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        "author": "Fabian Keller",
        "author_email": "pypi.20.fkeller@spamgourmet.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Information Technology",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "# Tabloo [![Build Status](https://travis-ci.org/bluenote10/tabloo.svg?branch=master)](https://travis-ci.org/bluenote10/tabloo) [![license](https://img.shields.io/github/license/mashape/apistatus.svg)](LICENSE)\n\nMinimalistic dashboard app for visualizing tabular data -- WIP.\n\n![demo](/../examples/examples/basic_demo.gif)\n\n\n## Installation\n\n```sh\npip install tabloo\n```\n\n\n## Usage\n\n### Embedded mode\n\nRun tabloo from within Python on an instance of a Pandas DataFrame:\n\n```python\n# given a pandas dataframe `df`\nimport tabloo\ntabloo.show(df)\n```\n\n\n### CLI mode\n\nRun tabloo from the command line on a CSV file:\n\n```sh\ntabloo_cli my.csv\n```\n\n### Bring our own backend\n\nTabloo decouples the visualization frontend from the data backend.\nYou could drive the frontend by arbitrary data backends, i.e.,\ninstead of running from DataFrames, you may implement a backend accessing other data sources like databases.\nThis implies that backends can be implemented in any other language.\nCurrently the backend API is still under development and may change.\nFor now, the [flask backend](src_backend_python/tabloo/server.py) can serve as a reference implementation.\n\n\n## Usage notes\n\nCurrently the \"Filter\" input element accepts `DataFrame.query()` expressions.\nIt handles expressions like:\n\n```\n# filter by specific values\nSomeColumn == \"some value\"\n\n# standard inequality operations are supported\nSomeNumberColumn > 0\n\n# it's possible to use advanced string conditions via `.str`\nSomeStringColumn.str.len() < 10\n\n# Combining conditions is possible with & | and or\nColumnA == 42 & SomeStringColumn.str.contains(\"substring\")\n```\n\nRefer to documentation of [query()](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-query) for more examples.\n\nNote: Eventually the syntax may change into a Pandas agnostic syntax to simplify implementations in non-Pandas backends.\n\n\n",
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