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        "author": "John Shojaei",
        "author_email": "titan550@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: SQL",
            "Topic :: Database"
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        "description": "# bcpy\n\n<table>\n<tr>\n  <td>Latest Release</td>\n  <td>\n    <a href=\"https://pypi.org/project/bcpy/\">\n    <img src=\"https://img.shields.io/pypi/v/bcpy.svg\" alt=\"latest release\" />\n    </a>\n  </td>\n</tr>\n<tr>\n  <td>License</td>\n  <td>\n    <a href=\"https://github.com/titan550/bcpy/blob/master/LICENSE\">\n    <img src=\"https://img.shields.io/pypi/l/bcpy.svg\" alt=\"license\" />\n    </a>\n</td>\n</tr>\n<tr>\n  <td>Build Status (master)</td>\n  <td>\n    <a href=\"https://travis-ci.org/titan550/bcpy\">\n    <img src=\"https://api.travis-ci.org/titan550/bcpy.svg?branch=master\" alt=\"travis build status\" />\n    </a>\n  </td>\n</tr>\n</table>\n\n## What is it?\n\nThis 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.\n\n## How Can I Install It?\n\nYou can download and install this package from PyPI repository by running the command below.\n\n```bash\npip install bcpy\n```\n\n## Examples\n\nFollowing examples show you how to load (1) flat files and (2) DataFrame objects to SQL Server using this package.\n\n### Flat File\n\nFollowing 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.\n\n```python\nimport bcpy\n\n\nsql_config = {\n    'server': 'sql_server_hostname',\n    'database': 'database_name',\n    'username': 'test_user',\n    'password': 'test_user_password1234'\n}\nsql_table_name = 'test_data1'\ncsv_file_path = 'tests/data1.csv'\nflat_file = bcpy.FlatFile(qualifier='', path=csv_file_path)\nsql_table = bcpy.SqlTable(sql_config, table=sql_table_name)\nflat_file.to_sql(sql_table)\n```\n\n### DataFrame\n\nThe following example creates a DataFrame with 100 rows and 4 columns populated with random data and then it sends it to SQL Server.\n\n```python\nimport bcpy\nimport numpy as np\nimport pandas as pd\n\n\nsql_config = {\n    'server': 'sql_server_hostname',\n    'database': 'database_name',\n    'username': 'test_user',\n    'password': 'test_user_password1234'\n}\ntable_name = 'test_dataframe'\ndf = pd.DataFrame(np.random.randint(-100, 100, size=(100, 4)),\n                  columns=list('ABCD'))\nbdf = bcpy.DataFrame(df)\nsql_table = bcpy.SqlTable(sql_config, table=table_name)\nbdf.to_sql(sql_table)\n```\n\n## Requirements\n\nYou 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.\n\n- [Dockerfile (Ubuntu 18.04)](./bcp.Dockerfile)\n- [Linux](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools)\n- [Mac](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-tools?view=sql-server-2017#macos)\n- [Windows](https://docs.microsoft.com/en-us/sql/tools/bcp-utility)",
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