HyukjinKwon commented on a change in pull request #34931:
URL: https://github.com/apache/spark/pull/34931#discussion_r773008409
##########
File path: python/pyspark/pandas/frame.py
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@@ -8809,16 +8814,29 @@ def describe(self, percentiles: Optional[List[float]] =
None) -> "DataFrame":
max 3.0
Name: numeric1, dtype: float64
"""
- exprs = []
+ exprs_numeric = []
+ exprs_non_numeric = []
column_labels = []
+ # For storing the name of non-numeric type columns
+ column_names = []
+ is_timestamp_types = []
for label in self._internal.column_labels:
psser = self._psser_for(label)
- if isinstance(psser.spark.data_type, NumericType):
- exprs.append(psser._dtype_op.nan_to_null(psser).spark.column)
+ spark_data_type = psser.spark.data_type
+ if isinstance(spark_data_type, (NumericType, TimestampType,
TimestampNTZType)):
+
exprs_numeric.append(psser._dtype_op.nan_to_null(psser).spark.column)
column_labels.append(label)
-
- if len(exprs) == 0:
- raise ValueError("Cannot describe a DataFrame without columns")
+ # For checking if the column has timestamp type.
+ # We should handle the timestamp type differently from numeric
type.
+ is_timestamp_types.append(
+ (
+ isinstance(spark_data_type, (TimestampType,
TimestampNTZType)),
Review comment:
Can we just add another list called something like `exprs_timestamp`,
and store them? the variable name `is_timestamp_types` holding pairs looks a
bit weird.
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