automatic import of python-dataprep

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+/dataprep-0.4.5.tar.gz
diff --git a/python-dataprep.spec b/python-dataprep.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-dataprep
+Version:	0.4.5
+Release:	1
+Summary:	Dataprep: Data Preparation in Python
+License:	MIT
+URL:		https://github.com/sfu-db/dataprep
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f5/dc/283421cb08790a0d4563bcb03d4a0d6a5aa2e23026361d327027e9f958db/dataprep-0.4.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-bokeh
+Requires:	python3-dask[dataframe,array,delayed]
+Requires:	python3-nltk
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-pydot
+Requires:	python3-scipy
+Requires:	python3-wordcloud
+Requires:	python3-sqlalchemy
+Requires:	python3-aiohttp
+Requires:	python3-ipywidgets
+Requires:	python3-jinja2
+Requires:	python3-jsonpath-ng
+Requires:	python3-pydantic
+Requires:	python3-tqdm
+Requires:	python3-flask
+Requires:	python3-flask_cors
+Requires:	python3-metaphone
+Requires:	python3-stdnum
+Requires:	python3-regex
+Requires:	python3-varname
+Requires:	python3-crfsuite
+Requires:	python3-rapidfuzz
+
+%description
+<p align="center">
+  <a href="LICENSE"><img src="https://img.shields.io/pypi/l/dataprep?style=flat-square"/></a>
+  <a href="https://sfu-db.github.io/dataprep/"><img src="https://img.shields.io/badge/dynamic/json?color=blue&label=docs&prefix=v&query=%24.info.version&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fdataprep%2Fjson&style=flat-square"/></a>
+  <a href="https://pypi.org/project/dataprep/"><img src="https://img.shields.io/pypi/pyversions/dataprep?style=flat-square"/></a>
+  <a href="https://www.codacy.com/gh/sfu-db/dataprep?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sfu-db/dataprep&amp;utm_campaign=Badge_Coverage"><img src="https://app.codacy.com/project/badge/Coverage/ed658f08dcce4f088c850253475540ba"/></a>
+<!--   <a href="https://codecov.io/gh/sfu-db/dataprep"><img src="https://img.shields.io/codecov/c/github/sfu-db/dataprep?style=flat-square"/></a> -->
+  <a href="https://www.codacy.com/gh/sfu-db/dataprep?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sfu-db/dataprep&amp;utm_campaign=Badge_Grade"><img src="https://app.codacy.com/project/badge/Grade/ed658f08dcce4f088c850253475540ba"/></a>
+</p>
+<p align="center">
+  <a href="https://sfu-db.github.io/dataprep/">Documentation</a>
+  | 
+  <a href="https://github.com/sfu-db/dataprep/discussions">Forum</a>
+</p>
+DataPrep lets you prepare your data using a single library with a few lines of code.
+Currently, you can use DataPrep to:
+- Collect data from common data sources (through [`dataprep.connector`](#connector))
+- Do your exploratory data analysis (through [`dataprep.eda`](#eda))
+- Clean and standardize data (through [`dataprep.clean`](#clean))
+- ...more modules are coming
+## Releases
+<div align="center">
+  <table>
+    <tr>
+      <th>Repo</th>
+      <th>Version</th>
+      <th>Downloads</th>
+    </tr>
+    <tr>
+      <td>PyPI</td>
+      <td><a href="https://pypi.org/project/dataprep/"><img src="https://img.shields.io/pypi/v/dataprep?style=flat-square"/></a></td>
+      <td><a href="https://pepy.tech/project/dataprep"><img src="https://pepy.tech/badge/dataprep"/></a></td>
+    </tr>
+    <tr> 
+      <td>conda-forge</td>
+      <td><a href="https://anaconda.org/conda-forge/dataprep"><img src="https://img.shields.io/conda/vn/conda-forge/dataprep.svg"/></a></td>
+      <td><a href="https://anaconda.org/conda-forge/dataprep"><img src="https://img.shields.io/conda/dn/conda-forge/dataprep.svg"/></a></td>
+    </tr>
+  </table>
+</div>
+## Installation
+```bash
+pip install -U dataprep
+```
+## EDA
+DataPrep.EDA is the fastest and the easiest EDA (Exploratory Data Analysis) tool in Python. It allows you to understand a Pandas/Dask DataFrame with a few lines of code in seconds.
+#### Create Profile Reports, Fast
+You can create a beautiful profile report from a Pandas/Dask DataFrame with the `create_report` function. DataPrep.EDA has the following advantages compared to other tools:
+- **[10X Faster](https://arxiv.org/abs/2104.00841)**: DataPrep.EDA can be 10X faster than Pandas-based profiling tools due to its highly optimized Dask-based computing module.
+- **Interactive Visualization**: DataPrep.EDA generates interactive visualizations in a report, which makes the report look more appealing to end users.
