Python 'map' function inserting NaN, possible to return original values instead?

Question

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!

Answer 1

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 

Answer 2

If you still want to use map the map function (can be faster than replace in some cases), you can define missing values:

class MyDict(dict): def __missing__(self, key):     return key  s = pd.Series(['one', 'two', 'three', 'four'])  recodes = MyDict({ 'one':'A', 'two':'B', 'three':'C' })  s.map(recodes)  0       A 1       B 2       C 3    four dtype: object