Is there a difference between `Series.replace()` and `Series.map()` in pandas?

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Both pandas.Series.map and pandas.Series.replace seem to give the same result. Is there a reason for using one over the other? For example:

import pandas as pd
df = pd.Series(['Yes', 'No'])
df

0    Yes
1     No
dtype: object

df.replace(to_replace=['Yes', 'No'], value=[True, False])

0     True
1    False
dtype: bool

df.map({'Yes':True, 'No':False})

0     True
1    False
dtype: bool

df.replace(to_replace=['Yes', 'No'], value=[True, False]).equals(df.map({'Yes':True, 'No':False}))

True
pandas series python-3.x python
1 Answers

Both of these methods are used for substituting values.

From Series.replace docs:

Replace values given in to_replace with value.

From Series.map docs:

Used for substituting each value in a Series with another value, that may be derived from a function, a dict or a Series.

They differ in the following:

  1. replace accepts str, regex, list, dict, Series, int, float, or None. map accepts a dict or a Series.
  2. They differ in handling null values.
  3. replace uses re.sub under the hood.The rules for substitution for re.sub are the same.

Take below example:

In [124]: s = pd.Series([0, 1, 2, 3, 4])    
In [125]: s
Out[125]: 
0    0
1    1
2    2
3    3
4    4
dtype: int64

In [126]: s.replace({0: 5})
Out[126]: 
0    5
1    1
2    2
3    3
4    4
dtype: int64

In [129]: s.map({0: 'kitten', 1: 'puppy'}) 
Out[129]: 
0    kitten
1     puppy
2       NaN
3       NaN
4       NaN
dtype: object

As you can see for s.map method, values that are not found in the dict are converted to NaN, unless the dict has a default value (e.g. defaultdict)

For s.replace, it just replaces the value to be replaced keeping the rest as it is.

user_58208140
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