+- **Big Data Support**: DataPrep.EDA naturally supports big data stored in a Dask cluster by accepting a Dask dataframe as input.
+The following code demonstrates how to use DataPrep.EDA to create a profile report for the titanic dataset.
+```python
+from dataprep.datasets import load_dataset
+from dataprep.eda import create_report
+df = load_dataset("titanic")
+create_report(df).show_browser()
+```
+Click [here](https://docs.dataprep.ai/_downloads/1a61c6aebb3ecbe9dc9742bd6ca78ddb/titanic_dp.html) to see the generated report of the above code.
+Click [here](https://docs.dataprep.ai/dev/bench/index.html) to see the benchmark result.
+#### Try DataPrep.EDA Online: [DataPrep.EDA Demo in Colab](https://colab.research.google.com/drive/1U_-pAMcne3hK1HbMB3kuEt-093Np_7Uk?usp=sharing)
+#### Innovative System Design
+DataPrep.EDA is the **_only_** task-centric EDA system in Python. It is carefully designed to improve usability.
+- **Task-Centric API Design**: You can declaratively specify a wide range of EDA tasks in different granularity with a single function call. All needed visualizations will be automatically and intelligently generated for you.
+- **Auto-Insights**: DataPrep.EDA automatically detects and highlights the insights (e.g., a column has many outliers) to facilitate pattern discovery about the data.
+- **How-to Guide**: A how-to guide is provided to show the configuration of each plot function. With this feature, you can easily customize the generated visualizations.
+#### Learn DataPrep.EDA in 2 minutes:
+<a href="https://youtu.be/nSkQy3ew3EI"><img src="assets/eda_video_cover.png"/></a>
+Click [here](https://sfu-db.github.io/dataprep/user_guide/eda/introduction.html) to check all the supported tasks.
+Check [plot](https://sfu-db.github.io/dataprep/user_guide/eda/plot.html), [plot_correlation](https://sfu-db.github.io/dataprep/user_guide/eda/plot_correlation.html), [plot_missing](https://sfu-db.github.io/dataprep/user_guide/eda/plot_missing.html) and [create_report](https://sfu-db.github.io/dataprep/user_guide/eda/create_report.html) to see how each function works.
+## Clean
+DataPrep.Clean contains about **140+** functions designed for cleaning and validating data in a DataFrame. It provides
+- **A Convenient GUI**: incorporated into Jupyter Notebook, users can clean their own DataFrame without any coding (see the video below).
+- **A Unified API**: each function follows the syntax `clean_{type}(df, 'column name')` (see an example below).
+- **Speed**: the computations are parallelized using Dask. It can clean **50K rows per second** on a dual-core laptop (that means cleaning 1 million rows in only 20 seconds).
+- **Transparency**: a report is generated that summarizes the alterations to the data that occured during cleaning.
+The following video shows how to use GUI of Dataprep.Clean
+<a href="https://youtu.be/WtJaVBIVoxQ"><img src="assets/clean_video_cover.png"/></a>
+The following example shows how to clean and standardize a column of country names.
+```python
+from dataprep.clean import clean_country
+import pandas as pd
+df = pd.DataFrame({'country': ['USA', 'country: Canada', '233', ' tr ', 'NA']})
+df2 = clean_country(df, 'country')
+df2
+           country  country_clean
+0              USA  United States
+1  country: Canada         Canada
+2              233        Estonia
+3              tr          Turkey
+4               NA            NaN
+```
+Type validation is also supported:
+```python
+from dataprep.clean import validate_country
+series = validate_country(df['country'])
+series
+0     True
+1    False
+2     True
+3     True
+4    False
+Name: country, dtype: bool
+```
+Check [Documentation of Dataprep.Clean](https://docs.dataprep.ai/user_guide/clean/introduction.html) to see how each function works.
+## Connector
+Connector now supports loading data from both web API and databases.
+### Web API
+Connector is an intuitive, open-source API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow.
+Connector provides a simple wrapper to collect structured data from different Web APIs (e.g., Twitter, Spotify), making web data collection easy and efficient, without requiring advanced programming skills.
+Do you want to leverage the growing number of websites that are opening their data through public APIs? Connector is for you!
+Let's check out the several benefits that Connector offers:
+- **A unified API:** You can fetch data using one or two lines of code to get data from [tens of popular websites](https://github.com/sfu-db/DataConnectorConfigs).
+- **Auto Pagination:** Do you want to invoke a Web API that could return a large result set and need to handle it through pagination? Connector automatically does the pagination for you! Just specify the desired number of returned results (argument `_count`) without getting into unnecessary detail about a specific pagination scheme.
+- **Speed:** Do you want to fetch results more quickly by making concurrent requests to Web APIs? Through the `_concurrency` argument, Connector simplifies concurrency, issuing API requests in parallel while respecting the API's rate limit policy.
+#### How to fetch all publications of Andrew Y. Ng?
