automatic import of python-bcpy

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+/bcpy-0.1.8.tar.gz
diff --git a/python-bcpy.spec b/python-bcpy.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-bcpy
+Version:	0.1.8
+Release:	1
+Summary:	Microsoft SQL Server bcp (Bulk Copy) wrapper
+License:	MIT License
+URL:		https://github.com/titan550/bcpy
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f5/55/2ce7ad290d4907cd420424a8e685c2b39c453cebda3aa0e549a424fddf9d/bcpy-0.1.8.tar.gz
+BuildArch:	noarch
+
+
+%description
+# bcpy
+
+<table>
+<tr>
+  <td>Latest Release</td>
+  <td>
+    <a href="https://pypi.org/project/bcpy/">
+    <img src="https://img.shields.io/pypi/v/bcpy.svg" alt="latest release" />
+    </a>
+  </td>
+</tr>
+<tr>
+  <td>License</td>
+  <td>
+    <a href="https://github.com/titan550/bcpy/blob/master/LICENSE">
+    <img src="https://img.shields.io/pypi/l/bcpy.svg" alt="license" />
+    </a>
+</td>
+</tr>
+<tr>
+  <td>Build Status (master)</td>
+  <td>
+    <a href="https://travis-ci.org/titan550/bcpy">
+    <img src="https://api.travis-ci.org/titan550/bcpy.svg?branch=master" alt="travis build status" />
+    </a>
+  </td>
+</tr>
+</table>
+
+## What is it?
+
+This package is a wrapper for Microsoft's SQL Server bcp utility. Current database drivers available in Python are not fast enough for transferring millions of records (yes, I have tried [pyodbc fast_execute_many](https://github.com/mkleehammer/pyodbc/wiki/Features-beyond-the-DB-API#fast_executemany)). Despite the IO hits, the fastest option by far is saving the data to a CSV file in file system (preferably /dev/shm tmpfs) and using the bcp utility to transfer the CSV file to SQL Server.
+
+## How Can I Install It?
+
+1. Make sure your computeer has the [requirements](#requirements).
+1. You can download and install this package from PyPI repository by running the command below.
+
+```bash
+pip install bcpy
+```
+
+## Examples
+
+Following examples show you how to load (1) flat files and (2) DataFrame objects to SQL Server using this package.
+
+### Flat File
+
+Following example assumes that you have a comma separated file with no qualifier in path 'tests/data1.csv'. The code below sends the the file to SQL Server.
+
+```python
+import bcpy
+
+
+sql_config = {
+    'server': 'sql_server_hostname',
+    'database': 'database_name',
+    'username': 'test_user',
+    'password': 'test_user_password1234'
+}
+sql_table_name = 'test_data1'
+csv_file_path = 'tests/data1.csv'
+flat_file = bcpy.FlatFile(qualifier='', path=csv_file_path)
+sql_table = bcpy.SqlTable(sql_config, table=sql_table_name)
+flat_file.to_sql(sql_table)
+```
+
+### DataFrame
+
+The following example creates a DataFrame with 100 rows and 4 columns populated with random data and then it sends it to SQL Server.
+
+```python
+import bcpy
+import numpy as np
+import pandas as pd
+
+
+sql_config = {
+    'server': 'sql_server_hostname',
+    'database': 'database_name',
+    'username': 'test_user',
+    'password': 'test_user_password1234'
+}
+table_name = 'test_dataframe'
+df = pd.DataFrame(np.random.randint(-100, 100, size=(100, 4)),
+                  columns=list('ABCD'))
+bdf = bcpy.DataFrame(df)
+sql_table = bcpy.SqlTable(sql_config, table=table_name)
+bdf.to_sql(sql_table)
+```
+
+## Requirements
+
+You need a working version of Microsoft bcp installed in your system. Your PATH environment variable should contain the directory of the bcp utility. Following are the installation tutorials for different operating systems.
