我想对 PySpark 上的数据框进行分层抽样。有一个 sampleBy(col, fractions, seed=None) 函数，但它似乎只使用一列作为层。有什么方法可以将多个列用作一个层吗？
请您参考如下方法:
基于答案here
将其转换为 python 后，我认为答案可能如下所示:
#create a dataframe to use
df = sc.parallelize([ (1,1234,282),(1,1396,179),(2,8620,178),(3,1620,191),(3,8820,828) ] ).toDF(["ID","X","Y"])
#we are going to use the first two columns as our key (strata)
#assign sampling percentages to each key # you could do something cooler here
fractions = df.rdd.map(lambda x: (x[0],x[1])).distinct().map(lambda x: (x,0.3)).collectAsMap()
#setup how we want to key the dataframe
kb = df.rdd.keyBy(lambda x: (x[0],x[1]))
#create a dataframe after sampling from our newly keyed rdd
#note, if the sample did not return any values you'll get a `ValueError: RDD is empty` error
sampleddf = kb.sampleByKey(False,fractions).map(lambda x: x[1]).toDF(df.columns)
sampleddf.show()
+---+----+---+
| ID| X| Y|
+---+----+---+
| 1|1234|282|
| 1|1396|179|
| 3|1620|191|
+---+----+---+
#other examples
kb.sampleByKey(False,fractions).map(lambda x: x[1]).toDF(df.columns).show()
+---+----+---+
| ID| X| Y|
+---+----+---+
| 2|8620|178|
+---+----+---+
kb.sampleByKey(False,fractions).map(lambda x: x[1]).toDF(df.columns).show()
+---+----+---+
| ID| X| Y|
+---+----+---+
| 1|1234|282|
| 1|1396|179|
+---+----+---+
这就是您要找的东西吗？