+```python
+from dataprep.connector import connect
+conn_dblp = connect("dblp", _concurrency = 5)
+df = await conn_dblp.query("publication", author = "Andrew Y. Ng", _count = 2000)
+```
+Here, you can find detailed [Examples.](https://github.com/sfu-db/dataprep/tree/develop/examples)
+Connector is designed to be easy to extend. If you want to connect with your own web API, you just have to write a simple [configuration file](https://github.com/sfu-db/DataConnectorConfigs/blob/develop/CONTRIBUTING.md#add-configuration-files) to support it. This configuration file describes the API's main attributes like the URL, query parameters, authorization method, pagination properties, etc.
+### Database
+Connector now has adopted [connectorx](https://github.com/sfu-db/connector-x) in order to enable loading data from databases (Postgres, Mysql, SQLServer, etc.) into Python dataframes (pandas, dask, modin, arrow, polars) in the fastest and most memory efficient way. [[Benchmark]](https://github.com/sfu-db/connector-x/blob/main/Benchmark.md#benchmark-result-on-aws-r54xlarge)
+What you need to do is just install `connectorx` (`pip install -U connectorx`) and run one line of code:
+```python
+from dataprep.connector import read_sql
+read_sql("postgresql://username:password@server:port/database", "SELECT * FROM lineitem")
+```
+Check out [here](https://github.com/sfu-db/connector-x#supported-sources--destinations) for supported databases and dataframes and more examples usages.
+## Documentation
+The following documentation can give you an impression of what DataPrep can do:
+- [Connector](https://docs.dataprep.ai/user_guide/connector/introduction.html)
+- [EDA](https://docs.dataprep.ai/user_guide/eda/introduction.html)
+- [Clean](https://docs.dataprep.ai/user_guide/clean/introduction.html)
+## Contribute
+There are many ways to contribute to DataPrep.
+- Submit bugs and help us verify fixes as they are checked in.
+- Review the source code changes.
+- Engage with other DataPrep users and developers on StackOverflow.
+- Ask questions & propose new ideas in our [Forum].
+- [![Twitter]](https://twitter.com/dataprepai)
+- Contribute bug fixes.
+- Providing use cases and writing down your user experience.
+Please take a look at our [wiki] for development documentations!
+[build status]: https://img.shields.io/circleci/build/github/sfu-db/dataprep/master?style=flat-square&token=f68e38757f5c98771f46d1c7e700f285a0b9784d
+[forum]: https://github.com/sfu-db/dataprep/discussions
+[wiki]: https://github.com/sfu-db/dataprep/wiki
+[examples]: https://github.com/sfu-db/dataprep/tree/master/examples
+[twitter]: https://img.shields.io/twitter/follow/dataprepai?style=social
+## Acknowledgement
+Some functionalities of DataPrep are inspired by the following packages.
+- [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling)
+  Inspired the report functionality and insights provided in `dataprep.eda`.
+- [missingno](https://github.com/ResidentMario/missingno)
+  Inspired the missing value analysis in `dataprep.eda`.
+## Citing DataPrep
+If you use DataPrep, please consider citing the following paper:
+Jinglin Peng, Weiyuan Wu, Brandon Lockhart, Song Bian, Jing Nathan Yan, Linghao Xu, Zhixuan Chi, Jeffrey M. Rzeszotarski, and Jiannan Wang. [DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical
+Modeling in Python.](https://arxiv.org/abs/2104.00841) _SIGMOD 2021_.
+BibTeX entry:
+```bibtex
+@inproceedings{dataprepeda2021,
+  author    = {Jinglin Peng and Weiyuan Wu and Brandon Lockhart and Song Bian and Jing Nathan Yan and Linghao Xu and Zhixuan Chi and Jeffrey M. Rzeszotarski and Jiannan Wang},
+  title     = {DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python},
+  booktitle = {Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China},
+  year      = {2021}
+}
+```
+
+%package -n python3-dataprep
+Summary:	Dataprep: Data Preparation in Python
+Provides:	python-dataprep
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-dataprep
+<p align="center">
+  <a href="LICENSE"><img src="https://img.shields.io/pypi/l/dataprep?style=flat-square"/></a>
+  <a href="https://sfu-db.github.io/dataprep/"><img src="https://img.shields.io/badge/dynamic/json?color=blue&label=docs&prefix=v&query=%24.info.version&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fdataprep%2Fjson&style=flat-square"/></a>
+  <a href="https://pypi.org/project/dataprep/"><img src="https://img.shields.io/pypi/pyversions/dataprep?style=flat-square"/></a>
+  <a href="https://www.codacy.com/gh/sfu-db/dataprep?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sfu-db/dataprep&amp;utm_campaign=Badge_Coverage"><img src="https://app.codacy.com/project/badge/Coverage/ed658f08dcce4f088c850253475540ba"/></a>
+<!--   <a href="https://codecov.io/gh/sfu-db/dataprep"><img src="https://img.shields.io/codecov/c/github/sfu-db/dataprep?style=flat-square"/></a> -->
+  <a href="https://www.codacy.com/gh/sfu-db/dataprep?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sfu-db/dataprep&amp;utm_campaign=Badge_Grade"><img src="https://app.codacy.com/project/badge/Grade/ed658f08dcce4f088c850253475540ba"/></a>
+</p>
+<p align="center">
+  <a href="https://sfu-db.github.io/dataprep/">Documentation</a>
+  | 
+  <a href="https://github.com/sfu-db/dataprep/discussions">Forum</a>
+</p>
+DataPrep lets you prepare your data using a single library with a few lines of code.
