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Pandas DataFrame | applymap method

Pandas
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Documentation
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DataFrame
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Functions and Aggregations
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.applymap(~) method applies a function on each entry of the source DataFrame. Note that a new copy of the DataFrame will be returned, and so the source DataFrame will be kept intact.

Parameters

1. func | function

A function that takes as argument an entry of the DataFrame and returns a new value.

Return Value

A new DataFrame with the transformed values.

WARNING

In order to check whether any optimisation is possible, applymap(~) calls the func twice on the first row/column. As you would expect, only one of these functions will actually apply the mapping and return a new value.

The trap though is that if the specified func modifies some other thing, then that modification will end up occurring twice for the first row/column.

Examples

Basic usage

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,4], "B":[5,6]})
df
A B
0 3 5
1 4 6

Suppose we wanted to increment every value in the DataFrame by 5:

df.applymap(lambda x : x + 5)
A B
0 8 10
1 9 11

Note the following:

  • x represents an entry of df (e.g. 3, 4 and so on).

  • our original df is kept intact since a new DataFrame is returned.

Prefer vectorised operations over applymap

Most of the time, we do not need to use applymap(~) since we can operate on elements directly, like so:

df + 5
A B
0 8 10
1 9 11

These vectorised operations are considerably more performant than transformations like apply(~) and applymap(~).

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Published by Isshin Inada
Edited by 0 others
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