""" Table Schema builders http://specs.frictionlessdata.io/json-table-schema/ """ from pandas.core.common import _all_not_none from pandas.core.dtypes.common import ( is_integer_dtype, is_timedelta64_dtype, is_numeric_dtype, is_bool_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_categorical_dtype, is_period_dtype, is_string_dtype ) def as_json_table_type(x): """ Convert a NumPy / pandas type to its corresponding json_table. Parameters ---------- x : array or dtype Returns ------- t : str the Table Schema data types Notes ----- This table shows the relationship between NumPy / pandas dtypes, and Table Schema dtypes. ============== ================= Pandas type Table Schema type ============== ================= int64 integer float64 number bool boolean datetime64[ns] datetime timedelta64[ns] duration object str categorical any =============== ================= """ if is_integer_dtype(x): return 'integer' elif is_bool_dtype(x): return 'boolean' elif is_numeric_dtype(x): return 'number' elif (is_datetime64_dtype(x) or is_datetime64tz_dtype(x) or is_period_dtype(x)): return 'datetime' elif is_timedelta64_dtype(x): return 'duration' elif is_categorical_dtype(x): return 'any' elif is_string_dtype(x): return 'string' else: return 'any' def set_default_names(data): """Sets index names to 'index' for regular, or 'level_x' for Multi""" if _all_not_none(*data.index.names): return data data = data.copy() if data.index.nlevels > 1: names = [name if name is not None else 'level_{}'.format(i) for i, name in enumerate(data.index.names)] data.index.names = names else: data.index.name = data.index.name or 'index' return data def make_field(arr, dtype=None): dtype = dtype or arr.dtype if arr.name is None: name = 'values' else: name = arr.name field = {'name': name, 'type': as_json_table_type(dtype)} if is_categorical_dtype(arr): if hasattr(arr, 'categories'): cats = arr.categories ordered = arr.ordered else: cats = arr.cat.categories ordered = arr.cat.ordered field['constraints'] = {"enum": list(cats)} field['ordered'] = ordered elif is_period_dtype(arr): field['freq'] = arr.freqstr elif is_datetime64tz_dtype(arr): if hasattr(arr, 'dt'): field['tz'] = arr.dt.tz.zone else: field['tz'] = arr.tz.zone return field def build_table_schema(data, index=True, primary_key=None, version=True): """ Create a Table schema from ``data``. Parameters ---------- data : Series, DataFrame index : bool, default True Whether to include ``data.index`` in the schema. primary_key : bool or None, default True column names to designate as the primary key. The default `None` will set `'primaryKey'` to the index level or levels if the index is unique. version : bool, default True Whether to include a field `pandas_version` with the version of pandas that generated the schema. Returns ------- schema : dict Examples -------- >>> df = pd.DataFrame( ... {'A': [1, 2, 3], ... 'B': ['a', 'b', 'c'], ... 'C': pd.date_range('2016-01-01', freq='d', periods=3), ... }, index=pd.Index(range(3), name='idx')) >>> build_table_schema(df) {'fields': [{'name': 'idx', 'type': 'integer'}, {'name': 'A', 'type': 'integer'}, {'name': 'B', 'type': 'string'}, {'name': 'C', 'type': 'datetime'}], 'pandas_version': '0.20.0', 'primaryKey': ['idx']} Notes ----- See `_as_json_table_type` for conversion types. Timedeltas as converted to ISO8601 duration format with 9 decimal places after the secnods field for nanosecond precision. Categoricals are converted to the `any` dtype, and use the `enum` field constraint to list the allowed values. The `ordered` attribute is included in an `ordered` field. """ if index is True: data = set_default_names(data) schema = {} fields = [] if index: if data.index.nlevels > 1: for level in data.index.levels: fields.append(make_field(level)) else: fields.append(make_field(data.index)) if data.ndim > 1: for column, s in data.iteritems(): fields.append(make_field(s)) else: fields.append(make_field(data)) schema['fields'] = fields if index and data.index.is_unique and primary_key is None: if data.index.nlevels == 1: schema['primaryKey'] = [data.index.name] else: schema['primaryKey'] = data.index.names elif primary_key is not None: schema['primaryKey'] = primary_key if version: schema['pandas_version'] = '0.20.0' return schema