+Currently, you can use DataPrep to:
+- Collect data from common data sources (through [`dataprep.connector`](#connector))
+- Do your exploratory data analysis (through [`dataprep.eda`](#eda))
+- Clean and standardize data (through [`dataprep.clean`](#clean))
+- ...more modules are coming
+## Releases
+<div align="center">
+  <table>
+    <tr>
+      <th>Repo</th>
+      <th>Version</th>
+      <th>Downloads</th>
+    </tr>
+    <tr>
+      <td>PyPI</td>
+      <td><a href="https://pypi.org/project/dataprep/"><img src="https://img.shields.io/pypi/v/dataprep?style=flat-square"/></a></td>
+      <td><a href="https://pepy.tech/project/dataprep"><img src="https://pepy.tech/badge/dataprep"/></a></td>
+    </tr>
+    <tr> 
+      <td>conda-forge</td>
+      <td><a href="https://anaconda.org/conda-forge/dataprep"><img src="https://img.shields.io/conda/vn/conda-forge/dataprep.svg"/></a></td>
+      <td><a href="https://anaconda.org/conda-forge/dataprep"><img src="https://img.shields.io/conda/dn/conda-forge/dataprep.svg"/></a></td>
+    </tr>
+  </table>
+</div>
+## Installation
+```bash
+pip install -U dataprep
+```
+## EDA
+DataPrep.EDA is the fastest and the easiest EDA (Exploratory Data Analysis) tool in Python. It allows you to understand a Pandas/Dask DataFrame with a few lines of code in seconds.
+#### Create Profile Reports, Fast
+You can create a beautiful profile report from a Pandas/Dask DataFrame with the `create_report` function. DataPrep.EDA has the following advantages compared to other tools:
+- **[10X Faster](https://arxiv.org/abs/2104.00841)**: DataPrep.EDA can be 10X faster than Pandas-based profiling tools due to its highly optimized Dask-based computing module.
+- **Interactive Visualization**: DataPrep.EDA generates interactive visualizations in a report, which makes the report look more appealing to end users.
+- **Big Data Support**: DataPrep.EDA naturally supports big data stored in a Dask cluster by accepting a Dask dataframe as input.
+The following code demonstrates how to use DataPrep.EDA to create a profile report for the titanic dataset.
+```python
+from dataprep.datasets import load_dataset
+from dataprep.eda import create_report
+df = load_dataset("titanic")
+create_report(df).show_browser()
+```
+Click [here](https://docs.dataprep.ai/_downloads/1a61c6aebb3ecbe9dc9742bd6ca78ddb/titanic_dp.html) to see the generated report of the above code.
+Click [here](https://docs.dataprep.ai/dev/bench/index.html) to see the benchmark result.
+#### Try DataPrep.EDA Online: [DataPrep.EDA Demo in Colab](https://colab.research.google.com/drive/1U_-pAMcne3hK1HbMB3kuEt-093Np_7Uk?usp=sharing)
+#### Innovative System Design
+DataPrep.EDA is the **_only_** task-centric EDA system in Python. It is carefully designed to improve usability.
+- **Task-Centric API Design**: You can declaratively specify a wide range of EDA tasks in different granularity with a single function call. All needed visualizations will be automatically and intelligently generated for you.
+- **Auto-Insights**: DataPrep.EDA automatically detects and highlights the insights (e.g., a column has many outliers) to facilitate pattern discovery about the data.
+- **How-to Guide**: A how-to guide is provided to show the configuration of each plot function. With this feature, you can easily customize the generated visualizations.
+#### Learn DataPrep.EDA in 2 minutes:
+<a href="https://youtu.be/nSkQy3ew3EI"><img src="assets/eda_video_cover.png"/></a>
+Click [here](https://sfu-db.github.io/dataprep/user_guide/eda/introduction.html) to check all the supported tasks.
+Check [plot](https://sfu-db.github.io/dataprep/user_guide/eda/plot.html), [plot_correlation](https://sfu-db.github.io/dataprep/user_guide/eda/plot_correlation.html), [plot_missing](https://sfu-db.github.io/dataprep/user_guide/eda/plot_missing.html) and [create_report](https://sfu-db.github.io/dataprep/user_guide/eda/create_report.html) to see how each function works.
