Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/18999#discussion_r134123916
--- 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
- seed = seed if seed is not None else random.randint(0, sys.maxsize)
- rdd = self._jdf.sample(withReplacement, fraction, long(seed))
- return DataFrame(rdd, self.sql_ctx)
+ .. note:: `fraction` is required and, `withReplacement` and `seed`
are optional.
+
+ >>> df = spark.range(10)
+ >>> df.sample(0.5, 3).count()
+ 4
+ >>> df.sample(fraction=0.5, seed=3).count()
+ 4
+ >>> df.sample(withReplacement=True, fraction=0.5, seed=3).count()
+ 1
+ >>> df.sample(1.0).count()
+ 10
+ >>> df.sample(fraction=1.0).count()
+ 10
+ >>> df.sample(False, fraction=1.0).count()
+ 10
+ >>> df.sample("a").count()
+ Traceback (most recent call last):
+ ...
+ TypeError:...
+ >>> df.sample(seed="abc").count()
+ Traceback (most recent call last):
+ ...
+ TypeError:...
--- End diff --
that makes sense! doc tests are examples users can follow
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