diff options
Diffstat (limited to 'python/pyspark/sql')
-rw-r--r-- | python/pyspark/sql/dataframe.py | 40 |
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 |