diff --git a/src/sage/graphs/base/static_sparse_graph.pyx b/src/sage/graphs/base/static_sparse_graph.pyx
index 6c4bc1b7edd..22509f2028c 100644
--- a/src/sage/graphs/base/static_sparse_graph.pyx
+++ b/src/sage/graphs/base/static_sparse_graph.pyx
@@ -732,8 +732,8 @@ def tarjan_strongly_connected_components(G):
 
     Checking against NetworkX::
 
-        sage: import networkx
-        sage: for i in range(10):                          # long time
+        sage: import networkx                                                                       # optional - networkx
+        sage: for i in range(10):                          # long time                              # optional - networkx
         ....:      g = digraphs.RandomDirectedGNP(100,.05)
         ....:      h = g.networkx_graph()
         ....:      scc1 = g.strongly_connected_components()
diff --git a/src/sage/graphs/bipartite_graph.py b/src/sage/graphs/bipartite_graph.py
index 62089c773da..dc68357dccf 100644
--- a/src/sage/graphs/bipartite_graph.py
+++ b/src/sage/graphs/bipartite_graph.py
@@ -308,10 +308,10 @@ class BipartiteGraph(Graph):
 
     #. From a NetworkX bipartite graph::
 
-        sage: import networkx
-        sage: G = graphs.OctahedralGraph()
-        sage: N = networkx.make_clique_bipartite(G.networkx_graph())
-        sage: B = BipartiteGraph(N)
+        sage: import networkx                                                                       # optional - networkx
+        sage: G = graphs.OctahedralGraph()                                                          # optional - networkx
+        sage: N = networkx.make_clique_bipartite(G.networkx_graph())                                # optional - networkx
+        sage: B = BipartiteGraph(N)                                                                 # optional - networkx
 
     TESTS:
 
diff --git a/src/sage/graphs/centrality.pyx b/src/sage/graphs/centrality.pyx
index 852b86c34e8..91f0fc21a52 100755
--- a/src/sage/graphs/centrality.pyx
+++ b/src/sage/graphs/centrality.pyx
@@ -99,11 +99,11 @@ def centrality_betweenness(G, bint exact=False, bint normalize=True):
 
     Compare with NetworkX::
 
-        sage: import networkx
-        sage: g = graphs.RandomGNP(100, .2)
-        sage: nw = networkx.betweenness_centrality(g.networkx_graph())
-        sage: sg = centrality_betweenness(g)
-        sage: max(abs(nw[x] - sg[x]) for x in g) # abs tol 1e-10
+        sage: import networkx                                                                       # optional - networkx
+        sage: g = graphs.RandomGNP(100, .2)                                                         # optional - networkx
+        sage: nw = networkx.betweenness_centrality(g.networkx_graph())                              # optional - networkx
+        sage: sg = centrality_betweenness(g)                                                        # optional - networkx
+        sage: max(abs(nw[x] - sg[x]) for x in g) # abs tol 1e-10                                    # optional - networkx
         0
 
     Stupid cases::
diff --git a/src/sage/graphs/digraph.py b/src/sage/graphs/digraph.py
index 2a63c18f3b1..4a3ccf3da78 100644
--- a/src/sage/graphs/digraph.py
+++ b/src/sage/graphs/digraph.py
@@ -427,17 +427,17 @@ class DiGraph(GenericGraph):
 
     #. A NetworkX MultiDiGraph::
 
-            sage: import networkx
-            sage: g = networkx.MultiDiGraph({0: [1, 2, 3], 2: [4]})
-            sage: DiGraph(g)
+            sage: import networkx                                                       # optional - networkx
+            sage: g = networkx.MultiDiGraph({0: [1, 2, 3], 2: [4]})                     # optional - networkx
+            sage: DiGraph(g)                                                            # optional - networkx
             Multi-digraph on 5 vertices
 
 
     #. A NetworkX digraph::
 
