=28 align="center"> | <a href="https://modin.readthedocs.io/en/latest/?badge=latest"><img alt="" src="https://readthedocs.org/projects/modin/badge/?version=latest" height=28 align="center"></a> |
+
+</div>
+
+<p align="center">
+<a href="https://pepy.tech/project/modin"><img src="https://static.pepy.tech/personalized-badge/modin?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads" align="center"></a>
+<a href="https://codecov.io/gh/modin-project/modin"><img src="https://codecov.io/gh/modin-project/modin/branch/master/graph/badge.svg" align="center"/></a>
+<a href="https://github.com/modin-project/modin/actions"><img src="https://github.com/modin-project/modin/workflows/master/badge.svg" align="center"></a>
+<a href="https://pypi.org/project/modin/"><img src="https://badge.fury.io/py/modin.svg" alt="PyPI version" align="center"></a>
+<a href="https://modin.org/modin-bench/#/"><img src="https://img.shields.io/badge/benchmarked%20by-asv-blue.svg" align="center"></a>
+</p>
+
+### What is Modin?
+
+Modin is a drop-in replacement for [pandas](https://github.com/pandas-dev/pandas). While pandas is
+single-threaded, Modin lets you instantly speed up your workflows by scaling pandas so it uses all of your
+cores. Modin works especially well on larger datasets, where pandas becomes painfully slow or runs
+[out of memory](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html).
+
+By simply replacing the import statement, Modin offers users effortless speed and scale for their pandas workflows:
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Import.gif" style="display: block;margin-left: auto;margin-right: auto;" width="100%"></img>
+
+In the GIFs below, Modin (left) and pandas (right) perform *the same pandas operations* on a 2GB dataset. The only difference between the two notebook examples is the import statement. 
+
+<table class="tg">
+<thead>
+  <tr>
+    <th class="tg-0lax" style="text-align: center;"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/MODIN_ver2_hrz.png?raw=True" height="35px"></th>
+    <th class="tg-0lax" style="text-align: center;"><img src="https://pandas.pydata.org/static/img/pandas.svg" height="50px"></img></th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-0lax"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Modin.gif"></img></td>
+    <td class="tg-0lax"><img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Pandas.gif"></img></td>
+  </tr>
+</tbody>
+</table>
+
+The charts below show the speedup you get by replacing pandas with Modin based on the examples above. The example notebooks can be found [here](examples/jupyter). To learn more about the speedups you could get with Modin and try out some examples on your own, check out our [10-minute quickstart guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html) to try out some examples on your own!
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/Modin_Speedup.svg" style="display: block;margin-left: auto;margin-right: auto;" width="100%"></img>
+
+### Installation
+
+#### From PyPI
+
+Modin can be installed with `pip` on Linux, Windows and MacOS:
+
+```bash
+pip install modin[all] # (Recommended) Install Modin with all of Modin's currently supported engines.
+```
+
+If you want to install Modin with a specific engine, we recommend:
+
+```bash
+pip install modin[ray] # Install Modin dependencies and Ray.
+pip install modin[dask] # Install Modin dependencies and Dask.
+pip install modin[unidist] # Install Modin dependencies and Unidist to run on Unidist
+```
+
+Modin automatically detects which engine(s) you have installed and uses that for scheduling computation.
+
+#### From conda-forge
+
+Installing from [conda forge](https://github.com/conda-forge/modin-feedstock) using `modin-all`
+will install Modin and four engines: [Ray](https://github.com/ray-project/ray), [Dask](https://github.com/dask/dask),
+[Unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
+
+```bash
+conda install -c conda-forge modin-all
+```
+
+Each engine can also be installed individually (and also as a combination of several engines):
+
+```bash
+conda install -c conda-forge modin-ray  # Install Modin dependencies and Ray.
+conda install -c conda-forge modin-dask # Install Modin dependencies and Dask.
+conda install -c conda-forge modin-unidist # Install Modin dependencies and Unidist.
+conda install -c conda-forge modin-hdk # Install Modin dependencies and HDK.
