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        "description": "# pandleau\n\nA quick and easy way to convert a Pandas DataFrame to a Tableau .tde or .hyper extract.\n\n## Getting Started\n\n### Prerequisites\n\n* If you want to output as a .tde format, you'll need to install TableauSDK directly from Tableau's site [here]( https://onlinehelp.tableau.com/current/api/sdk/en-us/help.htm#SDK/tableau_sdk_installing.htm%3FTocPath%3D_____3).\n* If you want to output as a .hyper format, you'll need to install Extract API 2.0 directly from Tableau's site [here](https://onlinehelp.tableau.com/current/api/extract_api/en-us/help.htm#Extract/extract_api_installing.htm%3FTocPath%3D_____3).\n* Although Tableau's site claims Python 3 is not supported, this module is tested to work fully functional on Python 3.6.\n\n### Installing\n\nOnce installing TableauSDK is done, download this repository, navigate to your downloads file and run the following in cmd or terminal:  \n```bash\npython -m setup.py install\n```\n\nYou can also install pandleau using pip:\n```bash\npip install pandleau\n```\nBut note that this will throw a warning to install tableausdk using the above link in Prerequisites.\n\n## Example\n\nI grabbed the following Brazil flights data off of kaggle for this example: https://www.kaggle.com/microtang/exploring-brazil-flights-data/data.\n\n```python\nimport pandas as pd\nfrom pandleau import *\n\n# Import the data\nexample_df = pd.read_csv(r'example/BrFlights2.csv', encoding = 'iso-8859-1')\n\n# Format dates in pandas\nexample_df['Partida.Prevista'] = pd.to_datetime(example_df['Partida.Prevista'], format = '%Y-%m-%d')\nexample_df['Partida.Real'] = pd.to_datetime(example_df['Partida.Real'], format = '%Y-%m-%d')\nexample_df['Chegada.Prevista'] = pd.to_datetime(example_df['Chegada.Prevista'], format = '%Y-%m-%d')\nexample_df['Chegada.Real'] = pd.to_datetime(example_df['Chegada.Real'], format = '%Y-%m-%d')\n\n# Set up a spatial column\nexample_df.loc[:, 'SpatialDest'] = example_df['LongDest'].apply( lambda x: \"POINT (\" + str( round(x, 6) ) ) + \\\n\texample_df['LatDest'].apply( lambda x: \" \"+str( round(x, 6) ) + \")\" )\n\n# Change to pandleau object\ndf_tableau = pandleau(example_df)\n\n# Define spatial column\ndf_tableau.set_spatial('SpatialDest', indicator=True)\n\n# Write .tde or .hyper Extract!\ndf_tableau.to_tableau('test.hyper', add_index=False)\n\n```\n\n## Tableau Server/Online Automation\n\nEric Chan ([erickhchan](https://github.com/erickhchan)) wrote a really cool blog post on using Python to blend and clean data before pushing it to Tableau Online (which is a SaaS version of Tableau Server). This is a great way to learn how to automate the data refresh process with Tableau Server Client and Pandleau. Check out his blog post here: https://www.erickhchan.com/data/2019/03/18/python-tableau-server.html\n\n## Authors\n\n* **Benjamin Wiley** - [jamin4lyfe](https://github.com/bwiley1)\n* **Zhirui(Jerry) Wang**  - [zhiruiwang](https://github.com/zhiruiwang)\n* **Aaron Wiegel** - [aawiegel](https://github.com/aawiegel)\n* **Pointy Shiny Burning** - [PointyShinyBurning](https://github.com/pointyshinyburning)\n* **Harrison** - [harrison-h](https://github.com/harrison-h)\n\n## Related Project\n\n[RTableau](https://github.com/zhiruiwang/RTableau) Convert R data.frame to Tableau Extract using pandleau\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n\n\n",
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