def __init__(self, exprs, percentiles_relative_error=1 / 10000):
self.percentiles_relative_error = percentiles_relative_error
self.column_stat_helpers = {}
self.cols = [parse(e) for e in exprs] if exprs else [parse("*")]
# As python < 3.6 does not guarantee dict ordering
# we need to keep track of in which order the columns were
self.col_names = []
def merge(self, row, schema):
for col in self.cols:
for field in col.output_fields(schema):
col_name = field.name
if col_name not in self.column_stat_helpers:
self.column_stat_helpers[col_name] = ColumnStatHelper(
parse(col_name), self.percentiles_relative_error)
self.col_names.append(col_name)
self.column_stat_helpers[col_name].merge(row, schema)
return self
def pivot(self, pivot_col, values=None):
"""
>>> from pysparkling import Context, Row
>>> from pysparkling.sql.session import SparkSession
>>> from pysparkling.sql.functions import col, avg, sum
>>> sc = Context()
>>> spark = SparkSession(sc)
>>> df4 = sc.parallelize([Row(course="dotNET", year=2012, earnings=10000),
... Row(course="Java", year=2012, earnings=20000),
... Row(course="dotNET", year=2012, earnings=5000),
... Row(course="dotNET", year=2013, earnings=48000),
... Row(course="Java", year=2013, earnings=30000)]).toDF()
>>> df4.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").collect()
[Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)]
>>> df4.groupBy("year").pivot("course").sum("earnings").collect()
[Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]
>>> df4.groupBy("year").pivot("course", ["dotNET"]).sum("earnings").collect()
[Row(year=2012, dotNET=15000), Row(year=2013, dotNET=48000)]
>>> df4.groupBy("year").pivot("course").agg(sum("earnings")).show()
+----+-----+------+
|year| Java|dotNET|
+----+-----+------+
|2012|20000| 15000|
|2013|30000| 48000|
+----+-----+------+
>>> df4.groupBy("year").pivot("course", ["dotNET", "PHP"]).agg(sum("earnings")).show()
+----+------+----+
|year|dotNET| PHP|
+----+------+----+
|2012| 15000|null|
|2013| 48000|null|
+----+------+----+
>>> df4.groupBy("year").pivot("course").agg(sum("earnings"), avg("earnings")).show()
+----+------------------+------------------+--------------------+--------------------+
|year|Java_sum(earnings)|Java_avg(earnings)|dotNET_sum(earnings)|dotNET_avg(earnings)|
+----+------------------+------------------+--------------------+--------------------+
|2012| 20000| 20000.0| 15000| 7500.0|
|2013| 30000| 30000.0| 48000| 48000.0|
+----+------------------+------------------+--------------------+--------------------+
"""
jgd = self._jgd.pivot(parse(pivot_col), values)
return GroupedData(jgd, self._df)
def agg(self, *exprs):
"""
# >>> sorted(gdf.agg({"*": "count"}).collect())
# [Row(name=u'Alice', count(1)=1), Row(name=u'Bob', count(1)=1)]
>>> from pysparkling import Context, Row
>>> from pysparkling.sql.session import SparkSession
>>> from pysparkling.sql.functions import col, avg
>>> spark = SparkSession(Context())
>>> df = spark.createDataFrame(
... [Row(age=2, name='Alice'), Row(age=5, name='Bob')]
... )
>>> gdf = df.groupBy(df.name)
>>> from pysparkling.sql import functions as F
>>> sorted(gdf.agg(F.min(df.age)).collect())
[Row(name='Alice', min(age)=2), Row(name='Bob', min(age)=5)]
>>> df.groupBy("age").agg(avg("age"), col("age")).show()
+---+--------+---+
|age|avg(age)|age|
+---+--------+---+
| 2| 2.0| 2|
| 5| 5.0| 5|
+---+--------+---+
"""
if not exprs:
raise ValueError("exprs should not be empty")
if len(exprs) == 1 and isinstance(exprs[0], dict):
# pylint: disable=W0511
# todo implement agg_dict
jdf = self._jgd.agg_dict(exprs[0])
else:
# Columns
if not all(isinstance(c, Column) for c in exprs):
raise ValueError("all exprs should be Column")
# noinspection PyProtectedMember
jdf = self._jgd.agg([parse(e) for e in exprs])
return DataFrame(jdf, self.sql_ctx)
def sum(self, *cols):
return self.agg(*(sum(parse(col)) for col in cols))
def min(self, *cols):
return self.agg(*(min(parse(col)) for col in cols))
def avg(self, *cols):
return self.agg(*(avg(parse(col)) for col in cols))
