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# Difference between methods apply and applymap of a Pandas DataFrame

Pandas

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Common questions

*schedule*Jul 1, 2022

local_offer Python●Pandas

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Both the DataFrame methods `apply(~)`

and `applymap(~)`

transform values in the DataFrame using the specified function.

However, the difference is as follows:

`apply(~)`

applies the specified function to each row or column of the DataFrame.`applymap(~)`

applies the specified function to each value of the DataFrame.

# Examples

Consider the following DataFrame:

```
df = pd.DataFrame({"A":[3,4], "B":[5,6]}, index=["a","b"])df
A Ba 3 5b 4 6
```

To compute the sum of each column of `df`

:

```
df.apply(np.sum)
A 7B 15dtype: int64
```

Since the summation is an operation involving a row or column, we must use `apply(~)`

method - `applymap(~)`

will not work here.

The `applymap(~)`

comes into play when we want to apply a transformation on each value of the DataFrame:

```
df.applymap(lambda x : 1 if x > 3 else 0)
A Ba 0 1b 1 1
```

# Related

Pandas DataFrame | apply method

Applies the specified function to each row or column of the DataFrame.

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Pandas DataFrame | applymap method

Applies a function on each entry of the source DataFrame.

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Published by Isshin Inada

Edited by 0 others

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