Method copy

networkx/classes/graph.py:1592–1679

Returns a copy of the graph. The copy method by default returns an independent shallow copy of the graph and attributes. That is, if an attribute is a container, that container is shared by the original an the copy. Use Python's `copy.deepcopy` for new containers.

(self, as_view=False)

        return False

    def copy(self, as_view=False):
        """Returns a copy of the graph.

        The copy method by default returns an independent shallow copy
        of the graph and attributes. That is, if an attribute is a
        container, that container is shared by the original an the copy.
        Use Python's `copy.deepcopy` for new containers.

        If `as_view` is True then a view is returned instead of a copy.

        Notes
        -----
        All copies reproduce the graph structure, but data attributes
        may be handled in different ways. There are four types of copies
        of a graph that people might want.

        Deepcopy -- A "deepcopy" copies the graph structure as well as
        all data attributes and any objects they might contain.
        The entire graph object is new so that changes in the copy
        do not affect the original object. (see Python's copy.deepcopy)

        Data Reference (Shallow) -- For a shallow copy the graph structure
        is copied but the edge, node and graph attribute dicts are
        references to those in the original graph. This saves
        time and memory but could cause confusion if you change an attribute
        in one graph and it changes the attribute in the other.
        NetworkX does not provide this level of shallow copy.

        Independent Shallow -- This copy creates new independent attribute
        dicts and then does a shallow copy of the attributes. That is, any
        attributes that are containers are shared between the new graph
        and the original. This is exactly what `dict.copy()` provides.
        You can obtain this style copy using:

            >>> G = nx.path_graph(5)
            >>> H = G.copy()
            >>> H = G.copy(as_view=False)
            >>> H = nx.Graph(G)
            >>> H = G.__class__(G)

        Fresh Data -- For fresh data, the graph structure is copied while
        new empty data attribute dicts are created. The resulting graph
        is independent of the original and it has no edge, node or graph
        attributes. Fresh copies are not enabled. Instead use:

            >>> H = G.__class__()
            >>> H.add_nodes_from(G)
            >>> H.add_edges_from(G.edges)

        View -- Inspired by dict-views, graph-views act like read-only
        versions of the original graph, providing a copy of the original
        structure without requiring any memory for copying the information.

        See the Python copy module for more information on shallow
        and deep copies, https://docs.python.org/3/library/copy.html.

        Parameters
        ----------