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 inBYTE_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': '1.4.0',
'creator': {
'library': 'pyarrow',
'version': '0.13.0'
}}