How to replace column values using na.replace() method while using more than one dictionary? - dataframe

Question

While replacing values of a column in a df using replace method how can we make use of the dictionary to do the same. There are 5 dictionaries that have to get applied to a dataframe. I am having problems with the syntax of doing this in one step. This dataset will be growing so i am trying to find the most efficent way to chain the replace method or create a list array with the column names.
ab_normaldict= { '0': 'Normal' , '1': 'Abnormal' , '999': 'Not Done'}
ethnicitydict = { '1': 'Hispanic or Latino' , '2': 'Not Hispanic or Latino' , '3':' Unknown'}
racedict = { '1': 'American Indian or Alaska Native' , '2':'Asian' , '3': 'Black or African American' , '4': 'Native Hawaiian or Other Pacific Islander' , '5': 'White' , '7': 'More than one race' , '6': 'Unknown/Other'}
sexdict = { '1': 'Female' , '2': 'Male' , '888': 'Other' , '999': 'Unknown'}
df1= spark.createDataFrame([
(person1, "0", "1", "2", "1"),
(person2, "1", "2", "1", "2"),
(person3, "999", "2", "3" , "1" ),
(person4,'Null', "1", "6", "1")])\
.toDF("id", "abnormal", "ethnicity, "racedict", "sex")
I saw that the syntax is:
df1.na.replace(to_replace= ab_normaldict,'abnormal')
df2=df1
df2.na.replace(to_replace=sexdict, 'sex')
but i need something like below so i don't have to keep creating a new dataframe
df1.na.replace(to_replace= ab_normaldict,'abnormal').na.replace(to_replace=sexdict, 'sex')```

Answer 1

Your code works just fine with me (except the positional argument)
(df1
.na.replace(ab_normaldict, 'abnormal')
.na.replace(sexdict, 'sex')
.show()
)
+---+--------+---------+--------+--------+
| id|abnormal|ethnicity|racedict| sex|
+---+--------+---------+--------+--------+
| 1| Normal| Abnormal| Male|Abnormal|
| 2|Abnormal| Male|Abnormal| Male|
| 3|Not Done| Male| 3|Abnormal|
| 4| Null| Abnormal| 6|Abnormal|
+---+--------+---------+--------+--------+