Revert "[SPARK-7157][SQL] add sampleBy to DataFrame"

This reverts commit 0401cbaa8ee51c71f43604f338b65022a479da0a.

The new test case on Jenkins is failing.

1 files changed, 0 insertions, 40 deletions

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 213338dfe5..152b87351d 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -448,41 +448,6 @@ class DataFrame(object):
         rdd = self._jdf.sample(withReplacement, fraction, long(seed))
         return DataFrame(rdd, self.sql_ctx)
 
-    @since(1.5)
-    def sampleBy(self, col, fractions, seed=None):
-        """
-        Returns a stratified sample without replacement based on the
-        fraction given on each stratum.
-
-        :param col: column that defines strata
-        :param fractions:
-            sampling fraction for each stratum. If a stratum is not
-            specified, we treat its fraction as zero.
-        :param seed: random seed
-        :return: a new DataFrame that represents the stratified sample
-
-        >>> from pyspark.sql.functions import col
-        >>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key"))
-        >>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
-        >>> sampled.groupBy("key").count().orderBy("key").show()
-        +---+-----+
-        |key|count|
-        +---+-----+
-        |  0|    5|
-        |  1|    8|
-        +---+-----+
-        """
-        if not isinstance(col, str):
-            raise ValueError("col must be a string, but got %r" % type(col))
-        if not isinstance(fractions, dict):
-            raise ValueError("fractions must be a dict but got %r" % type(fractions))
-        for k, v in fractions.items():
-            if not isinstance(k, (float, int, long, basestring)):
-                raise ValueError("key must be float, int, long, or string, but got %r" % type(k))
-            fractions[k] = float(v)
-        seed = seed if seed is not None else random.randint(0, sys.maxsize)
-        return DataFrame(self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sql_ctx)
-
     @since(1.4)
     def randomSplit(self, weights, seed=None):
         """Randomly splits this :class:`DataFrame` with the provided weights.
@@ -1357,11 +1322,6 @@ class DataFrameStatFunctions(object):
 
     freqItems.__doc__ = DataFrame.freqItems.__doc__
 
-    def sampleBy(self, col, fractions, seed=None):
-        return self.df.sampleBy(col, fractions, seed)
-
-    sampleBy.__doc__ = DataFrame.sampleBy.__doc__
-
 
 def _test():
     import doctest