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    "info": {
        "author": "Chuck McCallum",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6"
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        "description": "# dataframer\n\n[![PyPI version](https://badge.fury.io/py/dataframer.svg)](https://pypi.org/project/dataframer/)\n\nTries to load any file into a pandas DataFrame,\nwith a minimum of configuration,\nand a focus on bioinformatics\n\n## Examples\n\nTypically, you\u2019ll read a file from disk (`open('my-file.txt', 'rb')`),\nbut a byte stream is simpler here.\n\n```\n>>> from io import BytesIO\n>>> from dataframer import dataframer\n>>> from pandas import set_option\n\n>>> set_option('display.max_columns', None)\n\n>>> bytes = b'a,b,c,z\\n1,2,3,foo\\n4,5,6,bar'\n>>> stream = BytesIO(bytes)\n\n```\n\nDefault behavior is to strip non-numeric values after the first column.\n```\n>>> df_info = dataframer.parse(stream)\n>>> df_info.data_frame\n   b  c\na      \n1  2  3\n4  5  6\n>>> df_info.label_map is None\nTrue\n\n```\n\nAlternatively, they can be preserved in place...\n```\n>>> df_info = dataframer.parse(stream, keep_strings=True)\n>>> df_info.data_frame\n   b  c    z\na           \n1  2  3  foo\n4  5  6  bar\n>>> df_info.label_map is None\nTrue\n\n```\n\n... or they can be used to compose more meaningful row labels.\n```\n>>> df_info = dataframer.parse(stream, relabel=True)\n>>> df_info.data_frame\n   b  c\na      \n1  2  3\n4  5  6\n>>> df_info.label_map\n{1: 'foo / 1', 4: 'bar / 4'}\n\n```\n\nAlternatively, the first column can also be treated as data.\n```\n>>> df_info = dataframer.parse(stream, col_zero_index=False)\n>>> df_info.data_frame\n   a  b  c\n0  1  2  3\n1  4  5  6\n>>> df_info.label_map is None\nTrue\n\n```\n\nIf you don't need the whole file, but instead only want the first\nrow for column information:\n```\n>>> df_info = dataframer.parse(stream, first_row_only=True)\n>>> df_info.data_frame\n   b  c\na      \n1  2  3\n>>> df_info.label_map is None\nTrue\n\n```\n\nSingle column lists are given an implicit header:\n```\n>>> bytes = b'banana\\napple\\npear'\n>>> stream = BytesIO(bytes)\n>>> df_info = dataframer.parse(stream)\n>>> df_info.data_frame\n     item\n0  banana\n1   apple\n2    pear\n\n```\n\n## Release process\n\nIn your branch update `VERSION.txt`, using semantic versioning: When the PR\nis merged, the successful Travis build will push a new version to pypi.\n\n\n",
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        "license": "MIT License",
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        "name": "dataframer",
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