# Developer

This section will focus on downstream applications of pandas.

## Storing pandas DataFrame objects in Apache Parquet format

The Apache Parquet format provides key-value metadata at the file and column level, stored in the footer of the Parquet file:

    5: optional list<KeyValue> key_value_metadata

where `KeyValue` is

    struct KeyValue {
      1: required string key
      2: optional string value
    }

So that a `pandas.DataFrame` can be faithfully reconstructed, we store a `pandas` metadata key in the `FileMetaData` with the value stored as :

    {'index_columns': [<descr0>, <descr1>, ...],
     'column_indexes': [<ci0>, <ci1>, ..., <ciN>],
     'columns': [<c0>, <c1>, ...],
     'pandas_version': $VERSION,
     'creator': {
       'library': $LIBRARY,
       'version': $LIBRARY_VERSION
     }}

The “descriptor” values `<descr0>` in the `'index_columns'` field are strings (referring to a column) or dictionaries with values as described below.

The `<c0>`/`<ci0>` and so forth are dictionaries containing the metadata for each column, including the index columns. This has JSON form:

    {'name': column_name,
     'field_name': parquet_column_name,
     'pandas_type': pandas_type,
     'numpy_type': numpy_type,
     'metadata': metadata}

See below for the detailed specification for these.

### Index metadata descriptors

`RangeIndex` can be stored as metadata only, not requiring serialization. The descriptor format for these as is follows:

    index = pd.RangeIndex(0, 10, 2)
    {
        "kind": "range",
        "name": index.name,
        "start": index.start,
        "stop": index.stop,
        "step": index.step,
    }

Other index types must be serialized as data columns along with the other DataFrame columns. The metadata for these is a string indicating the name of the field in the data columns, for example `'__index_level_0__'`.

If an index has a non-None `name` attribute, and there is no other column with a name matching that value, then the `index.name` value can be used as the descriptor. Otherwise (for unnamed indexes and ones with names colliding with other column names) a disambiguating name with pattern matching `__index_level_\d+__` should be used. In cases of named indexes as data columns, `name` attribute is always stored in the column descriptors as above.

### Column metadata

`pandas_type` is the logical type of the column, and is one of:

- Boolean: `'bool'`

- Integers: `'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64'`

- Floats: `'float16', 'float32', 'float64'`

- Date and Time Types: `'datetime', 'datetimetz'`, `'timedelta'`

- String: `'unicode', 'bytes'`

- Categorical: `'categorical'`

- Other Python objects: `'object'`

The `numpy_type` is the physical storage type of the column, which is the result of `str(dtype)` for the underlying NumPy array that holds the data. So for `datetimetz` this is `datetime64[ns]` and for categorical, it may be any of the supported integer categorical types.

The `metadata` field is `None` except for:

- `datetimetz`: `{'timezone': zone, 'unit': 'ns'}`, e.g. `{'timezone', 'America/New_York', 'unit': 'ns'}`. The `'unit'` is optional, and if omitted it is assumed to be nanoseconds.

- `categorical`: `{'num_categories': K, 'ordered': is_ordered, 'type': $TYPE}`

  > - Here `'type'` is optional, and can be a nested pandas type specification here (but not categorical)

- `unicode`: `{'encoding': encoding}`

  > - The encoding is optional, and if not present is UTF-8

- `object`: `{'encoding': encoding}`. Objects can be serialized and stored in `BYTE_ARRAY` Parquet columns. The encoding can be one of:

  > - `'pickle'`

  > - `'bson'`

  > - `'json'`

- `timedelta`: `{'unit': 'ns'}`. The `'unit'` is optional, and if omitted it is assumed to be nanoseconds. This metadata is optional altogether

For types other than these, the `'metadata'` key can be omitted. Implementations can assume `None` if the key is not present.

As an example of fully-formed metadata:

    {'index_columns': ['__index_level_0__'],
     'column_indexes': [
         {'name': None,
          'field_name': 'None',
          'pandas_type': 'unicode',
          'numpy_type': 'object',
          'metadata': {'encoding': 'UTF-8'}}
     ],
     'columns': [
         {'name': 'c0',
          'field_name': 'c0',
          'pandas_type': 'int8',
          'numpy_type': 'int8',
          'metadata': None},
         {'name': 'c1',
          'field_name': 'c1',
          'pandas_type': 'bytes',
          'numpy_type': 'object',
          'metadata': None},
         {'name': 'c2',
          'field_name': 'c2',
          'pandas_type': 'categorical',
          'numpy_type': 'int16',
          'metadata': {'num_categories': 1000, 'ordered': False}},
         {'name': 'c3',
          'field_name': 'c3',
          'pandas_type': 'datetimetz',
          'numpy_type': 'datetime64[ns]',
          'metadata': {'timezone': 'America/Los_Angeles'}},
         {'name': 'c4',
          'field_name': 'c4',
          'pandas_type': 'object',
          'numpy_type': 'object',
          'metadata': {'encoding': 'pickle'}},
         {'name': None,
          'field_name': '__index_level_0__',
          'pandas_type': 'int64',
          'numpy_type': 'int64',
          'metadata': None}
     ],
     'pandas_version': '0.20.0',
     'creator': {
       'library': 'pyarrow',
       'version': '0.13.0'
     }}