我的数据框包含id，price，timestamp这些列。
我想找到按id分组的中位数值。
我正在使用此代码来查找它，但它给了我这个错误。
from pyspark.sql import DataFrameStatFunctions as statFunc
windowSpec = Window.partitionBy("id")
median = statFunc.approxQuantile("price",
[0.5],
0) \
.over(windowSpec)
return df.withColumn("Median", median)
是否无法使用DataFrameStatFunctions填充新列中的值？
TypeError: unbound method approxQuantile() must be called with DataFrameStatFunctions instance as first argument (got str instance instead)
答案 0 :(得分：31)
Well, indeed it is not possible to use approxQuantile to fill values in a new dataframe column, but this is not why you are getting this error. Unfortunately, the whole underneath story is a rather frustrating one, as I have argued that is the case with many Spark (especially PySpark) features and their lack of adequate documentation.
To start with, there is not one, but two approxQuantile methods; the first one is part of the standard DataFrame class, i.e. you don't need to import DataFrameStatFunctions:
spark.version
# u'2.1.1'
sampleData = [("bob","Developer",125000),("mark","Developer",108000),("carl","Tester",70000),("peter","Developer",185000),("jon","Tester",65000),("roman","Tester",82000),("simon","Developer",98000),("eric","Developer",144000),("carlos","Tester",75000),("henry","Developer",110000)]
df = spark.createDataFrame(sampleData, schema=["Name","Role","Salary"])
df.show()
# +------+---------+------+
# | Name| Role|Salary|
# +------+---------+------+
# | bob|Developer|125000|
# | mark|Developer|108000|
# | carl| Tester| 70000|
# | peter|Developer|185000|
# | jon| Tester| 65000|
# | roman| Tester| 82000|
# | simon|Developer| 98000|
# | eric|Developer|144000|
# |carlos| Tester| 75000|
# | henry|Developer|110000|
# +------+---------+------+
med = df.approxQuantile("Salary", [0.5], 0.25) # no need to import DataFrameStatFunctions
med
# [98000.0]
The second one is part of DataFrameStatFunctions, but if you use it as you do, you get the error you report:
from pyspark.sql import DataFrameStatFunctions as statFunc
med2 = statFunc.approxQuantile( "Salary", [0.5], 0.25)
# TypeError: unbound method approxQuantile() must be called with DataFrameStatFunctions instance as first argument (got str instance instead)
because the correct usage is
med2 = statFunc(df).approxQuantile( "Salary", [0.5], 0.25)
med2
# [82000.0]
although you won't be able to find a simple example in the PySpark documentation about this (it took me some time to figure it out myself)... The best part? The two values are not equal:
med == med2
# False
I suspect this is due to the non-deterministic algorithm used (after all, it is supposed to be an approximate median), and even if you re-run the commands with the same toy data you may get different values (and different from the ones I report here) - I suggest to experiment a little to get the feeling...
But, as I already said, this is not the reason why you cannot use approxQuantile to fill values in a new dataframe column - even if you use the correct syntax, you will get a different error:
df2 = df.withColumn('median_salary', statFunc(df).approxQuantile( "Salary", [0.5], 0.25))
# AssertionError: col should be Column
Here, col refers to the second argument of the withColumn operation, i.e. the approxQuantile one, and the error message says that it is not a Column type - indeed, it is a list:
type(statFunc(df).approxQuantile( "Salary", [0.5], 0.25))
# list
So, when filling column values, Spark expects arguments of type Column, and you cannot use lists; here is an example of creating a new column with mean values per Role instead of median ones:
import pyspark.sql.functions as func
from pyspark.sql import Window
windowSpec = Window.partitionBy(df['Role'])
df2 = df.withColumn('mean_salary', func.mean(df['Salary']).over(windowSpec))
df2.show()
# +------+---------+------+------------------+
# | Name| Role|Salary| mean_salary|
# +------+---------+------+------------------+
# | carl| Tester| 70000| 73000.0|
# | jon| Tester| 65000| 73000.0|
# | roman| Tester| 82000| 73000.0|
# |carlos| Tester| 75000| 73000.0|
# | bob|Developer|125000|128333.33333333333|
# | mark|Developer|108000|128333.33333333333|
# | peter|Developer|185000|128333.33333333333|
# | simon|Developer| 98000|128333.33333333333|
# | eric|Developer|144000|128333.33333333333|
# | henry|Developer|110000|128333.33333333333|
# +------+---------+------+------------------+
which works because, contrary to approxQuantile, mean returns a Column:
type(func.mean(df['Salary']).over(windowSpec))
# pyspark.sql.column.Column
答案 1 :(得分：3)
如果您可以使用聚合而不是窗口函数，那么还可以使用pandas_udf。但是它们不如纯Spark快。这是docs的改编示例：
from pyspark.sql.functions import pandas_udf, PandasUDFType
df = spark.createDataFrame(
[(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id", "price")
)
@pandas_udf("double", PandasUDFType.GROUPED_AGG)
def median_udf(v):
return v.median()
df.groupby("id").agg(median_udf(df["price"])).show()
答案 2 :(得分：1)
由于组缺少聚合函数，因此我添加了一个按名称构造函数调用的示例（在这种情况下为percentile_approx）：
from pyspark.sql.column import Column, _to_java_column, _to_seq
def from_name(sc, func_name, *params):
"""
create call by function name
"""
callUDF = sc._jvm.org.apache.spark.sql.functions.callUDF
func = callUDF(func_name, _to_seq(sc, *params, _to_java_column))
return Column(func)
在groupBy中应用percentile_approx函数：
from pyspark.sql import SparkSession
from pyspark.sql import functions as f
spark = SparkSession.builder.getOrCreate()
sc = spark.sparkContext
# build percentile_approx function call by name:
target = from_name(sc, "percentile_approx", [f.col("salary"), f.lit(0.95)])
# load dataframe for persons data
# with columns "person_id", "group_id" and "salary"
persons = spark.read.parquet( ... )
# apply function for each group
persons.groupBy("group_id").agg(
target.alias("target")).show()
答案 3 :(得分：0)
从 PySpark 3.1.0 开始，引入了 percentile_approx 函数来解决这个问题。
函数 percentile_approx 返回一个列表，因此您需要对第一个元素进行切片。
如：
windowSpec = Window.partitionBy("id")
df.withColumn("Median", F.percentile_approx("price", [0.5]).over(windowSpec)[0])