Pyspark: reshape data without aggregation

问题

I want to reshape my data from 4x3 to 2x2 in pyspark without aggregating. My current output is the following:

columns = ['FAULTY', 'value_HIGH', 'count']
vals = [
    (1, 0, 141),
    (0, 0, 140),
    (1, 1, 21),
    (0, 1, 12)
]

What I want is a contingency table with the second column as two new binary columns (value_HIGH_1, value_HIGH_0) and the values from the count column - meaning:

columns = ['FAULTY', 'value_HIGH_1', 'value_HIGH_0']
vals = [
    (1, 21, 141),
    (0, 12, 140)
]

回答1:

You can use pivot with a fake maximum aggregation (since you have only one element for each group):

import pyspark.sql.functions as F
df.groupBy('FAULTY').pivot('value_HIGH').agg(F.max('count')).selectExpr(
    'FAULTY', '`1` as value_high_1', '`0` as value_high_0'
).show()
+------+------------+------------+
|FAULTY|value_high_1|value_high_0|
+------+------------+------------+
|     0|          12|         140|
|     1|          21|         141|
+------+------------+------------+

回答2:

Using groupby and pivot is the natural way to do this, but if you want to avoid any aggregation you can achieve this with a filter and join

import pyspark.sql.functions as f

df.where("value_HIGH = 1").select("FAULTY", f.col("count").alias("value_HIGH_1"))\
    .join(
        df.where("value_HIGH = 0").select("FAULTY", f.col("count").alias("value_HIGH_1")),
        on="FAULTY"
    )\
    .show()
#+------+------------+------------+
#|FAULTY|value_HIGH_1|value_HIGH_1|
#+------+------------+------------+
#|     0|          12|         140|
#|     1|          21|         141|
#+------+------------+------------+

来源：https://stackoverflow.com/questions/53103269/pyspark-reshape-data-without-aggregation

标签

apache-spark

pyspark

contingency