<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def applymap(self, func):
 """ 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. Returns ------- DataFrame Transformed DataFrame. See Also -------- DataFrame.apply : Apply a function along input axis of 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 -------- 0 1 0 1.000 2.120 1 3.356 4.567 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. 0 1 0 1.000000 4.494400 1 11.262736 20.857489 But it's better to avoid applymap in that case. 0 1 0 1.000000 4.494400 1 11.262736 20.857489 """


        # if we have a dtype == 'M8[ns]', provide boxed values
        def infer(x):
            if x.empty:
                return lib.map_infer(x, func)
            return lib.map_infer(x.astype(object).values, func)

        return self.apply(infer)