-            sage: import networkx
-            sage: g = networkx.DiGraph({0: [1, 2, 3], 2: [4]})
-            sage: DiGraph(g)
+            sage: import networkx                                                       # optional - networkx
+            sage: g = networkx.DiGraph({0: [1, 2, 3], 2: [4]})                          # optional - networkx
+            sage: DiGraph(g)                                                            # optional - networkx
             Digraph on 5 vertices
 
     #. An igraph directed Graph (see also
@@ -477,18 +477,18 @@ class DiGraph(GenericGraph):
     Demonstrate that digraphs using the static backend are equal to mutable
     graphs but can be used as dictionary keys::
 
-        sage: import networkx
-        sage: g = networkx.DiGraph({0:[1,2,3], 2:[4]})
-        sage: G = DiGraph(g)
-        sage: G_imm = DiGraph(G, data_structure="static_sparse")
-        sage: H_imm = DiGraph(G, data_structure="static_sparse")
-        sage: H_imm is G_imm
+        sage: import networkx                                                           # optional - networkx
+        sage: g = networkx.DiGraph({0:[1,2,3], 2:[4]})                                  # optional - networkx
+        sage: G = DiGraph(g)                                                            # optional - networkx
+        sage: G_imm = DiGraph(G, data_structure="static_sparse")                        # optional - networkx
+        sage: H_imm = DiGraph(G, data_structure="static_sparse")                        # optional - networkx
+        sage: H_imm is G_imm                                                            # optional - networkx
         False
-        sage: H_imm == G_imm == G
+        sage: H_imm == G_imm == G                                                       # optional - networkx
         True
-        sage: {G_imm:1}[H_imm]
+        sage: {G_imm:1}[H_imm]                                                          # optional - networkx
         1
-        sage: {G_imm:1}[G]
+        sage: {G_imm:1}[G]                                                              # optional - networkx
         Traceback (most recent call last):
         ...
         TypeError: This graph is mutable, and thus not hashable. Create an
@@ -498,10 +498,10 @@ class DiGraph(GenericGraph):
     specifying the ``immutable`` optional argument (not only by
     ``data_structure='static_sparse'`` as above)::
 
-        sage: J_imm = DiGraph(G, immutable=True)
-        sage: J_imm == G_imm
+        sage: J_imm = DiGraph(G, immutable=True)                                        # optional - networkx
+        sage: J_imm == G_imm                                                            # optional - networkx
         True
-        sage: type(J_imm._backend) == type(G_imm._backend)
+        sage: type(J_imm._backend) == type(G_imm._backend)                              # optional - networkx
         True
 
     From a list of vertices and a list of edges::
@@ -513,7 +513,7 @@ class DiGraph(GenericGraph):
 
     Check that :trac:`27505` is fixed::
 
-        sage: DiGraph(DiGraph().networkx_graph(), weighted=None, format='NX')
+        sage: DiGraph(DiGraph().networkx_graph(), weighted=None, format='NX')           # optional - networkx
         Digraph on 0 vertices
     """
     _directed = True
diff --git a/src/sage/graphs/generators/basic.py b/src/sage/graphs/generators/basic.py
index 84d520dd969..a685f6cc233 100644
--- a/src/sage/graphs/generators/basic.py
+++ b/src/sage/graphs/generators/basic.py
@@ -248,12 +248,12 @@ def CycleGraph(n):
 
     Compare plotting using the predefined layout and networkx::
 
-        sage: import networkx
-        sage: n = networkx.cycle_graph(23)
-        sage: spring23 = Graph(n)
-        sage: posdict23 = graphs.CycleGraph(23)
-        sage: spring23.show()  # long time
-        sage: posdict23.show()  # long time
+        sage: import networkx                                                           # optional - networkx
+        sage: n = networkx.cycle_graph(23)                                              # optional - networkx
+        sage: spring23 = Graph(n)                                                       # optional - networkx
+        sage: posdict23 = graphs.CycleGraph(23)                                         # optional - networkx
+        sage: spring23.show()  # long time                                              # optional - networkx
+        sage: posdict23.show()  # long time                                             # optional - networkx
 
