345 lines
10 KiB
Python
345 lines
10 KiB
Python
"""
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Table Schema builders
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https://specs.frictionlessdata.io/json-table-schema/
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"""
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from typing import TYPE_CHECKING, Any, Dict, Optional, cast
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import warnings
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import pandas._libs.json as json
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from pandas._typing import DtypeObj, FrameOrSeries, JSONSerializable
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from pandas.core.dtypes.common import (
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is_bool_dtype,
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is_categorical_dtype,
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is_datetime64_dtype,
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is_datetime64tz_dtype,
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is_integer_dtype,
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is_numeric_dtype,
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is_period_dtype,
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is_string_dtype,
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is_timedelta64_dtype,
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)
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from pandas.core.dtypes.dtypes import CategoricalDtype
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from pandas import DataFrame
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import pandas.core.common as com
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if TYPE_CHECKING:
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from pandas.core.indexes.multi import MultiIndex
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loads = json.loads
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def as_json_table_type(x: DtypeObj) -> str:
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"""
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Convert a NumPy / pandas type to its corresponding json_table.
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Parameters
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----------
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x : np.dtype or ExtensionDtype
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Returns
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-------
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str
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the Table Schema data types
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Notes
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-----
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This table shows the relationship between NumPy / pandas dtypes,
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and Table Schema dtypes.
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============== =================
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Pandas type Table Schema type
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============== =================
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int64 integer
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float64 number
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bool boolean
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datetime64[ns] datetime
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timedelta64[ns] duration
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object str
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categorical any
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=============== =================
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"""
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if is_integer_dtype(x):
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return "integer"
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elif is_bool_dtype(x):
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return "boolean"
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elif is_numeric_dtype(x):
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return "number"
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elif is_datetime64_dtype(x) or is_datetime64tz_dtype(x) or is_period_dtype(x):
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return "datetime"
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elif is_timedelta64_dtype(x):
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return "duration"
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elif is_categorical_dtype(x):
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return "any"
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elif is_string_dtype(x):
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return "string"
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else:
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return "any"
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def set_default_names(data):
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"""Sets index names to 'index' for regular, or 'level_x' for Multi"""
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if com.all_not_none(*data.index.names):
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nms = data.index.names
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if len(nms) == 1 and data.index.name == "index":
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warnings.warn("Index name of 'index' is not round-trippable")
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elif len(nms) > 1 and any(x.startswith("level_") for x in nms):
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warnings.warn("Index names beginning with 'level_' are not round-trippable")
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return data
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data = data.copy()
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if data.index.nlevels > 1:
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names = [
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name if name is not None else f"level_{i}"
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for i, name in enumerate(data.index.names)
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]
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data.index.names = names
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else:
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data.index.name = data.index.name or "index"
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return data
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def convert_pandas_type_to_json_field(arr):
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dtype = arr.dtype
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if arr.name is None:
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name = "values"
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else:
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name = arr.name
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field: Dict[str, JSONSerializable] = {
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"name": name,
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"type": as_json_table_type(dtype),
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}
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if is_categorical_dtype(dtype):
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cats = dtype.categories
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ordered = dtype.ordered
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field["constraints"] = {"enum": list(cats)}
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field["ordered"] = ordered
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elif is_period_dtype(dtype):
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field["freq"] = dtype.freq.freqstr
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elif is_datetime64tz_dtype(dtype):
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field["tz"] = dtype.tz.zone
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return field
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def convert_json_field_to_pandas_type(field):
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"""
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Converts a JSON field descriptor into its corresponding NumPy / pandas type
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Parameters
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----------
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field
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A JSON field descriptor
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Returns
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-------
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dtype
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Raises
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------
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ValueError
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If the type of the provided field is unknown or currently unsupported
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Examples
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--------
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>>> convert_json_field_to_pandas_type({'name': 'an_int',
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'type': 'integer'})
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'int64'
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>>> convert_json_field_to_pandas_type({'name': 'a_categorical',
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'type': 'any',
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'constraints': {'enum': [
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'a', 'b', 'c']},
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'ordered': True})
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'CategoricalDtype(categories=['a', 'b', 'c'], ordered=True)'
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>>> convert_json_field_to_pandas_type({'name': 'a_datetime',
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'type': 'datetime'})
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'datetime64[ns]'
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>>> convert_json_field_to_pandas_type({'name': 'a_datetime_with_tz',
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'type': 'datetime',
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'tz': 'US/Central'})
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'datetime64[ns, US/Central]'
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"""
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typ = field["type"]
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if typ == "string":
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return "object"
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elif typ == "integer":
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return "int64"
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elif typ == "number":
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return "float64"
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elif typ == "boolean":
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return "bool"
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elif typ == "duration":
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return "timedelta64"
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elif typ == "datetime":
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if field.get("tz"):
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return f"datetime64[ns, {field['tz']}]"
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else:
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return "datetime64[ns]"
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elif typ == "any":
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if "constraints" in field and "ordered" in field:
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return CategoricalDtype(
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categories=field["constraints"]["enum"], ordered=field["ordered"]
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)
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else:
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return "object"
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raise ValueError(f"Unsupported or invalid field type: {typ}")
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def build_table_schema(
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data: FrameOrSeries,
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index: bool = True,
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primary_key: Optional[bool] = None,
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version: bool = True,
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) -> Dict[str, JSONSerializable]:
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"""
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Create a Table schema from ``data``.