+## Clean
+DataPrep.Clean contains about **140+** functions designed for cleaning and validating data in a DataFrame. It provides
+- **A Convenient GUI**: incorporated into Jupyter Notebook, users can clean their own DataFrame without any coding (see the video below).
+- **A Unified API**: each function follows the syntax `clean_{type}(df, 'column name')` (see an example below).
+- **Speed**: the computations are parallelized using Dask. It can clean **50K rows per second** on a dual-core laptop (that means cleaning 1 million rows in only 20 seconds).
+- **Transparency**: a report is generated that summarizes the alterations to the data that occured during cleaning.
+The following video shows how to use GUI of Dataprep.Clean
+<a href="https://youtu.be/WtJaVBIVoxQ"><img src="assets/clean_video_cover.png"/></a>
+The following example shows how to clean and standardize a column of country names.
+```python
+from dataprep.clean import clean_country
+import pandas as pd
+df = pd.DataFrame({'country': ['USA', 'country: Canada', '233', ' tr ', 'NA']})
+df2 = clean_country(df, 'country')
+df2
+           country  country_clean
+0              USA  United States
+1  country: Canada         Canada
+2              233        Estonia
+3              tr          Turkey
+4               NA            NaN
+```
+Type validation is also supported:
+```python
+from dataprep.clean import validate_country
+series = validate_country(df['country'])
+series
+0     True
+1    False
+2     True
+3     True
+4    False
+Name: country, dtype: bool
+```
+Check [Documentation of Dataprep.Clean](https://docs.dataprep.ai/user_guide/clean/introduction.html) to see how each function works.
+## Connector
+Connector now supports loading data from both web API and databases.
+### Web API
+Connector is an intuitive, open-source API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow.
+Connector provides a simple wrapper to collect structured data from different Web APIs (e.g., Twitter, Spotify), making web data collection easy and efficient, without requiring advanced programming skills.
+Do you want to leverage the growing number of websites that are opening their data through public APIs? Connector is for you!
+Let's check out the several benefits that Connector offers:
+- **A unified API:** You can fetch data using one or two lines of code to get data from [tens of popular websites](https://github.com/sfu-db/DataConnectorConfigs).
+- **Auto Pagination:** Do you want to invoke a Web API that could return a large result set and need to handle it through pagination? Connector automatically does the pagination for you! Just specify the desired number of returned results (argument `_count`) without getting into unnecessary detail about a specific pagination scheme.
+- **Speed:** Do you want to fetch results more quickly by making concurrent requests to Web APIs? Through the `_concurrency` argument, Connector simplifies concurrency, issuing API requests in parallel while respecting the API's rate limit policy.
+#### How to fetch all publications of Andrew Y. Ng?
+```python
+from dataprep.connector import connect
+conn_dblp = connect("dblp", _concurrency = 5)
+df = await conn_dblp.query("publication", author = "Andrew Y. Ng", _count = 2000)
+```
+Here, you can find detailed [Examples.](https://github.com/sfu-db/dataprep/tree/develop/examples)
+Connector is designed to be easy to extend. If you want to connect with your own web API, you just have to write a simple [configuration file](https://github.com/sfu-db/DataConnectorConfigs/blob/develop/CONTRIBUTING.md#add-configuration-files) to support it. This configuration file describes the API's main attributes like the URL, query parameters, authorization method, pagination properties, etc.
+### Database
+Connector now has adopted [connectorx](https://github.com/sfu-db/connector-x) in order to enable loading data from databases (Postgres, Mysql, SQLServer, etc.) into Python dataframes (pandas, dask, modin, arrow, polars) in the fastest and most memory efficient way. [[Benchmark]](https://github.com/sfu-db/connector-x/blob/main/Benchmark.md#benchmark-result-on-aws-r54xlarge)
+What you need to do is just install `connectorx` (`pip install -U connectorx`) and run one line of code:
+```python
+from dataprep.connector import read_sql
+read_sql("postgresql://username:password@server:port/database", "SELECT * FROM lineitem")
+```
+Check out [here](https://github.com/sfu-db/connector-x#supported-sources--destinations) for supported databases and dataframes and more examples usages.
+## Documentation
+The following documentation can give you an impression of what DataPrep can do:
+- [Connector](https://docs.dataprep.ai/user_guide/connector/introduction.html)
+- [EDA](https://docs.dataprep.ai/user_guide/eda/introduction.html)
+- [Clean](https://docs.dataprep.ai/user_guide/clean/introduction.html)
+## Contribute
+There are many ways to contribute to DataPrep.
+- Submit bugs and help us verify fixes as they are checked in.
+- Review the source code changes.
+- Engage with other DataPrep users and developers on StackOverflow.
+- Ask questions & propose new ideas in our [Forum].