+```
+
+To speed up conda installation we recommend using libmamba solver. To do this install it in a base environment:
+
+```bash
+conda install -n base conda-libmamba-solver
+```
+
+and then use it during istallation either like:
+
+```bash
+conda install -c conda-forge modin-ray modin-hdk --experimental-solver=libmamba
+```
+
+or starting from conda 22.11 and libmamba solver 22.12 versions:
+
+```bash
+conda install -c conda-forge modin-ray modin-hdk --solver=libmamba
+```
+
+#### Choosing a Compute Engine
+
+If you want to choose a specific compute engine to run on, you can set the environment
+variable `MODIN_ENGINE` and Modin will do computation with that engine:
+
+```bash
+export MODIN_ENGINE=ray  # Modin will use Ray
+export MODIN_ENGINE=dask  # Modin will use Dask
+export MODIN_ENGINE=unidist # Modin will use Unidist
+```
+
+If you want to choose the Unidist engine, you should set the additional environment 
+variable ``UNIDIST_BACKEND``, because currently Modin only supports Unidist on MPI:
+
+```bash
+export UNIDIST_BACKEND=mpi # Unidist will use MPI backend
+```
+
+This can also be done within a notebook/interpreter before you import Modin:
+
+```python
+import modin.config as modin_cfg
+import unidist.config as unidist_cfg
+
+modin_cfg.Engine.put("ray")  # Modin will use Ray
+modin_cfg.Engine.put("dask")  # Modin will use Dask
+
+modin_cfg.Engine.put('unidist') # Modin will use Unidist
+unidist_cfg.Backend.put('mpi') # Unidist will use MPI backend
+```
+
+Check [this Modin docs section](https://modin.readthedocs.io/en/latest/development/using_hdk.html) for HDK engine setup.
+
+_Note: You should not change the engine after your first operation with Modin as it will result in undefined behavior._
+
+#### Which engine should I use?
+
+On Linux, MacOS, and Windows you can install and use either Ray, Dask or Unidist. There is no knowledge required
+to use either of these engines as Modin abstracts away all of the complexity, so feel
+free to pick either!
+
+On Linux you also can choose [HDK](https://modin.readthedocs.io/en/latest/development/using_hdk.html), which is an experimental
+engine based on [HDK](https://github.com/intel-ai/hdk) and included in the
+[Intel® Distribution of Modin](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/distribution-of-modin.html),
+which is a part of [Intel® oneAPI AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html).
+
+### Pandas API Coverage
+
+<p align="center">
+
+| pandas Object     | Modin's Ray Engine Coverage                                                          | Modin's Dask Engine Coverage | Modin's Unidist Engine Coverage |
+|-------------------|:------------------------------------------------------------------------------------:|:---------------:|:---------------:|
+| `pd.DataFrame`    | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> | <img src=https://img.shields.io/badge/api%20coverage-90.8%25-hunter.svg> |
+| `pd.Series`       | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> | <img src=https://img.shields.io/badge/api%20coverage-88.05%25-green.svg> 
+| `pd.read_csv`     | ✅                                               | ✅ | ✅ |
+| `pd.read_table`   | ✅                                               | ✅ | ✅ |
+| `pd.read_parquet` | ✅                                               | ✅ | ✅ |
+| `pd.read_sql`     | ✅                                               | ✅ | ✅ |
+| `pd.read_feather` | ✅                                               | ✅ | ✅ |
+| `pd.read_excel`   | ✅                                               | ✅ | ✅ |
+| `pd.read_json`    | [✳️](https://github.com/modin-project/modin/issues/554)                                         | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) |
+| `pd.read_<other>` | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) | [✴️](https://modin.readthedocs.io/en/latest/supported_apis/io_supported.html) |
+
+</p>
+Some pandas APIs are easier to implement than others, so if something is missing feel
+free to open an issue!
+
+### More about Modin
+
+For the complete documentation on Modin, visit our [ReadTheDocs](https://modin.readthedocs.io/en/latest/index.html) page.
+
+#### Scale your pandas workflow by changing a single line of code.
+
+_Note: In local mode (without a cluster), Modin will create and manage a local (Dask or Ray) cluster for the execution._
+
+To use Modin, you do not need to specify how to distribute the data, or even know how many
+cores your system has. In fact, you can continue using your previous
+pandas notebooks while experiencing a considerable speedup from Modin, even on a single
+machine. Once you've changed your import statement, you're ready to use Modin just like
+you would with pandas!
