Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/18999#discussion_r134131441
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -659,19 +659,77 @@ def distinct(self):
return DataFrame(self._jdf.distinct(), self.sql_ctx)
@since(1.3)
- def sample(self, withReplacement, fraction, seed=None):
+ def sample(self, withReplacement=None, fraction=None, seed=None):
"""Returns a sampled subset of this :class:`DataFrame`.
+ :param withReplacement: Sample with replacement or not (default
False).
+ :param fraction: Fraction of rows to generate, range [0.0, 1.0].
+ :param seed: Seed for sampling (default a random seed).
+
.. note:: This is not guaranteed to provide exactly the fraction
specified of the total
count of the given :class:`DataFrame`.
- >>> df.sample(False, 0.5, 42).count()
- 2
- """
- assert fraction >= 0.0, "Negative fraction value: %s" % fraction
--- End diff --
Hm.. wouldn't we better avoid duplicating expression requirement? It looks
I should do:
https://github.com/apache/spark/blob/5ad1796b9fd6bce31bbc1cdc2f607115d2dd0e7d/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala#L714-L722
within Python side. I have been thinking of avoiding it if the error
message makes sense to Python users (but not the case of exposing non-Pythonic
error messages, for example, Java types `java.lang.Long` in the error message)
although I understand it is better to throw an exception ahead before going to
JVM.
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