I am passing a dictionary to the map function to recode values in the column of a Pandas dataframe. However, I noticed that if there is a value in the original series that is not explicitly in the dictionary, it gets recoded to NaN. Here is a simple example:
Typing...
s = pd.Series(['one','two','three','four'])
...creates the series
0 one
1 two
2 three
3 four
dtype: object
But applying the map...
recodes = {'one':'A', 'two':'B', 'three':'C'}
s.map(recodes)
...returns the series
0 A
1 B
2 C
3 NaN
dtype: object
I would prefer that if any element in series s is not in the recodes dictionary, it remains unchanged. That is, I would prefer to return the series below (with the original four instead of NaN).
0 A
1 B
2 C
3 four
dtype: object
Is there an easy way to do this, for example an option to pass to the map function? The challenge I am having is that I can't always anticipate all possible values that will be in the series I'm recoding - the data will be updated in the future and new values could appear.
Thanks!
Use replace instead of map:
>>> s = pd.Series(['one','two','three','four'])
>>> recodes = {'one':'A', 'two':'B', 'three':'C'}
>>> s.map(recodes)
0 A
1 B
2 C
3 NaN
dtype: object
>>> s.replace(recodes)
0 A
1 B
2 C
3 four
dtype: object