替换 pyspark 数据帧中列名中的字符

Question

I have a dataframew like below in Pyspark

df = spark.createDataFrame([(2,'john',1,1),
                            (2,'john',1,2),
                            (3,'pete',8,3),
                            (3,'pete',8,4),
                            (5,'steve',9,5)],
                           ['id','/na/me','val/ue', 'rank/'])

df.show()

+---+------+------+-----+
| id|/na/me|val/ue|rank/|
+---+------+------+-----+
|  2|  john|     1|    1|
|  2|  john|     1|    2|
|  3|  pete|     8|    3|
|  3|  pete|     8|    4|
|  5| steve|     9|    5|
+---+------+------+-----+

Now in this data frame I want to replace the column names where / to under scrore _ . But if the / comes at the start or end of the column name then remove the / but don't replace with _ .

I have done like below

for name in df.schema.names:
  df = df.withColumnRenamed(name, name.replace('/', '_'))


>>> df
DataFrame[id: bigint, _na_me: string, val_ue: bigint, rank_: bigint]


>>>df.show()
+---+------+------+-----+
| id|_na_me|val_ue|rank_|
+---+------+------+-----+
|  2|  john|     1|    1|
|  2|  john|     1|    2|
|  3|  pete|     8|    3|
|  3|  pete|     8|    4|
|  5| steve|     9|    5|
+---+------+------+-----+

How can I achieve my desired result which is below

+---+------+------+-----+
| id| na_me|val_ue| rank|
+---+------+------+-----+
|  2|  john|     1|    1|
|  2|  john|     1|    2|
|  3|  pete|     8|    3|
|  3|  pete|     8|    4|
|  5| steve|     9|    5|
+---+------+------+-----+

Answer 1

Try with regular expression replace(re.sub) in python way.

import re
cols=[re.sub(r'(^_|_$)','',f.replace("/","_")) for f in df.columns]

df = spark.createDataFrame([(2,'john',1,1),
                            (2,'john',1,2),
                            (3,'pete',8,3),
                            (3,'pete',8,4),
                            (5,'steve',9,5)],
                           ['id','/na/me','val/ue', 'rank/'])

df.toDF(*cols).show()
#+---+-----+------+----+
#| id|na_me|val_ue|rank|
#+---+-----+------+----+
#|  2| john|     1|   1|
#|  2| john|     1|   2|
#|  3| pete|     8|   3|
#|  3| pete|     8|   4|
#|  5|steve|     9|   5|
#+---+-----+------+----+

#or using for loop on schema.names
for name in df.schema.names:
  df = df.withColumnRenamed(name, re.sub(r'(^_|_$)','',name.replace('/', '_')))

df.show()
#+---+-----+------+----+
#| id|na_me|val_ue|rank|
#+---+-----+------+----+
#|  2| john|     1|   1|
#|  2| john|     1|   2|
#|  3| pete|     8|   3|
#|  3| pete|     8|   4|
#|  5|steve|     9|   5|
#+---+-----+------+----+