+- [![Twitter]](https://twitter.com/dataprepai)
+- Contribute bug fixes.
+- Providing use cases and writing down your user experience.
+Please take a look at our [wiki] for development documentations!
+[build status]: https://img.shields.io/circleci/build/github/sfu-db/dataprep/master?style=flat-square&token=f68e38757f5c98771f46d1c7e700f285a0b9784d
+[forum]: https://github.com/sfu-db/dataprep/discussions
+[wiki]: https://github.com/sfu-db/dataprep/wiki
+[examples]: https://github.com/sfu-db/dataprep/tree/master/examples
+[twitter]: https://img.shields.io/twitter/follow/dataprepai?style=social
+## Acknowledgement
+Some functionalities of DataPrep are inspired by the following packages.
+- [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling)
+  Inspired the report functionality and insights provided in `dataprep.eda`.
+- [missingno](https://github.com/ResidentMario/missingno)
+  Inspired the missing value analysis in `dataprep.eda`.
+## Citing DataPrep
+If you use DataPrep, please consider citing the following paper:
+Jinglin Peng, Weiyuan Wu, Brandon Lockhart, Song Bian, Jing Nathan Yan, Linghao Xu, Zhixuan Chi, Jeffrey M. Rzeszotarski, and Jiannan Wang. [DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical
+Modeling in Python.](https://arxiv.org/abs/2104.00841) _SIGMOD 2021_.
+BibTeX entry:
+```bibtex
+@inproceedings{dataprepeda2021,
+  author    = {Jinglin Peng and Weiyuan Wu and Brandon Lockhart and Song Bian and Jing Nathan Yan and Linghao Xu and Zhixuan Chi and Jeffrey M. Rzeszotarski and Jiannan Wang},
+  title     = {DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python},
+  booktitle = {Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China},
+  year      = {2021}
+}
+```
+
+%package help
+Summary:	Development documents and examples for dataprep
+Provides:	python3-dataprep-doc
+%description help
+<p align="center">
+  <a href="LICENSE"><img src="https://img.shields.io/pypi/l/dataprep?style=flat-square"/></a>
+  <a href="https://sfu-db.github.io/dataprep/"><img src="https://img.shields.io/badge/dynamic/json?color=blue&label=docs&prefix=v&query=%24.info.version&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fdataprep%2Fjson&style=flat-square"/></a>
+  <a href="https://pypi.org/project/dataprep/"><img src="https://img.shields.io/pypi/pyversions/dataprep?style=flat-square"/></a>
+  <a href="https://www.codacy.com/gh/sfu-db/dataprep?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sfu-db/dataprep&amp;utm_campaign=Badge_Coverage"><img src="https://app.codacy.com/project/badge/Coverage/ed658f08dcce4f088c850253475540ba"/></a>
+<!--   <a href="https://codecov.io/gh/sfu-db/dataprep"><img src="https://img.shields.io/codecov/c/github/sfu-db/dataprep?style=flat-square"/></a> -->
+  <a href="https://www.codacy.com/gh/sfu-db/dataprep?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sfu-db/dataprep&amp;utm_campaign=Badge_Grade"><img src="https://app.codacy.com/project/badge/Grade/ed658f08dcce4f088c850253475540ba"/></a>
+</p>
+<p align="center">
+  <a href="https://sfu-db.github.io/dataprep/">Documentation</a>
+  | 
+  <a href="https://github.com/sfu-db/dataprep/discussions">Forum</a>
+</p>
+DataPrep lets you prepare your data using a single library with a few lines of code.
+Currently, you can use DataPrep to:
+- Collect data from common data sources (through [`dataprep.connector`](#connector))
+- Do your exploratory data analysis (through [`dataprep.eda`](#eda))
+- Clean and standardize data (through [`dataprep.clean`](#clean))
+- ...more modules are coming
+## Releases
+<div align="center">
+  <table>
+    <tr>
+      <th>Repo</th>
+      <th>Version</th>
+      <th>Downloads</th>
+    </tr>
+    <tr>
+      <td>PyPI</td>
+      <td><a href="https://pypi.org/project/dataprep/"><img src="https://img.shields.io/pypi/v/dataprep?style=flat-square"/></a></td>
+      <td><a href="https://pepy.tech/project/dataprep"><img src="https://pepy.tech/badge/dataprep"/></a></td>
+    </tr>
+    <tr> 
+      <td>conda-forge</td>
+      <td><a href="https://anaconda.org/conda-forge/dataprep"><img src="https://img.shields.io/conda/vn/conda-forge/dataprep.svg"/></a></td>
+      <td><a href="https://anaconda.org/conda-forge/dataprep"><img src="https://img.shields.io/conda/dn/conda-forge/dataprep.svg"/></a></td>
+    </tr>
+  </table>
+</div>
+## Installation
+```bash
+pip install -U dataprep
+```
+## EDA
+DataPrep.EDA is the fastest and the easiest EDA (Exploratory Data Analysis) tool in Python. It allows you to understand a Pandas/Dask DataFrame with a few lines of code in seconds.
