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fix: Always use temporal type for dttm columns [ID-2] (#17458)

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* fix: Always use temporal type for dttm columns

* move inference and implement in chart postproc

* fix test

* fix test case

Co-authored-by: Ville Brofeldt <ville.v.brofeldt@gmail.com>
This commit is contained in:
Kamil Gabryjelski
2021-11-22 15:40:38 +01:00
committed by GitHub
parent 66d756955b
commit 1f8eff72de
5 changed files with 40 additions and 11 deletions
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16
superset/utils/core.py
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@@ -1597,7 +1597,9 @@ def get_column_names_from_metrics(metrics: List[Metric]) -> List[str]:
return [col for col in map(get_column_name_from_metric, metrics) if col]
def extract_dataframe_dtypes(df: pd.DataFrame) -> List[GenericDataType]:
def extract_dataframe_dtypes(
df: pd.DataFrame, datasource: Optional["BaseDatasource"] = None,
) -> List[GenericDataType]:
"""Serialize pandas/numpy dtypes to generic types"""
# omitting string types as those will be the default type
@@ -1612,11 +1614,21 @@ def extract_dataframe_dtypes(df: pd.DataFrame) -> List[GenericDataType]:
"date": GenericDataType.TEMPORAL,
}
columns_by_name = (
{column.column_name: column for column in datasource.columns}
if datasource
else {}
)
generic_types: List[GenericDataType] = []
for column in df.columns:
column_object = columns_by_name.get(column)
series = df[column]
inferred_type = infer_dtype(series)
generic_type = inferred_type_map.get(inferred_type, GenericDataType.STRING)
generic_type = (
GenericDataType.TEMPORAL
if column_object and column_object.is_dttm
else inferred_type_map.get(inferred_type, GenericDataType.STRING)
)
generic_types.append(generic_type)
return generic_types