Chain several WHEN conditions in a scalable way in PySpark

I have a dictionary (variable pats) with many when arguments: conditions and values.

from pyspark.sql import functions as F
df = spark.createDataFrame([("ė",), ("2",), ("",), ("@",)], ["col1"])

pats = {
  r"^\d$"          :"digit",
  r"^\p{L}$"       :"letter",
  r"^[\p{P}\p{S}]$":"spec_char",
  r"^$"            :"empty"
}

whens = (
    F.when(F.col("col1").rlike(list(pats.keys())[0]), pats[list(pats.keys())[0]])
     .when(F.col("col1").rlike(list(pats.keys())[1]), pats[list(pats.keys())[1]])
     .when(F.col("col1").rlike(list(pats.keys())[2]), pats[list(pats.keys())[2]])
     .when(F.col("col1").rlike(list(pats.keys())[3]), pats[list(pats.keys())[3]])
     .otherwise(F.col("col1"))
)
df = df.withColumn("col2", whens)

df.show()
# +----+---------+
# |col1|     col2|
# +----+---------+
# |   ė|   letter|
# |   2|    digit|
# |    |    empty|
# |   @|spec_char|
# +----+---------+

I'm looking for a scalable way to chain all the when conditions, so I wouldn't need to write a line for every key.

• Without reduce

  whens = F
  for k, v in pats.items():
      whens = whens.when(F.col("col1").rlike(k), v)
  whens = whens.otherwise(F.col("col1"))
  

  Full code:

  from pyspark.sql import functions as F
  df = spark.createDataFrame([("ė",), ("2",), ("",), ("@",)], ["col1"])
  
  pats = {
    r"^\d$"          :"digit",
    r"^\p{L}$"       :"letter",
    r"^[\p{P}\p{S}]$":"spec_char",
    r"^$"            :"empty"
  }
  
  whens = F
  for k, v in pats.items():
      whens = whens.when(F.col("col1").rlike(k), v)
  whens = whens.otherwise(F.col("col1"))
  
  df = df.withColumn("col2", whens)
  
  df.show()
  # +----+---------+
  # |col1|     col2|
  # +----+---------+
  # |   ė|   letter|
  # |   2|    digit|
  # |    |    empty|
  # |   @|spec_char|
  # +----+---------+
  

• Using reduce

  from functools import reduce
  
  whens = reduce(
      lambda acc, p: acc.when(F.col("col1").rlike(p), pats[p]),
      pats.keys(),
      F
  ).otherwise(F.col("col1"))
  

  Full code:

  from pyspark.sql import functions as F
  from functools import reduce
  df = spark.createDataFrame([("ė",), ("2",), ("",), ("@",)], ["col1"])
  
  pats = {
    r"^\d$"          :"digit",
    r"^\p{L}$"       :"letter",
    r"^[\p{P}\p{S}]$":"spec_char",
    r"^$"            :"empty"
  }
  
  whens = reduce(
      lambda acc, p: acc.when(F.col("col1").rlike(p), pats[p]),
      pats.keys(),
      F
  ).otherwise(F.col("col1"))
  
  df = df.withColumn("col2", whens)
  
  df.show()
  # +----+---------+
  # |col1|     col2|
  # +----+---------+
  # |   ė|   letter|
  # |   2|    digit|
  # |    |    empty|
  # |   @|spec_char|
  # +----+---------+