+
+- [Dockerfile (Ubuntu 18.04)](./bcp.Dockerfile)
+- [Linux](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools)
+- [Mac](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools?view=sql-server-2017#macos)
+- [Windows](https://docs.microsoft.com/en-us/sql/tools/bcp-utility)
+
+%package -n python3-bcpy
+Summary:	Microsoft SQL Server bcp (Bulk Copy) wrapper
+Provides:	python-bcpy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-bcpy
+# bcpy
+
+<table>
+<tr>
+  <td>Latest Release</td>
+  <td>
+    <a href="https://pypi.org/project/bcpy/">
+    <img src="https://img.shields.io/pypi/v/bcpy.svg" alt="latest release" />
+    </a>
+  </td>
+</tr>
+<tr>
+  <td>License</td>
+  <td>
+    <a href="https://github.com/titan550/bcpy/blob/master/LICENSE">
+    <img src="https://img.shields.io/pypi/l/bcpy.svg" alt="license" />
+    </a>
+</td>
+</tr>
+<tr>
+  <td>Build Status (master)</td>
+  <td>
+    <a href="https://travis-ci.org/titan550/bcpy">
+    <img src="https://api.travis-ci.org/titan550/bcpy.svg?branch=master" alt="travis build status" />
+    </a>
+  </td>
+</tr>
+</table>
+
+## What is it?
+
+This package is a wrapper for Microsoft's SQL Server bcp utility. Current database drivers available in Python are not fast enough for transferring millions of records (yes, I have tried [pyodbc fast_execute_many](https://github.com/mkleehammer/pyodbc/wiki/Features-beyond-the-DB-API#fast_executemany)). Despite the IO hits, the fastest option by far is saving the data to a CSV file in file system (preferably /dev/shm tmpfs) and using the bcp utility to transfer the CSV file to SQL Server.
+
+## How Can I Install It?
+
+1. Make sure your computeer has the [requirements](#requirements).
+1. You can download and install this package from PyPI repository by running the command below.
+
+```bash
+pip install bcpy
+```
+
+## Examples
+
+Following examples show you how to load (1) flat files and (2) DataFrame objects to SQL Server using this package.
+
+### Flat File
+
+Following example assumes that you have a comma separated file with no qualifier in path 'tests/data1.csv'. The code below sends the the file to SQL Server.
+
+```python
+import bcpy
+
+
+sql_config = {
+    'server': 'sql_server_hostname',
+    'database': 'database_name',
+    'username': 'test_user',
+    'password': 'test_user_password1234'
+}
+sql_table_name = 'test_data1'
+csv_file_path = 'tests/data1.csv'
+flat_file = bcpy.FlatFile(qualifier='', path=csv_file_path)
+sql_table = bcpy.SqlTable(sql_config, table=sql_table_name)
+flat_file.to_sql(sql_table)
+```
+
+### DataFrame
+
+The following example creates a DataFrame with 100 rows and 4 columns populated with random data and then it sends it to SQL Server.
+
+```python
+import bcpy
+import numpy as np
+import pandas as pd
+
+
+sql_config = {
+    'server': 'sql_server_hostname',
+    'database': 'database_name',
+    'username': 'test_user',
+    'password': 'test_user_password1234'
+}
+table_name = 'test_dataframe'
+df = pd.DataFrame(np.random.randint(-100, 100, size=(100, 4)),
+                  columns=list('ABCD'))
+bdf = bcpy.DataFrame(df)
+sql_table = bcpy.SqlTable(sql_config, table=table_name)
+bdf.to_sql(sql_table)
+```
+
+## Requirements
+
+You need a working version of Microsoft bcp installed in your system. Your PATH environment variable should contain the directory of the bcp utility. Following are the installation tutorials for different operating systems.