     We next view many cycle graphs as a Sage graphics array. First we use the
     ``CycleGraph`` constructor, which fills in the position dictionary::
@@ -275,17 +275,17 @@ def CycleGraph(n):
 
         sage: g = []
         sage: j = []
-        sage: for i in range(9):
+        sage: for i in range(9):                                                        # optional - networkx
         ....:     spr = networkx.cycle_graph(i+3)
         ....:     k = Graph(spr)
         ....:     g.append(k)
-        sage: for i in range(3):
+        sage: for i in range(3):                                                        # optional - networkx
         ....:     n = []
         ....:     for m in range(3):
         ....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
         ....:     j.append(n)
-        sage: G = graphics_array(j)
-        sage: G.show()  # long time
+        sage: G = graphics_array(j)                                                     # optional - networkx
+        sage: G.show()  # long time                                                     # optional - networkx
 
     TESTS:
 
@@ -349,27 +349,27 @@ def CompleteGraph(n):
 
     We compare to plotting with the spring-layout algorithm::
 
-        sage: import networkx
+        sage: import networkx                                                           # optional - networkx
         sage: g = []
         sage: j = []
-        sage: for i in range(9):
+        sage: for i in range(9):                                                        # optional - networkx
         ....:     spr = networkx.complete_graph(i+3)
         ....:     k = Graph(spr)
         ....:     g.append(k)
-        sage: for i in range(3):
+        sage: for i in range(3):                                                        # optional - networkx
         ....:     n = []
         ....:     for m in range(3):
         ....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
         ....:     j.append(n)
-        sage: G = graphics_array(j)
-        sage: G.show()  # long time
+        sage: G = graphics_array(j)                                                     # optional - networkx
+        sage: G.show()  # long time                                                     # optional - networkx
 
     Compare the constructors (results will vary)::
 
-        sage: import networkx
-        sage: t = cputime()
-        sage: n = networkx.complete_graph(389); spring389 = Graph(n)
-        sage: cputime(t)  # random
+        sage: import networkx                                                           # optional - networkx
+        sage: t = cputime()                                                             # optional - networkx
+        sage: n = networkx.complete_graph(389); spring389 = Graph(n)                    # optional - networkx
+        sage: cputime(t)  # random                                                      # optional - networkx
         0.59203700000000126
         sage: t = cputime()
         sage: posdict389 = graphs.CompleteGraph(389)
@@ -378,11 +378,11 @@ def CompleteGraph(n):
 
     We compare plotting::
 
-        sage: import networkx
-        sage: n = networkx.complete_graph(23)
+        sage: import networkx                                                           # optional - networkx
+        sage: n = networkx.complete_graph(23)                                           # optional - networkx
         sage: spring23 = Graph(n)
         sage: posdict23 = graphs.CompleteGraph(23)
-        sage: spring23.show()  # long time
+        sage: spring23.show()  # long time                                              # optional - networkx
         sage: posdict23.show()  # long time
     """
     G = Graph(n, name="Complete graph")
@@ -438,19 +438,19 @@ def CompleteBipartiteGraph(p, q, set_position=True):
     Two ways of constructing the complete bipartite graph, using different
     layout algorithms::
 
-        sage: import networkx
-        sage: n = networkx.complete_bipartite_graph(389, 157); spring_big = Graph(n)   # long time
+        sage: import networkx                                                                       # optional - networkx
+        sage: n = networkx.complete_bipartite_graph(389, 157); spring_big = Graph(n)   # long time  # optional - networkx
         sage: posdict_big = graphs.CompleteBipartiteGraph(389, 157)                    # long time
 
     Compare the plotting::
 
