可以使用translate：

df = spark.createDataFrame(
    [
    ('1','Japão'),
    ('2','Irã'),
    ('3','São Paulo'),
    ('5','Canadá'),
    ('6','Tókio'),
    ('7','México'),
    ('8','Albânia')
    ],
    ["id", "Local"]
)

df.show(truncate = False)

+---+---------+
|id |Local    |
+---+---------+
|1  |Japão    |
|2  |Irã      |
|3  |São Paulo|
|5  |Canadá   |
|6  |Tókio    |
|7  |México   |
|8  |Albânia  |
+---+---------+

from pyspark.sql import functions as F

df\
    .withColumn('Loc_norm', F.translate('Local',
                                       'ãäöüẞáäčďéěíĺľňóôŕšťúůýžÄÖÜẞÁÄČĎÉĚÍĹĽŇÓÔŔŠŤÚŮÝŽ',
                                       'aaousaacdeeillnoorstuuyzAOUSAACDEEILLNOORSTUUYZ'))\
    .show(truncate=False)

+---+---------+---------+
|id |Local    |Loc_norm |
+---+---------+---------+
|1  |Japão    |Japao    |
|2  |Irã      |Ira      |
|3  |São Paulo|Sao Paulo|
|5  |Canadá   |Canada   |
|6  |Tókio    |Tokio    |
|7  |México   |Mexico   |
|8  |Albânia  |Albânia  |
+---+---------+---------+

在PySpark中，您可以创建一个矢量化的pandas_udf，因此它比常规的udf更受欢迎。
这似乎是在Pandas身上做这件事的最好方法。因此，我们可以使用它为PySpark应用程序创建一个pandas_udf。

from pyspark.sql import functions as F
import pandas as pd

@F.pandas_udf('string')
def strip_accents(s: pd.Series) -> pd.Series:
    return s.str.normalize('NFKD').str.encode('ascii', 'ignore').str.decode('utf-8')

测试：

df = spark.createDataFrame([('México',), ('Albânia',), ('Japão',)], ['country'])

df = df.withColumn('country2', strip_accents('country'))

df.show()
# +-------+--------+
# |country|country2|
# +-------+--------+
# | México|  Mexico|
# |Albânia| Albania|
# |  Japão|   Japao|
# +-------+--------+