CODEWITHSUNDEEP
pythonpandas
zesla
zesla

Reputation: 11793

replace multiple values in a column and keep other values unchanged in pandas dataframe

I have an example dataframe like below:

df = pd.DataFrame({'name': ['red', 'orange', 'blue'],
                   'value': [22,44,66]})

    name    value
0   red     22
1   orange  44
2   blue    66

I need to replace red with 1 and blue with 2, leaving orange as it is, in the column name.

I use map function like below:

df.name.map({'red': 0, 'blue': 1})

I got result like:

    name    value
0   0.0     22
1   NaN     44
2   1.0     66

In the column, orange becomes NaN

What is the best way to replace the values?
I do not want to use df.name.str.replace(..) many times to replace many values. In my real data, there are 10 values to replace in a column. Thanks.

Upvotes: 1

Views: 5979

Answers (3)

Henry Yik
Henry Yik

Reputation: 22493

You can use Series.replace:

print (df["name"].replace({'red': 0, 'blue': 1}))

0         0
1    orange
2         1
Name: name, dtype: object

Upvotes: 5

N.Moudgil
N.Moudgil

Reputation: 869

One way can be creating your custom dict and overiding __missing__ 

import pandas as pd
df = pd.DataFrame({'name': ['red', 'orange', 'blue'],
               'value': [22,44,66]})

class Foo(dict):
    def __missing__(self, key):
       return key
df.name.map(Foo({'red': 0, 'blue': 1}))

From the documentation Series.map :

When arg is a dictionary, values in Series that are not in the dictionary (as keys) are converted to NaN. However, if the dictionary is a dict subclass that defines missing (i.e. provides a method for default values), then this default is used rather than NaN.

Upvotes: 2

BENY
BENY

Reputation: 323226

Fix your code with fillna 

df.name = df.name.map({'red': 0, 'blue': 1}).fillna(df.name)
0         0
1    orange
2         1
Name: name, dtype: object

Upvotes: 2

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