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Pandas Series | map method

schedule Aug 12, 2023
Last updated
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Pandas Series.map(~) method applies a mapping to each value of the Series. The mapping is not applied inplace, that is, a new Series is returned.

Parameters

1. arg | function or dict or Series

The mapping to apply to each value of the Series. Check out the examples below for clarification.

2. na_actionlink | None or string | optional

  • If None, then the mapping is also applied to NaN values.

  • If "ignore", then no mapping is applied to NaN values.

By default, na_action=None.

Return Value

A Series with the mapping applied.

Examples

Applying a function

To apply a function to a Series:

s = pd.Series([2,3])
s.map(lambda x: x+5)
0 7
1 8
dtype: int64

Here, a new Series is returned, so the original s is kept intact.

Applying a mapping

We can pass a dict or Series to map each value of the source Series to another value:

s = pd.Series(["a",3])
s.map({"a":"A"})
0 A
1 NaN
dtype: object

Notice how since 3 is not present as a a key in our dict, we get a NaN for that entry.

Specifying na_action

By default, na_action=None, which means that NaN values are also passed into the mapping function:

s = pd.Series([2,None,3])
s.map(lambda x: 5 if pd.isna(x) else 10) # na_action=None
0 10
1 5
2 10
dtype: int64

Setting to na_action="ignore" means that no mapping is applied to NaN values:

s = pd.Series([2,None,3])
s.map(lambda x: 5 if pd.isna(x) else 10, na_action="ignore")
0 10.0
1 NaN
2 10.0
dtype: float64
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Published by Isshin Inada
Edited by 0 others
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