-        sage: n = networkx.complete_bipartite_graph(11, 17)
-        sage: spring_med = Graph(n)
+        sage: n = networkx.complete_bipartite_graph(11, 17)                                         # optional - networkx
+        sage: spring_med = Graph(n)                                                                 # optional - networkx
         sage: posdict_med = graphs.CompleteBipartiteGraph(11, 17)
 
     Notice here how the spring-layout tends to center the nodes of `n1`::
 
-        sage: spring_med.show()  # long time
+        sage: spring_med.show()  # long time                                                        # optional - networkx
         sage: posdict_med.show()  # long time
 
     View many complete bipartite graphs with a Sage Graphics Array, with this
@@ -473,17 +473,17 @@ def CompleteBipartiteGraph(p, q, set_position=True):
 
         sage: g = []
         sage: j = []
-        sage: for i in range(9):
+        sage: for i in range(9):                                                                    # optional - networkx
         ....:     spr = networkx.complete_bipartite_graph(i+1,4)
         ....:     k = Graph(spr)
         ....:     g.append(k)
-        sage: for i in range(3):
+        sage: for i in range(3):                                                                    # optional - networkx
         ....:     n = []
         ....:     for m in range(3):
         ....:         n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
         ....:     j.append(n)
-        sage: G = graphics_array(j)
-        sage: G.show()  # long time
+        sage: G = graphics_array(j)                                                                 # optional - networkx
+        sage: G.show()  # long time                                                                 # optional - networkx
 
     :trac:`12155`::
 
@@ -933,9 +933,9 @@ def GridGraph(dim_list):
 
         sage: dim = [randint(1,4) for i in range(4)]
         sage: g = graphs.GridGraph(dim)
-        sage: import networkx
-        sage: h = Graph( networkx.grid_graph(list(dim)) )
-        sage: g.is_isomorphic(h)
+        sage: import networkx                                                                       # optional - networkx
+        sage: h = Graph(networkx.grid_graph(list(dim)))                                             # optional - networkx
+        sage: g.is_isomorphic(h)                                                                    # optional - networkx
         True
 
     Trivial cases::
@@ -1223,14 +1223,14 @@ def StarGraph(n):
 
     EXAMPLES::
 
-        sage: import networkx
+        sage: import networkx                                                                       # optional - networkx
 
     Compare the plots::
 
-        sage: n = networkx.star_graph(23)
-        sage: spring23 = Graph(n)
+        sage: n = networkx.star_graph(23)                                                           # optional - networkx
+        sage: spring23 = Graph(n)                                                                   # optional - networkx
         sage: posdict23 = graphs.StarGraph(23)
-        sage: spring23.show()  # long time
+        sage: spring23.show()  # long time                                                          # optional - networkx
         sage: posdict23.show()  # long time
 
     View many star graphs as a Sage Graphics Array
diff --git a/src/sage/graphs/generators/families.py b/src/sage/graphs/generators/families.py
index 9dd7bc1cb1e..ccd8af1ff25 100644
--- a/src/sage/graphs/generators/families.py
+++ b/src/sage/graphs/generators/families.py
@@ -1076,11 +1076,11 @@ def CirculantGraph(n, adjacency):
     EXAMPLES: Compare plotting using the predefined layout and
     networkx::
 
-        sage: import networkx
-        sage: n = networkx.cycle_graph(23)
-        sage: spring23 = Graph(n)
+        sage: import networkx                                                           # optional - networkx
+        sage: n = networkx.cycle_graph(23)                                              # optional - networkx
+        sage: spring23 = Graph(n)                                                       # optional - networkx
         sage: posdict23 = graphs.CirculantGraph(23,2)
-        sage: spring23.show() # long time
+        sage: spring23.show() # long time                                               # optional - networkx
         sage: posdict23.show() # long time
 
     We next view many cycle graphs as a Sage graphics array. First we
@@ -3409,29 +3409,29 @@ def WheelGraph(n):
 
     Next, using the spring-layout algorithm::
 
