在Spark 1.6 Dataframe上的其他字段中獲取每個組的不同元素

Question

我正在嘗試按星期在Spark數據框中進行分組，並為每個組計算一列的唯一值：

test.json
{"name":"Yin", "address":1111111, "date":20151122045510}
{"name":"Yin", "address":1111111, "date":20151122045501}
{"name":"Yln", "address":1111111, "date":20151122045500}
{"name":"Yun", "address":1111112, "date":20151122065832}
{"name":"Yan", "address":1111113, "date":20160101003221}
{"name":"Yin", "address":1111111, "date":20160703045231}
{"name":"Yin", "address":1111114, "date":20150419134543}
{"name":"Yen", "address":1111115, "date":20151123174302}

和代碼：

import pyspark.sql.funcions as func
from pyspark.sql.types import TimestampType
from datetime import datetime

df_y = sqlContext.read.json("/user/test.json")
udf_dt = func.udf(lambda x: datetime.strptime(x, '%Y%m%d%H%M%S'), TimestampType())
df = df_y.withColumn('datetime', udf_dt(df_y.date))
df_g = df_y.groupby(func.hour(df_y.date))    
df_g.count().distinct().show()

pyspark的結果是

df_y.groupby(df_y.name).count().distinct().show()
+----+-----+
|name|count|
+----+-----+
| Yan|    1|
| Yun|    1|
| Yin|    4|
| Yen|    1|
| Yln|    1|
+----+-----+

而我對大熊貓的期待是這樣的：

df = df_y.toPandas()
df.groupby('name').address.nunique()
Out[51]: 
name
Yan    1
Yen    1
Yin    2
Yln    1
Yun    1

如何通過其他字段獲取每個組的唯一元素，例如地址？

Answer 1

有一種方法可以使用函數countDistinct對每組的不同元素進行計數：

import pyspark.sql.functions as func
from pyspark.sql.types import TimestampType
from datetime import datetime

df_y = sqlContext.read.json("/user/test.json")
udf_dt = func.udf(lambda x: datetime.strptime(x, '%Y%m%d%H%M%S'), TimestampType())
df = df_y.withColumn('datetime', udf_dt(df_y.date))
df_g = df_y.groupby(func.hour(df_y.date))    
df_y.groupby(df_y.name).agg(func.countDistinct('address')).show()

+----+--------------+
|name|count(address)|
+----+--------------+
| Yan|             1|
| Yun|             1|
| Yin|             2|
| Yen|             1|
| Yln|             1|
+----+--------------+

文檔可在[這里]（ https://spark.apache.org/docs/1.6.0/api/java/org/apache/spark/sql/functions.html#countDistinct ( org.apache.spark.sql）。列 ，org.apache.spark.sql.Column ...））。

Answer 2

通過字段“_c1”對groupby進行簡明直接的回答，並計算字段“_c2”中不同數量的值：

import pyspark.sql.functions as F

dg = df.groupBy("_c1").agg(F.countDistinct("_c2"))