+
+- [Dockerfile (Ubuntu 18.04)](./bcp.Dockerfile)
+- [Linux](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools)
+- [Mac](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools?view=sql-server-2017#macos)
+- [Windows](https://docs.microsoft.com/en-us/sql/tools/bcp-utility)
+
+%package help
+Summary:	Development documents and examples for bcpy
+Provides:	python3-bcpy-doc
+%description help
+# bcpy
+
+<table>
+<tr>
+  <td>Latest Release</td>
+  <td>
+    <a href="https://pypi.org/project/bcpy/">
+    <img src="https://img.shields.io/pypi/v/bcpy.svg" alt="latest release" />
+    </a>
+  </td>
+</tr>
+<tr>
+  <td>License</td>
+  <td>
+    <a href="https://github.com/titan550/bcpy/blob/master/LICENSE">
+    <img src="https://img.shields.io/pypi/l/bcpy.svg" alt="license" />
+    </a>
+</td>
+</tr>
+<tr>
+  <td>Build Status (master)</td>
+  <td>
+    <a href="https://travis-ci.org/titan550/bcpy">
+    <img src="https://api.travis-ci.org/titan550/bcpy.svg?branch=master" alt="travis build status" />
+    </a>
+  </td>
+</tr>
+</table>
+
+## What is it?
+
+This package is a wrapper for Microsoft's SQL Server bcp utility. Current database drivers available in Python are not fast enough for transferring millions of records (yes, I have tried [pyodbc fast_execute_many](https://github.com/mkleehammer/pyodbc/wiki/Features-beyond-the-DB-API#fast_executemany)). Despite the IO hits, the fastest option by far is saving the data to a CSV file in file system (preferably /dev/shm tmpfs) and using the bcp utility to transfer the CSV file to SQL Server.
+
+## How Can I Install It?
+
+1. Make sure your computeer has the [requirements](#requirements).
+1. You can download and install this package from PyPI repository by running the command below.
+
+```bash
+pip install bcpy
+```
+
+## Examples
+
+Following examples show you how to load (1) flat files and (2) DataFrame objects to SQL Server using this package.
+
+### Flat File
+
+Following example assumes that you have a comma separated file with no qualifier in path 'tests/data1.csv'. The code below sends the the file to SQL Server.
+
+```python
+import bcpy
+
+
+sql_config = {
+    'server': 'sql_server_hostname',
+    'database': 'database_name',
+    'username': 'test_user',
+    'password': 'test_user_password1234'
+}
+sql_table_name = 'test_data1'
+csv_file_path = 'tests/data1.csv'
+flat_file = bcpy.FlatFile(qualifier='', path=csv_file_path)
+sql_table = bcpy.SqlTable(sql_config, table=sql_table_name)
+flat_file.to_sql(sql_table)
+```
+
+### DataFrame
+
+The following example creates a DataFrame with 100 rows and 4 columns populated with random data and then it sends it to SQL Server.
+
+```python
+import bcpy
+import numpy as np
+import pandas as pd
+
+
+sql_config = {
+    'server': 'sql_server_hostname',
+    'database': 'database_name',
+    'username': 'test_user',
+    'password': 'test_user_password1234'
+}
+table_name = 'test_dataframe'
+df = pd.DataFrame(np.random.randint(-100, 100, size=(100, 4)),
+                  columns=list('ABCD'))
+bdf = bcpy.DataFrame(df)
+sql_table = bcpy.SqlTable(sql_config, table=table_name)
+bdf.to_sql(sql_table)
+```
+
+## Requirements
+
+You need a working version of Microsoft bcp installed in your system. Your PATH environment variable should contain the directory of the bcp utility. Following are the installation tutorials for different operating systems.
+
+- [Dockerfile (Ubuntu 18.04)](./bcp.Dockerfile)
+- [Linux](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools)
+- [Mac](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools?view=sql-server-2017#macos)
+- [Windows](https://docs.microsoft.com/en-us/sql/tools/bcp-utility)
+
+%prep
+%autosetup -n bcpy-0.1.8
+
+%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-bcpy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.8-1
+- Package Spec generated
diff --git a/sources b/sources
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+8425eb306982ded5ba8e2f1b3c4fb0b8  bcpy-0.1.8.tar.gz