-        sage: import networkx
+        sage: import networkx                                                           # optional - networkx
         sage: g = []
         sage: j = []
-        sage: for i in range(9):
+        sage: for i in range(9):                                                        # optional - networkx
         ....:  spr = networkx.wheel_graph(i+3)
         ....:  k = Graph(spr)
         ....:  g.append(k)
         ...
-        sage: for i in range(3):
+        sage: for i in range(3):                                                        # optional - networkx
         ....:  n = []
         ....:  for m in range(3):
         ....:      n.append(g[3*i + m].plot(vertex_size=50, vertex_labels=False))
         ....:  j.append(n)
         ...
-        sage: G = graphics_array(j)
-        sage: G.show() # long time
+        sage: G = graphics_array(j)                                                     # optional - networkx
+        sage: G.show() # long time                                                      # optional - networkx
 
     Compare the plotting::
 
-        sage: n = networkx.wheel_graph(23)
-        sage: spring23 = Graph(n)
+        sage: n = networkx.wheel_graph(23)                                              # optional - networkx
+        sage: spring23 = Graph(n)                                                       # optional - networkx
         sage: posdict23 = graphs.WheelGraph(23)
-        sage: spring23.show() # long time
+        sage: spring23.show() # long time                                               # optional - networkx
         sage: posdict23.show() # long time
     """
     from sage.graphs.generators.basic import CycleGraph
diff --git a/src/sage/graphs/generators/platonic_solids.py b/src/sage/graphs/generators/platonic_solids.py
index f6244d587ea..536a5f155e6 100644
--- a/src/sage/graphs/generators/platonic_solids.py
+++ b/src/sage/graphs/generators/platonic_solids.py
@@ -43,24 +43,24 @@ def TetrahedralGraph():
 
     The following example requires networkx::
 
-        sage: import networkx as NX
+        sage: import networkx as NX                                                     # optional - networkx
 
     Compare this Tetrahedral, Wheel(4), Complete(4), and the Tetrahedral plotted
     with the spring-layout algorithm below in a Sage graphics array::
 
         sage: tetra_pos = graphs.TetrahedralGraph()
-        sage: tetra_spring = Graph(NX.tetrahedral_graph())
+        sage: tetra_spring = Graph(NX.tetrahedral_graph())                              # optional - networkx
         sage: wheel = graphs.WheelGraph(4)
         sage: complete = graphs.CompleteGraph(4)
-        sage: g = [tetra_pos, tetra_spring, wheel, complete]
+        sage: g = [tetra_pos, tetra_spring, wheel, complete]                            # optional - networkx
         sage: j = []
-        sage: for i in range(2):
+        sage: for i in range(2):                                                        # optional - networkx
         ....:     n = []
         ....:     for m in range(2):
         ....:         n.append(g[i + m].plot(vertex_size=50, vertex_labels=False))
         ....:     j.append(n)
-        sage: G = graphics_array(j)
-        sage: G.show()  # long time
+        sage: G = graphics_array(j)                                                     # optional - networkx
+        sage: G.show()  # long time                                                     # optional - networkx
     """
     edges = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]
     pos = {0: (0, 0),
diff --git a/src/sage/graphs/generators/smallgraphs.py b/src/sage/graphs/generators/smallgraphs.py
index 00ce30403bb..8a4a72b13f7 100644
--- a/src/sage/graphs/generators/smallgraphs.py
+++ b/src/sage/graphs/generators/smallgraphs.py
@@ -1804,9 +1804,9 @@ def ChvatalGraph():
 
     TESTS::
 
-        sage: import networkx
+        sage: import networkx                                                           # optional - networkx
         sage: G = graphs.ChvatalGraph()
-        sage: G.is_isomorphic(Graph(networkx.chvatal_graph()))
+        sage: G.is_isomorphic(Graph(networkx.chvatal_graph()))                          # optional - networkx
         True
     """
     edges = {0: [1, 4, 6, 9], 1: [2, 5, 7], 2: [3, 6, 8], 3: [4, 7, 9],
@@ -2835,9 +2835,9 @@ def HeawoodGraph():
 