+#### Create Profile Reports, Fast
+You can create a beautiful profile report from a Pandas/Dask DataFrame with the `create_report` function. DataPrep.EDA has the following advantages compared to other tools:
+- **[10X Faster](https://arxiv.org/abs/2104.00841)**: DataPrep.EDA can be 10X faster than Pandas-based profiling tools due to its highly optimized Dask-based computing module.
+- **Interactive Visualization**: DataPrep.EDA generates interactive visualizations in a report, which makes the report look more appealing to end users.
+- **Big Data Support**: DataPrep.EDA naturally supports big data stored in a Dask cluster by accepting a Dask dataframe as input.
+The following code demonstrates how to use DataPrep.EDA to create a profile report for the titanic dataset.
+```python
+from dataprep.datasets import load_dataset
+from dataprep.eda import create_report
+df = load_dataset("titanic")
+create_report(df).show_browser()
+```
+Click [here](https://docs.dataprep.ai/_downloads/1a61c6aebb3ecbe9dc9742bd6ca78ddb/titanic_dp.html) to see the generated report of the above code.
+Click [here](https://docs.dataprep.ai/dev/bench/index.html) to see the benchmark result.
+#### Try DataPrep.EDA Online: [DataPrep.EDA Demo in Colab](https://colab.research.google.com/drive/1U_-pAMcne3hK1HbMB3kuEt-093Np_7Uk?usp=sharing)
+#### Innovative System Design
+DataPrep.EDA is the **_only_** task-centric EDA system in Python. It is carefully designed to improve usability.
+- **Task-Centric API Design**: You can declaratively specify a wide range of EDA tasks in different granularity with a single function call. All needed visualizations will be automatically and intelligently generated for you.
+- **Auto-Insights**: DataPrep.EDA automatically detects and highlights the insights (e.g., a column has many outliers) to facilitate pattern discovery about the data.
+- **How-to Guide**: A how-to guide is provided to show the configuration of each plot function. With this feature, you can easily customize the generated visualizations.
+#### Learn DataPrep.EDA in 2 minutes:
+<a href="https://youtu.be/nSkQy3ew3EI"><img src="assets/eda_video_cover.png"/></a>
+Click [here](https://sfu-db.github.io/dataprep/user_guide/eda/introduction.html) to check all the supported tasks.
+Check [plot](https://sfu-db.github.io/dataprep/user_guide/eda/plot.html), [plot_correlation](https://sfu-db.github.io/dataprep/user_guide/eda/plot_correlation.html), [plot_missing](https://sfu-db.github.io/dataprep/user_guide/eda/plot_missing.html) and [create_report](https://sfu-db.github.io/dataprep/user_guide/eda/create_report.html) to see how each function works.
+## Clean
+DataPrep.Clean contains about **140+** functions designed for cleaning and validating data in a DataFrame. It provides
+- **A Convenient GUI**: incorporated into Jupyter Notebook, users can clean their own DataFrame without any coding (see the video below).
+- **A Unified API**: each function follows the syntax `clean_{type}(df, 'column name')` (see an example below).
+- **Speed**: the computations are parallelized using Dask. It can clean **50K rows per second** on a dual-core laptop (that means cleaning 1 million rows in only 20 seconds).
+- **Transparency**: a report is generated that summarizes the alterations to the data that occured during cleaning.
+The following video shows how to use GUI of Dataprep.Clean
+<a href="https://youtu.be/WtJaVBIVoxQ"><img src="assets/clean_video_cover.png"/></a>
+The following example shows how to clean and standardize a column of country names.
+```python
+from dataprep.clean import clean_country
+import pandas as pd
+df = pd.DataFrame({'country': ['USA', 'country: Canada', '233', ' tr ', 'NA']})
+df2 = clean_country(df, 'country')
+df2
+           country  country_clean
+0              USA  United States
+1  country: Canada         Canada
+2              233        Estonia
+3              tr          Turkey
+4               NA            NaN
+```
+Type validation is also supported:
+```python
+from dataprep.clean import validate_country
+series = validate_country(df['country'])
+series
+0     True
+1    False
+2     True
+3     True
+4    False
+Name: country, dtype: bool
+```
+Check [Documentation of Dataprep.Clean](https://docs.dataprep.ai/user_guide/clean/introduction.html) to see how each function works.
+## Connector
+Connector now supports loading data from both web API and databases.
+### Web API
+Connector is an intuitive, open-source API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow.
+Connector provides a simple wrapper to collect structured data from different Web APIs (e.g., Twitter, Spotify), making web data collection easy and efficient, without requiring advanced programming skills.
+Do you want to leverage the growing number of websites that are opening their data through public APIs? Connector is for you!
