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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 astype(self, dtype, copy=True, errors='raise', **kwargs):
 """ Cast a pandas object to a specified dtype ``dtype``. Parameters dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame's columns to column-specific types. copy : bool, default True Return a copy when ``copy=True`` (be very careful setting ``copy=False`` as changes to values then may propagate to other pandas objects). errors : {'raise', 'ignore'}, default 'raise' Control raising of exceptions on invalid data for provided dtype. - ``raise`` : allow exceptions to be raised - ``ignore`` : suppress exceptions. On error return original object .. versionadded:: 0.20.0 kwargs : keyword arguments to pass on to the constructor Returns ------- casted : same type as caller See Also -------- to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type. Examples -------- 0 1 1 2 dtype: int32 0 1 1 2 dtype: int64 Convert to categorical type: 0 1 1 2 dtype: category Categories (2, int64):
 [1, 2] Convert to ordered categorical type with custom ordering: 0 1 1 2 dtype: category Categories (2, int64):
 [2 < 1] Note that using ``copy=False`` and changing data on a new pandas object may propagate changes: 0 10 1 2 dtype: int64 """

        if is_dict_like(dtype):
            if self.ndim == 1:  # i.e. Series
                if len(dtype) > 1 or self.name not in dtype:
                    raise KeyError('Only the Series name can be used for '
                                   'the key in Series dtype mappings.')
                new_type = dtype[self.name]
                return self.astype(new_type, copy, errors, **kwargs)
            elif self.ndim > 2:
                raise NotImplementedError(
                    'astype() only accepts a dtype arg of type dict when '
                    'invoked on Series and DataFrames. A single dtype must be '
                    'specified when invoked on a Panel.'
                )
            for col_name in dtype.keys():
                if col_name not in self:
                    raise KeyError('Only a column name can be used for the '
                                   'key in a dtype mappings argument.')
            results = []
            for col_name, col in self.iteritems():
                if col_name in dtype:
                    results.append(col.astype(dtype=dtype[col_name], copy=copy,
                                              errors=errors, **kwargs))
                else:
                    results.append(results.append(col.copy() if copy else col))

        elif is_extension_array_dtype(dtype) and self.ndim > 1:
            # GH 18099/22869: columnwise conversion to extension dtype
            # GH 24704: use iloc to handle duplicate column names
            results = (self.iloc[:, i].astype(dtype, copy=copy)
                       for i in range(len(self.columns)))

        else:
            # else, only a single dtype is given
            new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,
                                         **kwargs)
            return self._constructor(new_data).__finalize__(self)

        # GH 19920: retain column metadata after concat
        result = pd.concat(results, axis=1, copy=False)
        result.columns = self.columns
        return result