Source code for woodwork.type_sys.utils

from datetime import datetime

import pandas as pd

import woodwork as ww
from woodwork.accessor_utils import _is_dask_series, _is_koalas_series
from woodwork.utils import import_or_none

ks = import_or_none("databricks.koalas")
dd = import_or_none("dask.dataframe")

def col_is_datetime(col, datetime_format=None):
    """Determine if a dataframe column contains datetime values or not. Returns True if column
    contains datetimes, False if not. Optionally specify the datetime format string for the column."""
    if _is_koalas_series(col):
        col = col.to_pandas()

    if"datetime") > -1 or (
        len(col) and isinstance(col.head(1), datetime)
        return True

    # if it can be cast to numeric, it's not a datetime
        pd.to_numeric(col, errors="raise")
    except (ValueError, TypeError):
        # finally, try to cast to datetime
        if"str") > -1 or"object") > -1:
            except Exception:
                return False
                return True

    return False

def _is_numeric_series(series, logical_type):
    """Determines whether a series will be considered numeric
    for the purposes of determining if it can be a time_index."""
    if _is_koalas_series(series):
        series = series.to_pandas()
    if _is_dask_series(series):
        series = series.get_partition(0).compute()

    # If column can't be made to be numeric, don't bother checking Logical Type
        pd.to_numeric(series, errors="raise")
    except (ValueError, TypeError):
        return False

    if logical_type is not None:
        if isinstance(logical_type, str):
            logical_type = ww.type_system.str_to_logical_type(logical_type)

        # Allow numeric columns to be interpreted as Datetimes - doesn't allow strings even if they could be numeric
        if _get_ltype_class(
        ) == ww.logical_types.Datetime and pd.api.types.is_numeric_dtype(series):
            return True
        logical_type = ww.type_system.infer_logical_type(series)

    return "numeric" in logical_type.standard_tags

[docs]def list_logical_types(): """Returns a dataframe describing all of the available Logical Types. Args: None Returns: pd.DataFrame: A dataframe containing details on each LogicalType, including the corresponding physical type and any standard semantic tags. """ ltypes_df = pd.DataFrame( [ { "name": ltype.__name__, "type_string": ltype.type_string, "description": ltype.__doc__, "physical_type": ltype.primary_dtype, "standard_tags": ltype.standard_tags, "is_default_type": ltype in ww.type_system._default_inference_functions, "is_registered": ltype in ww.type_system.registered_types, "parent_type": ww.type_system._get_parent(ltype), } for ltype in ww.logical_types.LogicalType.__subclasses__() ] ) return ltypes_df.sort_values("name").reset_index(drop=True)
[docs]def list_semantic_tags(): """Returns a dataframe describing all of the common semantic tags. Args: None Returns: pd.DataFrame: A dataframe containing details on each Semantic Tag, including the corresponding logical type(s). """ sem_tags = {} for ltype in ww.type_system.registered_types: for tag in ltype.standard_tags: if tag in sem_tags: sem_tags[tag].append(ltype) else: sem_tags[tag] = [ltype] tags_df = pd.DataFrame( [ {"name": tag, "is_standard_tag": True, "valid_logical_types": sem_tags[tag]} for tag in sem_tags ] ) tags_df = tags_df.append( pd.DataFrame( [ ["index", False, "Any LogicalType"], [ "time_index", False, [ww.type_system.str_to_logical_type("datetime")] + sem_tags["numeric"], ], [ "date_of_birth", False, [ww.type_system.str_to_logical_type("datetime")], ], ["ignore", False, "Any LogicalType"], ["passthrough", False, "Any LogicalType"], ], columns=tags_df.columns, ), ignore_index=True, ) return tags_df
def _get_ltype_class(ltype): if ltype in ww.type_system.registered_types: return ltype return ltype.__class__ def _get_specified_ltype_params(ltype): """Gets a dictionary of a LogicalType's parameters. Note: If the logical type has not been instantiated, no parameters have been specified for the LogicalType, so no parameters will be returned even if that LogicalType can have parameters set. Args: ltype (LogicalType): An instantiated or uninstantiated LogicalType Returns: dict: The LogicalType's specified parameters. """ if ltype in ww.type_system.registered_types: # Do not reveal parameters for an uninstantiated LogicalType return {} return ltype.__dict__ def _is_categorical_series(series: pd.Series, threshold: float) -> bool: """ Return ``True`` if the given series is "likely" to be categorical. Otherwise, return ``False``. We say that a series is "likely" to be categorical if the percentage of unique values relative to total non-NA values is below a certain threshold. In other words, if all values in the series are accounted for by a sufficiently small collection of unique values, then the series is categorical. """ try: nunique = series.nunique() except TypeError as e: # It doesn't seem like there's a more elegant way to do this. Pandas # doesn't provide an API that would give you any indication ahead of # time if a series with object dtype has any unhashable elements. if "unhashable type" in e.args[0]: return False else: raise # pragma: no cover if nunique == 0: return False pct_unique = nunique / series.count() return pct_unique <= threshold