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        "author": "Andrew Gross",
        "author_email": "andrew.w.gross@gmail.com",
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        "description": "Json2Parquet |Build Status|\n===========================\n\nThis library wraps ``pyarrow`` to provide some tools to easily convert\nJSON data into Parquet format. It is mostly in Python. It iterates over\nfiles. It copies the data several times in memory. It is not meant to be\nthe fastest thing available. However, it is convenient for smaller data\nsets, or people who don't have a huge issue with speed.\n\nInstallation\n~~~~~~~~~~~~\n\n.. code:: bash\n\n    pip install json2parquet\n\nUsage\n~~~~~\n\nHere's how to load a random JSON dataset.\n\n.. code:: python\n\n    from json2parquet import convert_json\n\n    # Infer Schema (requires reading dataset for column names)\n    convert_json(input_filename, output_filename)\n\n    # Given columns\n    convert_json(input_filename, output_filename, [\"my_column\", \"my_int\"])\n\n    # Given columns and custom field names\n    field_aliases = {'my_column': 'my_updated_column_name', \"my_int\": \"my_integer\"}\n    convert_json(input_filename, output_filename, [\"my_column\", \"my_int\"], field_aliases=field_aliases)\n\n\n    # Given PyArrow schema\n    import pyarrow as pa\n    schema = pa.schema([\n        pa.field('my_column', pa.string),\n        pa.field('my_int', pa.int64),\n    ])\n    convert_json(input_filename, output_filename, schema)\n\n\nYou can also work with Python data structures directly\n\n\n.. code:: python\n\n    from json2parquet import load_json, ingest_data, write_parquet, write_parquet_dataset\n\n    # Loading JSON to a PyArrow RecordBatch (schema is optional as above)\n    load_json(input_filename, schema)\n\n    # Working with a list of dictionaries\n    ingest_data(input_data, schema)\n\n    # Working with a list of dictionaries and custom field names\n    field_aliases = {'my_column': 'my_updated_column_name', \"my_int\": \"my_integer\"}\n    ingest_data(input_data, schema, field_aliases)\n\n    # Writing Parquet Files from PyArrow Record Batches\n    write_parquet(data, destination)\n\n    # You can also pass any keyword arguments that PyArrow accepts\n    write_parquet(data, destination, compression='snappy')\n\n    # You can also write partitioned date\n    write_parquet_dataset(data, destination_dir, partition_cols=[\"foo\", \"bar\", \"baz\"])\n\n\nIf you know your schema, you can specify custom datetime formats (only one for now).  This formatting will be ignored if you don't pass a PyArrow schema.\n\n.. code:: python\n\n    from json2parquet import convert_json\n\n    # Given PyArrow schema\n    import pyarrow as pa\n    schema = pa.schema([\n        pa.field('my_column', pa.string),\n        pa.field('my_int', pa.int64),\n    ])\n    date_format = \"%Y-%m-%dT%H:%M:%S.%fZ\"\n    convert_json(input_filename, output_filename, schema, date_format=date_format)\n\n\nAlthough ``json2parquet`` can infer schemas, it has helpers to pull in external ones as well\n\n.. code:: python\n\n    from json2parquet import load_json\n    from json2parquet.helpers import get_schema_from_redshift\n\n    # Fetch the schema from Redshift (requires psycopg2)\n    schema = get_schema_from_redshift(redshift_schema, redshift_table, redshift_uri)\n\n    # Load JSON with the Redshift schema\n    load_json(input_filename, schema)\n\n\nOperational Notes\n~~~~~~~~~~~~~~~~~\n\nIf you are using this library to convert JSON data to be read by ``Spark``, ``Athena``, ``Spectrum`` or ``Presto`` make sure you use ``use_deprecated_int96_timestamps`` when writing your Parquet files, otherwise you will see some really screwy dates.