How to replace accented characters in PySpark?

Question

I have a string column in a dataframe with values with accents, like

'México', 'Albânia', 'Japão'

How to replace letters with accents to get this:

'Mexico', 'Albania', 'Japao'

I tried many solutions available in Stack OverFlow, like this:

def strip_accents(s):
   return ''.join(c for c in unicodedata.normalize('NFD', s)
                  if unicodedata.category(c) != 'Mn')

But disappointed returns

strip_accents('México')
>>> 'M?xico'

Answer 1

In PySpark, you can create a pandas_udf which is vectorized, so it's preferred to a regular udf .

This seems to be the best way to do it in pandas. So, we can use it to create a pandas_udf for PySpark application.

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', errors='ignore').str.decode('utf-8')

Test:

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

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

Answer 2

You can use 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  |
+---+---------+---------+