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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 copy(self, deep=True):
 """ Make a copy of this object's indices and data. When ``deep=True`` (default), a new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below). When ``deep=False``, a new object will be created without copying the calling object's data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa). Parameters deep : bool, default True Make a deep copy, including a copy of the data and the indices. With ``deep=False`` neither the indices nor the data are copied. Returns ------- copy : Series, DataFrame or Panel Object type matches caller. Notes ----- When ``deep=True``, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to `copy.deepcopy` in the Standard Library, which recursively copies object data (see examples below). While ``Index`` objects are copied when ``deep=True``, the underlying numpy array is not copied for performance reasons. Since ``Index`` is immutable, the underlying data can be safely shared and a copy is not needed. Examples -------- a 1 b 2 dtype: int64 a 1 b 2 dtype: int64 **Shallow copy versus default (deep) copy:** Shallow copy shares data and index with original. False True Deep copy has own copy of data and index. False False Updates to the data shared by shallow copy and original is reflected in both; deep copy remains unchanged. a 3 b 4 dtype: int64 a 3 b 4 dtype: int64 a 1 b 2 dtype: int64 Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy. 0 [10, 2] 1 [3, 4] dtype: object 0 [10, 2] 1 [3, 4] dtype: object """

        data = self._data.copy(deep=deep)
        return self._constructor(data).__finalize__(self)