\n\n\nContributing\n~~~~~~~~~~~~\n\n\nCode Changes\n------------\n\n- Clone a fork of the library\n- Run ``make setup``\n- Run ``make test``\n- Apply your changes (don't bump version)\n- Add tests if needed\n- Run ``make test`` to ensure nothing broke\n- Submit PR\n\nDocumentation Changes\n---------------------\n\nIt is always a struggle to keep documentation correct and up to date.  Any fixes are welcome.  If you don't want to clone the repo to work locally, please feel free to edit using Github and to submit Pull Requests via Github's built in features.\n\n\n.. |Build Status| image:: https://travis-ci.org/andrewgross/json2parquet.svg?branch=master\n   :target: https://travis-ci.org/andrewgross/json2parquet\n\n\nChangelog\n---------\n\n0.0.28\n~~~~~~\n- Update `pyarrow` to `0.0.14` and `pandas` to `0.24.2`.  Thanks to @rudaporto for the PR!\n\n0.0.27\n~~~~~~\n- Update `pyarrow` to `0.0.12` to fix zlib issues and gain improved memory perf with lots of strings.  Thanks @nimish and @Madhu1512\n\n0.0.26\n~~~~~~\n- Add `field_aliases` kwarg to loading methods to allow mapping a JSON column name to a different parquet column name.  Thanks to @sojovi for the idea.\n\n0.0.25\n~~~~~~\n- Add `write_parquet_dataset` available as a default export, thanks to @gregburek\n\n0.0.24\n~~~~~~\n- Bump pyarrow, lower lock on Numpy to `>=1.14`\n\n0.0.23\n~~~~~~\n- Bump pyarrow, numpy and Pandas versions\n\n0.0.22\n~~~~~~\n- Bump pyarrow and Pandas versions\n\n0.0.21\n~~~~~~\n- Don't lock ciso8601 version.\n\n0.0.20\n~~~~~~\n- Add support for DATE fields. h/t to Spectrify for the implementation\n\n0.0.19\n~~~~~~\n- Properly handle boolean columns with `None`.\n\n0.0.18\n~~~~~~\n- Allow `schema` to be an optional argument to `convert_json`\n\n0.0.17\n~~~~~~\n- Bring `write_parquet_dataset` to a top level import\n\n0.0.16\n~~~~~~\n- Properly convert Boolean fields passed as numbers to PyArrow booleans.\n\n0.0.15\n~~~~~~\n- Add support for custom datetime formatting (thanks @Madhu1512)\n- Add support for writing partitioned datasets (thanks @mthota15)\n\n0.0.14\n~~~~~~\n- Stop silencing Redshift errors.\n\n0.0.13\n~~~~~~\n- Fix decimal type for newer pyarrow versions\n\n0.0.12\n~~~~~~\n- Allow casting of int64 -> int32\n\n0.0.11\n~~~~~~\n- Bump PyArrow and allow int32 data\n\n0.0.10\n~~~~~~\n- Allow passing partition columns when getting a Redshift schema, so they can be skipped\n\n0.0.9\n~~~~~~\n- Fix conversion of timestamp columns again\n\n0.0.8\n~~~~~~\n- Fix conversion of timestamp columns\n\n0.0.7\n~~~~~~\n- Force converted Timestamps to max out at `pandas.Timestamp.max` if they exceed the resolution of `datetime[ns]`\n\n0.0.6\n~~~~~~\n- Add automatic downcasting for Python ``float`` to ``float32`` via pandas when schema specifies ``pa.float32()``\n\n0.0.5\n~~~~~~\n- Fix conversion of float types to be size specific\n\n0.0.4\n~~~~~~\n- Fix ingestion of timestamp data with ns resolution\n\n0.0.3\n~~~~~~\n- Add pandas dependency\n- Add proper ingestion of timestamp data using Pandas ``to_datetime``\n\n0.0.2\n~~~~~~\n- Fix formatting of README so it displays on PyPI\n\n0.0.1\n~~~~~~\n\n- Initial release\n- JSON/data writing support\n- Redshift Schema reading support\n",
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