Question

我对pyspark非常陌生。我的数据框看起来像-

id        value      subject
1          75         eng
1          80         his
2          83         math
2          73         science
3          88         eng

我想要我的数据框-

id     eng      his     math     science
1      .49      .51      0         0
2       0        0      .53       .47
3       1        0       0         0

这意味着逐行求和，然后对每个单元进行除法。要计算每个单元格的百分比。

我已经完成了以下代码，但是它不起作用-

from pyspark.sql import functions as F
from pyspark.sql import Window

df = df.withColumn('rank',F.dense_rank().over(Window.orderBy("id","value","subject")))
df.withColumn('combcol',F.concat(F.lit('col_'),df['rank'])).groupby('id').pivot('combcol').agg(F.first('value')).show()

Answer 1

检查以下代码是否适合您。

from pyspark.sql import functions as F

df = spark.createDataFrame(
    [ (1,75,'eng'), (1,80,'his'), (2,83,'math'), (2,73,'science'), (3,88,'eng') ]
  , [ 'id','value','subject' ]
)

# create the pivot table
df1 = df.groupby('id').pivot('subject').agg(F.first('value')).fillna(0)

# column names used to sum up for total
cols = df1.columns[1:]

# calculate the total and then percentage accordingly for each cols
df1.withColumn('total', sum([F.col(c) for c in cols])) \
   .select('id', *[ F.format_number(F.col(c)/F.col('total'),2).alias(c) for c in cols] ) \
   .show()
#+---+----+----+----+-------+                                                    
#| id| eng| his|math|science|
#+---+----+----+----+-------+
#|  1|0.48|0.52|0.00|   0.00|
#|  3|1.00|0.00|0.00|   0.00|
#|  2|0.00|0.00|0.53|   0.47|
#+---+----+----+----+-------+

Pyspark中的数据透视表

1 个答案: