HyukjinKwon commented on a change in pull request #34931:
URL: https://github.com/apache/spark/pull/34931#discussion_r771141874
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File path: python/pyspark/pandas/frame.py
##########
@@ -8828,22 +8842,117 @@ def describe(self, percentiles: Optional[List[float]]
= None) -> "DataFrame":
else:
percentiles = [0.25, 0.5, 0.75]
- formatted_perc = ["{:.0%}".format(p) for p in sorted(percentiles)]
- stats = ["count", "mean", "stddev", "min", *formatted_perc, "max"]
+ if len(exprs_numeric) == 0:
+ if len(exprs_non_numeric) == 0:
+ raise ValueError("Cannot describe a DataFrame without columns")
- sdf = self._internal.spark_frame.select(*exprs).summary(*stats)
- sdf = sdf.replace("stddev", "std", subset=["summary"])
+ # Handling non-numeric type columns
+ # We will retrive the `count`, `unique`, `top` and `freq`.
+ sdf = self._internal.spark_frame.select(*exprs_non_numeric)
- internal = InternalFrame(
- spark_frame=sdf,
- index_spark_columns=[scol_for(sdf, "summary")],
- column_labels=column_labels,
- data_spark_columns=[
- scol_for(sdf, self._internal.spark_column_name_for(label))
- for label in column_labels
- ],
- )
- return DataFrame(internal).astype("float64")
+ # Get `count` & `unique` for each columns
+ has_timestamp_type = True in is_timestamp_types
+ if not has_timestamp_type:
+ counts, uniques = map(
+ lambda x: x[1:], sdf.summary("count",
"count_distinct").take(2)
+ )
+ else:
+ # `summary` doesn't support for timestamp column, so we should
manually compute it
+ # if timestamp type column exists.
+ counts = []
+ uniques = []
+ exprs = []
+ for column in exprs_non_numeric:
+ exprs.append(F.count(column))
+ exprs.append(F.count_distinct(column))
+
+ count_unique_values =
list(sdf.select(*exprs).first().asDict().values())
+ for i in range(0, len(count_unique_values) - 1, 2):
+ counts.append(str(count_unique_values[i]))
+ uniques.append(str(count_unique_values[i + 1]))
+
+ # Get `top` & `freq` for each columns
+ tops = []
+ freqs = []
+ for column in exprs_non_numeric:
+ top, freq = (
+ sdf.groupby(column)
+ .count()
+ .sort("count", ascending=False)
+ .first()
+ .asDict()
+ .values()
+ )
+ tops.append(str(top))
+ freqs.append(str(freq))
+
+ stats = [counts, uniques, tops, freqs]
+ stats_names = ["count", "unique", "top", "freq"]
+
+ # Get `first` & `last` for each columns if timestamp type column
exists.
+ if has_timestamp_type:
+ exprs = []
+ for is_timestamp_type, column, column_name in zip(
+ is_timestamp_types, exprs_non_numeric, column_names
+ ):
+ if is_timestamp_type:
+ # `first` & `last` are min & max respectively for
timestamp type.
+ exprs.append(F.min(column))
+ exprs.append(F.max(column))
+ else:
+ # `first` & `last` are always NaN for string type
columns.
+ exprs.append(F.lit(np.NaN).alias(column_name +
"_first"))
+ exprs.append(F.lit(np.NaN).alias(column_name +
"_last"))
+
+ firsts = []
+ lasts = []
+ first_last_values =
list(sdf.select(*exprs).first().asDict().values())
Review comment:
what about when rows are empty?
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