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Parameters
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----------
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data : Series, DataFrame
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index : bool, default True
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Whether to include ``data.index`` in the schema.
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primary_key : bool or None, default True
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Column names to designate as the primary key.
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The default `None` will set `'primaryKey'` to the index
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level or levels if the index is unique.
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version : bool, default True
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Whether to include a field `pandas_version` with the version
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of pandas that generated the schema.
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Returns
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-------
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schema : dict
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Notes
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-----
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See `Table Schema
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<https://pandas.pydata.org/docs/user_guide/io.html#table-schema>`__ for
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conversion types.
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Timedeltas as converted to ISO8601 duration format with
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9 decimal places after the seconds field for nanosecond precision.
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Categoricals are converted to the `any` dtype, and use the `enum` field
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constraint to list the allowed values. The `ordered` attribute is included
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in an `ordered` field.
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Examples
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--------
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>>> df = pd.DataFrame(
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... {'A': [1, 2, 3],
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... 'B': ['a', 'b', 'c'],
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... 'C': pd.date_range('2016-01-01', freq='d', periods=3),
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... }, index=pd.Index(range(3), name='idx'))
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>>> build_table_schema(df)
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{'fields': [{'name': 'idx', 'type': 'integer'},
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{'name': 'A', 'type': 'integer'},
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{'name': 'B', 'type': 'string'},
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{'name': 'C', 'type': 'datetime'}],
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'pandas_version': '0.20.0',
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'primaryKey': ['idx']}
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"""
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if index is True:
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data = set_default_names(data)
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schema: Dict[str, Any] = {}
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fields = []
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if index:
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if data.index.nlevels > 1:
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data.index = cast("MultiIndex", data.index)
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for level, name in zip(data.index.levels, data.index.names):
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new_field = convert_pandas_type_to_json_field(level)
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new_field["name"] = name
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fields.append(new_field)
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else:
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fields.append(convert_pandas_type_to_json_field(data.index))
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if data.ndim > 1:
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for column, s in data.items():
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fields.append(convert_pandas_type_to_json_field(s))
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else:
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fields.append(convert_pandas_type_to_json_field(data))
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schema["fields"] = fields
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if index and data.index.is_unique and primary_key is None:
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if data.index.nlevels == 1:
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schema["primaryKey"] = [data.index.name]
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else:
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schema["primaryKey"] = data.index.names
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elif primary_key is not None:
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schema["primaryKey"] = primary_key
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if version:
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schema["pandas_version"] = "0.20.0"
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return schema
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def parse_table_schema(json, precise_float):
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"""
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Builds a DataFrame from a given schema
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Parameters
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----------
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json :
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A JSON table schema
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precise_float : boolean
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Flag controlling precision when decoding string to double values, as
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dictated by ``read_json``
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Returns
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-------
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df : DataFrame
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Raises
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------
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NotImplementedError
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If the JSON table schema contains either timezone or timedelta data
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Notes
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-----
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Because :func:`DataFrame.to_json` uses the string 'index' to denote a
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name-less :class:`Index`, this function sets the name of the returned
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:class:`DataFrame` to ``None`` when said string is encountered with a
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normal :class:`Index`. For a :class:`MultiIndex`, the same limitation
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applies to any strings beginning with 'level_'. Therefore, an
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:class:`Index` name of 'index' and :class:`MultiIndex` names starting
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with 'level_' are not supported.
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See Also
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--------
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build_table_schema : Inverse function.
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pandas.read_json
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"""
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table = loads(json, precise_float=precise_float)
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col_order = [field["name"] for field in table["schema"]["fields"]]
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df = DataFrame(table["data"], columns=col_order)[col_order]
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dtypes = {
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field["name"]: convert_json_field_to_pandas_type(field)
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for field in table["schema"]["fields"]
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}
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# No ISO constructor for Timedelta as of yet, so need to raise
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if "timedelta64" in dtypes.values():
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raise NotImplementedError(
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'table="orient" can not yet read ISO-formatted Timedelta data'
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)
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df = df.astype(dtypes)
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if "primaryKey" in table["schema"]:
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df = df.set_index(table["schema"]["primaryKey"])
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if len(df.index.names) == 1:
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if df.index.name == "index":
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df.index.name = None
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else:
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df.index.names = [
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None if x.startswith("level_") else x for x in df.index.names
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]
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return df
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