+Let's check out the several benefits that Connector offers:
+- **A unified API:** You can fetch data using one or two lines of code to get data from [tens of popular websites](https://github.com/sfu-db/DataConnectorConfigs).
+- **Auto Pagination:** Do you want to invoke a Web API that could return a large result set and need to handle it through pagination? Connector automatically does the pagination for you! Just specify the desired number of returned results (argument `_count`) without getting into unnecessary detail about a specific pagination scheme.
+- **Speed:** Do you want to fetch results more quickly by making concurrent requests to Web APIs? Through the `_concurrency` argument, Connector simplifies concurrency, issuing API requests in parallel while respecting the API's rate limit policy.
+#### How to fetch all publications of Andrew Y. Ng?
+```python
+from dataprep.connector import connect
+conn_dblp = connect("dblp", _concurrency = 5)
+df = await conn_dblp.query("publication", author = "Andrew Y. Ng", _count = 2000)
+```
+Here, you can find detailed [Examples.](https://github.com/sfu-db/dataprep/tree/develop/examples)
+Connector is designed to be easy to extend. If you want to connect with your own web API, you just have to write a simple [configuration file](https://github.com/sfu-db/DataConnectorConfigs/blob/develop/CONTRIBUTING.md#add-configuration-files) to support it. This configuration file describes the API's main attributes like the URL, query parameters, authorization method, pagination properties, etc.
+### Database
+Connector now has adopted [connectorx](https://github.com/sfu-db/connector-x) in order to enable loading data from databases (Postgres, Mysql, SQLServer, etc.) into Python dataframes (pandas, dask, modin, arrow, polars) in the fastest and most memory efficient way. [[Benchmark]](https://github.com/sfu-db/connector-x/blob/main/Benchmark.md#benchmark-result-on-aws-r54xlarge)
+What you need to do is just install `connectorx` (`pip install -U connectorx`) and run one line of code:
+```python
+from dataprep.connector import read_sql
+read_sql("postgresql://username:password@server:port/database", "SELECT * FROM lineitem")
+```
+Check out [here](https://github.com/sfu-db/connector-x#supported-sources--destinations) for supported databases and dataframes and more examples usages.
+## Documentation
+The following documentation can give you an impression of what DataPrep can do:
+- [Connector](https://docs.dataprep.ai/user_guide/connector/introduction.html)
+- [EDA](https://docs.dataprep.ai/user_guide/eda/introduction.html)
+- [Clean](https://docs.dataprep.ai/user_guide/clean/introduction.html)
+## Contribute
+There are many ways to contribute to DataPrep.
+- Submit bugs and help us verify fixes as they are checked in.
+- Review the source code changes.
+- Engage with other DataPrep users and developers on StackOverflow.
+- Ask questions & propose new ideas in our [Forum].
+- [![Twitter]](https://twitter.com/dataprepai)
+- Contribute bug fixes.
+- Providing use cases and writing down your user experience.
+Please take a look at our [wiki] for development documentations!
+[build status]: https://img.shields.io/circleci/build/github/sfu-db/dataprep/master?style=flat-square&token=f68e38757f5c98771f46d1c7e700f285a0b9784d
+[forum]: https://github.com/sfu-db/dataprep/discussions
+[wiki]: https://github.com/sfu-db/dataprep/wiki
+[examples]: https://github.com/sfu-db/dataprep/tree/master/examples
+[twitter]: https://img.shields.io/twitter/follow/dataprepai?style=social
+## Acknowledgement
+Some functionalities of DataPrep are inspired by the following packages.
+- [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling)
+  Inspired the report functionality and insights provided in `dataprep.eda`.
+- [missingno](https://github.com/ResidentMario/missingno)
+  Inspired the missing value analysis in `dataprep.eda`.
+## Citing DataPrep
+If you use DataPrep, please consider citing the following paper:
+Jinglin Peng, Weiyuan Wu, Brandon Lockhart, Song Bian, Jing Nathan Yan, Linghao Xu, Zhixuan Chi, Jeffrey M. Rzeszotarski, and Jiannan Wang. [DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical
+Modeling in Python.](https://arxiv.org/abs/2104.00841) _SIGMOD 2021_.
+BibTeX entry:
+```bibtex
+@inproceedings{dataprepeda2021,
+  author    = {Jinglin Peng and Weiyuan Wu and Brandon Lockhart and Song Bian and Jing Nathan Yan and Linghao Xu and Zhixuan Chi and Jeffrey M. Rzeszotarski and Jiannan Wang},
+  title     = {DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python},
+  booktitle = {Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China},
+  year      = {2021}
+}
+```
+
+%prep
+%autosetup -n dataprep-0.4.5
+
+%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-dataprep -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.5-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..f26d904
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+84fbe60aa56059f8f25ffad8b893538c  dataprep-0.4.5.tar.gz