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pandavro

Build Status

The interface between Apache Avro and pandas DataFrame.

Installation

pandavro is available to install from PyPI.

$ pip install pandavro

Description

It prepares like pandas APIs:

  • read_avro
    • Read the records from Avro file and fit them into pandas DataFrame using fastavro.
  • to_avro
    • Write the rows of pandas DataFrame to Avro file with the original schema infer.

What can and can't pandavro do?

Avro can represent the following kinds of types:

  • Primitive types (null, bool, int etc.)
  • Complex types (records, arrays, maps etc.)
  • Logical types (annotated primitive/complex type to represent e.g. datetime)

When converting to Avro, pandavro will try to infer the schema. It will output a non-nested schema without any indexes set on the dataframe and it will also not try to infer if any column can be nullable so all columns are set as nullable, i.e. a boolean will be encoded in Avro schema as ['null', 'bool'].

Pandavro can handle these primitive types:

Numpy/pandas type Avro primitive type
np.bool_ boolean
np.float32 float
np.float64 double
np.unicode_ string
np.object_ string
np.int8, np.int16, np.int32 int
np.uint8, np.uint16, np.uint32 int
np.int64, np.uint64 long
pd.Int8Dtype, pd.Int16Dtype, pd.Int32Dtype int
pd.UInt8Dtype, pd.UInt16Dtype, pd.UInt32Dtype "unsigned" int
pd.Int64Dtype long
pd.UInt64Dtype "unsigned" long

Pandas 0.24 added support for nullable integers, which we can easily represent in Avro. We represent the unsigned versions of these integers by adding the non-standard "unsigned" flag as such: {'type': 'int', 'unsigned': True}.

And these logical types:

Numpy/pandas type Avro logical type
np.datetime64, pd.DatetimeTZDtype, pd.Timestamp timestamp-micros

Note that the timestamp must not contain any timezone (it must be naive) because Avro does not support timezones.

If you don't want pandavro to infer this schema but instead define it yourself, pass it using the schema kwarg to to_avro.

Example

import os
import numpy as np
import pandas as pd
import pandavro as pdx

OUTPUT_PATH='{}/example.avro'.format(os.path.dirname(__file__))


def main():
    df = pd.DataFrame({"Boolean": [True, False, True, False],
                       "Float64": np.random.randn(4),
                       "Int64": np.random.randint(0, 10, 4),
                       "String": ['foo', 'bar', 'foo', 'bar'],
                       "DateTime64": [pd.Timestamp('20190101'), pd.Timestamp('20190102'),
                                      pd.Timestamp('20190103'), pd.Timestamp('20190104')]})

    pdx.to_avro(OUTPUT_PATH, df)
    saved = pdx.read_avro(OUTPUT_PATH)
    print(saved)


if __name__ == '__main__':
    main()