pandas.DataFrame.applymap — pandas 0.25.3 documentation (original) (raw)
DataFrame.
applymap
(self, func)[source]¶
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar to every element of a DataFrame.
Parameters: | func : callable Python function, returns a single value from a single value. |
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Returns: | DataFrame Transformed DataFrame. |
Notes
In the current implementation applymap calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row.
Examples
df = pd.DataFrame([[1, 2.12], [3.356, 4.567]]) df 0 1 0 1.000 2.120 1 3.356 4.567
df.applymap(lambda x: len(str(x))) 0 1 0 3 4 1 5 5
Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.
df.applymap(lambda x: x**2) 0 1 0 1.000000 4.494400 1 11.262736 20.857489
But it’s better to avoid applymap in that case.
df ** 2 0 1 0 1.000000 4.494400 1 11.262736 20.857489