+
+#### Faster pandas, even on your laptop
+
+<img align="right" style="display:inline;" height="350" width="300" src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/read_csv_benchmark.png?raw=true"></a>
+
+The `modin.pandas` DataFrame is an extremely light-weight parallel DataFrame.
+Modin transparently distributes the data and computation so that you can continue using the same pandas API
+while working with more data faster. Because it is so light-weight,
+Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.
+
+In pandas, you are only able to use one core at a time when you are doing computation of
+any kind. With Modin, you are able to use all of the CPU cores on your machine. Even with a
+traditionally synchronous task like `read_csv`, we see large speedups by efficiently
+distributing the work across your entire machine.
+
+```python
+import modin.pandas as pd
+
+df = pd.read_csv("my_dataset.csv")
+```
+
+#### Modin can handle the datasets that pandas can't 
+
+Often data scientists have to switch between different tools
+for operating on datasets of different sizes. Processing large dataframes with pandas
+is slow, and pandas does not support working with dataframes that are too large to fit
+into the available memory. As a result, pandas workflows that work well
+for prototyping on a few MBs of data do not scale to tens or hundreds of GBs (depending on the size
+of your machine). Modin supports operating on data that does not fit in memory, so that you can comfortably
+work with hundreds of GBs without worrying about substantial slowdown or memory errors.
+With [cluster](https://modin.readthedocs.io/en/latest/getting_started/using_modin/using_modin_cluster.html)
+and [out of core](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html)
+support, Modin is a DataFrame library with both great single-node performance and high
+scalability in a cluster.
+
+#### Modin Architecture
+
+We designed [Modin's architecture](https://modin.readthedocs.io/en/latest/development/architecture.html)
+to be modular so we can plug in different components as they develop and improve:
+
+<img src="https://github.com/modin-project/modin/raw/7c009c747caa90554607e30b9ac2bd1b190b8c7d/docs/img/modin_architecture.png" alt="Modin's architecture" width="75%"></img>
+
+### Other Resources
+
+#### Getting Started with Modin
+
+- [Documentation](https://modin.readthedocs.io/en/latest/)
+- [10-min Quickstart Guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html)
+- [Examples and Tutorials](https://modin.readthedocs.io/en/latest/getting_started/examples.html)
+- [Videos and Blogposts](https://modin.readthedocs.io/en/latest/getting_started/examples.html#talks-podcasts)
+- [Benchmarking Modin](https://modin.readthedocs.io/en/latest/usage_guide/benchmarking.html)
+
+#### Modin Community
+
+- [Slack](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA)
+- [Discourse](https://discuss.modin.org)
+- [Twitter](https://twitter.com/modin_project)
+- [Mailing List](https://groups.google.com/g/modin-dev)
+- [GitHub Issues](https://github.com/modin-project/modin/issues)
+- [StackOverflow](https://stackoverflow.com/questions/tagged/modin)
+
+#### Learn More about Modin
+
+- [Frequently Asked Questions (FAQs)](https://modin.readthedocs.io/en/latest/getting_started/faq.html)
+- [Troubleshooting Guide](https://modin.readthedocs.io/en/latest/getting_started/troubleshooting.html)
+- [Development Guide](https://modin.readthedocs.io/en/latest/development/index.html)
+- Modin is built on many years of research and development at UC Berkeley. Check out these selected papers to learn more about how Modin works:
+  - [Flexible Rule-Based Decomposition and Metadata Independence in Modin](https://people.eecs.berkeley.edu/~totemtang/paper/Modin.pdf) (VLDB 2021)
+  - [Dataframe Systems: Theory, Architecture, and Implementation](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-193.pdf) (PhD Dissertation 2021)
+  - [Towards Scalable Dataframe Systems](https://arxiv.org/pdf/2001.00888.pdf) (VLDB 2020)
+
+#### Getting Involved
+
+***`modin.pandas` is currently under active development. Requests and contributions are welcome!***
+
+For more information on how to contribute to Modin, check out the
+[Modin Contribution Guide](https://modin.readthedocs.io/en/latest/development/contributing.html).
+
+### License
+
+[Apache License 2.0](LICENSE)
+
+
+
+
+%prep
+%autosetup -n modin-0.19.0
+
+%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-modin -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.19.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..27c0a2b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+c8238e5bdc0ad1fbfd2c8bdfd5d6e8d7  modin-0.19.0.tar.gz