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# Difference between methods apply and applymap of a Pandas DataFrame

schedule Aug 12, 2023
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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:

# 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 ```
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