Python DataFrame: Replace values using dictionary, convert NaN if not in dictionary

Question

I understand how to replace column values with using a dictionary however I want to convert all of the values that are not in my dictionary to NaN or some other value. I am getting this:

Dictionary is:
{'apple': 1, 'peach': 6, 'watermelon': 4, 'grapes': 5, 'orange': 2, 
'banana': 3}

DataFrame is: 
fruit_tag
apple
orange
banana
watermelon
red
blue

I use: 
df["fruit_tag"].replace(dict, inplace=True)
print(df)

I get:
fruit_tag
1
2
3
4
red
blue

What I want to get:
fruit_tag
1
2
3
4
NaN
NaN

jezrael · Accepted Answer · 2017-04-12 11:00:32Z

19

Use map:

d = {'apple': 1, 'peach': 6, 'watermelon': 4, 'grapes': 5, 'orange': 2,'banana': 3}

df["fruit_tag"] = df["fruit_tag"].map(d)
print (df)
   fruit_tag
0        1.0
1        2.0
2        3.0
3        4.0
4        NaN
5        NaN

answered Apr 12, 2017 at 11:00

jezrael

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2 Comments

Indrajeet Gour Over a year ago

what is the difference between map and replace?

jezrael Over a year ago

@IndrajeetGour - difference is with missing values. If use map and some category is missing, get NaNs. If use replace, catagory is untouched like in question last 2 rows..

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