     TESTS::
 
-        sage: import networkx
+        sage: import networkx                                                           # optional - networkx
         sage: G = graphs.HeawoodGraph()
-        sage: G.is_isomorphic(Graph(networkx.heawood_graph()))
+        sage: G.is_isomorphic(Graph(networkx.heawood_graph()))                          # optional - networkx
         True
     """
     edges = {0: [1, 5, 13], 1: [2, 10], 2: [3, 7], 3: [4, 12], 4: [5, 9],
@@ -3294,9 +3294,9 @@ def KrackhardtKiteGraph():
 
     TESTS::
 
-        sage: import networkx
+        sage: import networkx                                                           # optional - networkx
         sage: G = graphs.KrackhardtKiteGraph()
-        sage: G.is_isomorphic(Graph(networkx.krackhardt_kite_graph()))
+        sage: G.is_isomorphic(Graph(networkx.krackhardt_kite_graph()))                  # optional - networkx
         True
     """
     edges = {0: [1, 2, 3, 5], 1: [3, 4, 6], 2: [3, 5], 3: [4, 5, 6],
diff --git a/src/sage/graphs/generic_graph.py b/src/sage/graphs/generic_graph.py
index 29c33406774..d46792f9e07 100644
--- a/src/sage/graphs/generic_graph.py
+++ b/src/sage/graphs/generic_graph.py
@@ -1371,14 +1371,14 @@ def export_to_file(self, filename, format=None, **kwds):
             sage: g = graphs.PetersenGraph()
             sage: filename = tmp_filename(ext=".pajek")
             sage: g.export_to_file(filename)
-            sage: import networkx
-            sage: G_networkx = networkx.read_pajek(filename)
-            sage: Graph(G_networkx).is_isomorphic(g)
+            sage: import networkx                                                       # optional - networkx
+            sage: G_networkx = networkx.read_pajek(filename)                            # optional - networkx
+            sage: Graph(G_networkx).is_isomorphic(g)                                    # optional - networkx
             True
             sage: filename = tmp_filename(ext=".edgelist")
             sage: g.export_to_file(filename, data=False)
-            sage: h = Graph(networkx.read_edgelist(filename))
-            sage: g.is_isomorphic(h)
+            sage: h = Graph(networkx.read_edgelist(filename))                           # optional - networkx
+            sage: g.is_isomorphic(h)                                                    # optional - networkx
             True
 
         TESTS::
diff --git a/src/sage/graphs/graph.py b/src/sage/graphs/graph.py
index 266bfd09e9d..12657a226c3 100644
--- a/src/sage/graphs/graph.py
+++ b/src/sage/graphs/graph.py
@@ -112,10 +112,10 @@
 
    ::
 
-       sage: import networkx
-       sage: K = networkx.complete_bipartite_graph(12,7)
-       sage: G = Graph(K)
-       sage: G.degree()
+       sage: import networkx                                                            # optional - networkx
+       sage: K = networkx.complete_bipartite_graph(12,7)                                # optional - networkx
+       sage: G = Graph(K)                                                               # optional - networkx
+       sage: G.degree()                                                                 # optional - networkx
        [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 12, 12, 12, 12, 12, 12, 12]
 
 - graph6 or sparse6 format:
@@ -800,16 +800,16 @@ class Graph(GenericGraph):
 
     #. A NetworkX MultiGraph::
 
-          sage: import networkx
-          sage: g = networkx.MultiGraph({0:[1,2,3], 2:[4]})
-          sage: Graph(g)
+          sage: import networkx                                                         # optional - networkx
+          sage: g = networkx.MultiGraph({0:[1,2,3], 2:[4]})                             # optional - networkx
+          sage: Graph(g)                                                                # optional - networkx
           Multi-graph on 5 vertices
 
