def approxCountDistinct(col, rsd=None):
"""Returns a new :class:`Column` for approximate distinct count of ``col``.
>>> df.agg(approxCountDistinct(df.age).alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
if rsd is None:
jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col))
else:
jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col), rsd)
return Column(jc)
def approxCountDistinct(col, rsd=None):
"""Returns a new :class:`Column` for approximate distinct count of ``col``.
>>> df.agg(approxCountDistinct(df.age).alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
if rsd is None:
jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col))
else:
jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col), rsd)
return Column(jc)
def countDistinct(col, *cols):
"""Returns a new :class:`Column` for distinct count of ``col`` or ``cols``.
>>> df.agg(countDistinct(df.age, df.name).alias('c')).collect()
[Row(c=2)]
>>> df.agg(countDistinct("age", "name").alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
jcols = ListConverter().convert([_to_java_column(c) for c in cols], sc._gateway._gateway_client)
jc = sc._jvm.functions.countDistinct(_to_java_column(col), sc._jvm.PythonUtils.toSeq(jcols))
return Column(jc)
def countDistinct(col, *cols):
"""Returns a new :class:`Column` for distinct count of ``col`` or ``cols``.
>>> df.agg(countDistinct(df.age, df.name).alias('c')).collect()
[Row(c=2)]
>>> df.agg(countDistinct("age", "name").alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
sc._gateway._gateway_client)
jc = sc._jvm.functions.countDistinct(_to_java_column(col),
sc._jvm.PythonUtils.toSeq(jcols))
return Column(jc)
def countDistinct(col, *cols):
"""Returns a new :class:`Column` for distinct count of ``col`` or ``cols``.
>>> df.agg(countDistinct(df.age, df.name).alias('c')).collect()
[Row(c=2)]
>>> df.agg(countDistinct("age", "name").alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
jc = sc._jvm.functions.countDistinct(_to_java_column(col), _to_seq(sc, cols, _to_java_column))
return Column(jc)
def countDistinct(col, *cols):
"""Returns a new :class:`Column` for distinct count of ``col`` or ``cols``.
>>> df.agg(countDistinct(df.age, df.name).alias('c')).collect()
[Row(c=2)]
>>> df.agg(countDistinct("age", "name").alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
jc = sc._jvm.functions.countDistinct(_to_java_column(col), _to_seq(sc, cols, _to_java_column))
return Column(jc)
def explode(col):
"""Returns a new row for each element in the given array or map.
>>> from pyspark.sql import Row
>>> eDF = sqlContext.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])
>>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()
[Row(anInt=1), Row(anInt=2), Row(anInt=3)]
>>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()
+---+-----+
|key|value|
+---+-----+
| a| b|
+---+-----+
"""
sc = SparkContext._active_spark_context
jc = sc._jvm.functions.explode(_to_java_column(col))
return Column(jc)
def __call__(self, *cols):
sc = SparkContext._active_spark_context
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
sc._gateway._gateway_client)
jc = self._judf.apply(sc._jvm.PythonUtils.toSeq(jcols))
return Column(jc)
def __call__(self, *cols):
sc = SparkContext._active_spark_context
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
sc._gateway._gateway_client)
jc = self._judf.apply(sc._jvm.PythonUtils.toSeq(jcols))
return Column(jc)
def _(col):
sc = SparkContext._active_spark_context
jc = getattr(sc._jvm.functions, name)(_to_java_column(col))
return Column(jc)
