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        "description": "# shapeshifter Python Module\nThe official repository for the shapeshifter Python module, which allows for:\n* Transforming tabular data sets from one format to another.\n* Querying large data sets to filter out useful data.\n* Selecting additional columns/features to include in the resulting data set.\n* Merging data sets of various formats into a single file.\n* Gzipping resulting data sets, as well as the ability to read gzipped files.\n\nClick for information on the [shapeshifter command-line tool](https://github.com/srp33/ShapeShifter-CLI), which combines\nthe features of shapeshifter with the ease and speed of the command-line!\n\nBasic use is described below, but see the full documentation on [Read the Docs](https://shapeshifter.readthedocs.io/en/latest/).  \n## Install\n`pip3 install shapeshifter`\n\n## Basic Use\nAfter installing, import the ShapeShifter class with `from shapeshifter import ShapeShifter`. A ShapeShifter object \nrepresents the file to be transformed. It is then transformed using the `export_filter_results` method. Here is a simple\nexample of file called `input_file.tsv` being transformed into an HDF5 file called `output_file.h5`, while filtering \nthe data on sex and age:\n```python\nfrom shapeshifter import ShapeShifter\n\nmy_shapeshifter = ShapeShifter(\"input_file.tsv\")\nmy_shapeshifter.export_filter_results(\"output_file.h5\", filters=\"Sex == 'M' and Age > 40\")\n```\nNote that the type of file being read and exported to were not stated explicitly but inferred by shapeshifter based on\nthe file extensions provided. If necessary, `input_file_type` and `output_file_type` can be named explicitly.\n\n\n## Contributing\nWe welcome contributions that help expand shapeshifter to be compatible with additional file formats. If you are \ninterested in contributing, please follow the instructions [here](https://github.com/srp33/ShapeShifter/wiki).\n## Currently Supported Formats\n#### Input Formats:\n* CSV\n* TSV\n* JSON\n* Excel\n* HDF5\n* Parquet\n* MsgPack\n* Stata\n* Pickle\n* SQLite\n* ARFF\n* GCT\n* Kallisto\n* GEO\n\n#### Output Formats:\n* CSV \n* TSV\n* JSON\n* Excel\n* HDF5\n* Parquet\n* MsgPack\n* Stata \n* Pickle\n* SQLite \n* ARFF \n* GCT \n* RMarkdown \n* JupyterNotebook\n\n## Future Formats to Support\nWe are working hard to expand ShapeShifter to work with even more file formats! Expect the following formats to be \nincluded in future releases:\n* Fixed-width files (fwf)\n* Genomic Data Commons clinical XML\n\n\n",
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