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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def apply(self, func, convert_dtype=True, args=(), **kwds):
 """ Invoke function on values of Series. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. Parameters func : function Python function or NumPy ufunc to apply. convert_dtype : bool, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object. args : tuple Positional arguments passed to func after the series value. **kwds Additional keyword arguments passed to func. Returns ------- Series or DataFrame If func returns a Series object the result will be a DataFrame. See Also -------- Series.map: For element-wise operations. Series.agg: Only perform aggregating type operations. Series.transform: Only perform transforming type operations. Examples -------- Create a series with typical summer temperatures for each city. London 20 New York 21 Helsinki 12 dtype: int64 Square the values by defining a function and passing it as an argument to ``apply()``. London 400 New York 441 Helsinki 144 dtype: int64 Square the values by passing an anonymous function as an argument to ``apply()``. London 400 New York 441 Helsinki 144 dtype: int64 Define a custom function that needs additional positional arguments and pass these additional arguments using the ``args`` keyword. London 15 New York 16 Helsinki 7 dtype: int64 Define a custom function that takes keyword arguments and pass these arguments to ``apply``. London 95 New York 96 Helsinki 87 dtype: int64 Use a function from the Numpy library. London 2.995732 New York 3.044522 Helsinki 2.484907 dtype: float64 """

        if len(self) == 0:
            return self._constructor(dtype=self.dtype,
                                     index=self.index).__finalize__(self)

        # dispatch to agg
        if isinstance(func, (list, dict)):
            return self.aggregate(func, *args, **kwds)

        # if we are a string, try to dispatch
        if isinstance(func, str):
            return self._try_aggregate_string_function(func, *args, **kwds)

        # handle ufuncs and lambdas
        if kwds or args and not isinstance(func, np.ufunc):
            def f(x):
                return func(x, *args, **kwds)
        else:
            f = func

        with np.errstate(all='ignore'):
            if isinstance(f, np.ufunc):
                return f(self)

            # row-wise access
            if is_extension_type(self.dtype):
                mapped = self._values.map(f)
            else:
                values = self.astype(object).values
                mapped = lib.map_infer(values, f, convert=convert_dtype)

        if len(mapped) and isinstance(mapped[0], Series):
            from pandas.core.frame import DataFrame
            return DataFrame(mapped.tolist(), index=self.index)
        else:
            return self._constructor(mapped,
                                     index=self.index).__finalize__(self)