     #. A NetworkX graph::
 
-           sage: import networkx
-           sage: g = networkx.Graph({0:[1,2,3], 2:[4]})
-           sage: DiGraph(g)
+           sage: import networkx                                                        # optional - networkx
+           sage: g = networkx.Graph({0:[1,2,3], 2:[4]})                                 # optional - networkx
+           sage: DiGraph(g)                                                             # optional - networkx
            Digraph on 5 vertices
 
     #. An igraph Graph (see also
diff --git a/src/sage/graphs/graph_generators_pyx.pyx b/src/sage/graphs/graph_generators_pyx.pyx
index 8b5b68b7d26..716f4e5975a 100644
--- a/src/sage/graphs/graph_generators_pyx.pyx
+++ b/src/sage/graphs/graph_generators_pyx.pyx
@@ -55,8 +55,8 @@ def RandomGNP(n, p, bint directed=False, bint loops=False, seed=None):
 
     TESTS::
 
-        sage: from numpy import mean
-        sage: abs(mean([RandomGNP(200, .2).density() for i in range(30)]) - .2) < .001
+        sage: from numpy import mean                                                    # optional - numpy
+        sage: abs(mean([RandomGNP(200, .2).density() for i in range(30)]) - .2) < .001  # optional - numpy
         True
         sage: RandomGNP(150, .2, loops=True)
         Traceback (most recent call last):
diff --git a/src/sage/graphs/graph_input.py b/src/sage/graphs/graph_input.py
index fe1de72dcc5..1c952fcceae 100644
--- a/src/sage/graphs/graph_input.py
+++ b/src/sage/graphs/graph_input.py
@@ -614,42 +614,42 @@ def from_networkx_graph(G, gnx, weighted=None, loops=None, multiedges=None,
     Feeding a :class:`Graph` with a NetworkX ``Graph``::
 
         sage: from sage.graphs.graph_input import from_networkx_graph
-        sage: import networkx
+        sage: import networkx                                                           # optional - networkx
         sage: G = Graph()
-        sage: _ = gnx = networkx.Graph()
-        sage: _ = gnx.add_edge(0, 1)
-        sage: _ = gnx.add_edge(1, 2)
-        sage: from_networkx_graph(G, gnx)
-        sage: G.edges(sort=True, labels=False)
+        sage: _ = gnx = networkx.Graph()                                                # optional - networkx
+        sage: _ = gnx.add_edge(0, 1)                                                    # optional - networkx
+        sage: _ = gnx.add_edge(1, 2)                                                    # optional - networkx
+        sage: from_networkx_graph(G, gnx)                                               # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1), (1, 2)]
 
     Feeding a :class:`Graph` with a NetworkX ``MultiGraph``::
 
         sage: G = Graph()
-        sage: gnx = networkx.MultiGraph()
-        sage: _ = gnx.add_edge(0, 1)
-        sage: _ = gnx.add_edge(0, 1)
-        sage: from_networkx_graph(G, gnx)
-        sage: G.edges(sort=True, labels=False)
+        sage: gnx = networkx.MultiGraph()                                               # optional - networkx
+        sage: _ = gnx.add_edge(0, 1)                                                    # optional - networkx
+        sage: _ = gnx.add_edge(0, 1)                                                    # optional - networkx
+        sage: from_networkx_graph(G, gnx)                                               # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1), (0, 1)]
         sage: G = Graph()
-        sage: from_networkx_graph(G, gnx, multiedges=False)
-        sage: G.edges(sort=True, labels=False)
+        sage: from_networkx_graph(G, gnx, multiedges=False)                             # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1)]
 
     When feeding a :class:`Graph` `G` with a NetworkX ``DiGraph`` `D`, `G` has
     one edge `(u, v)` whenever `D` has arc `(u, v)` or `(v, u)` or both::
 
         sage: G = Graph()
-        sage: D = networkx.DiGraph()
-        sage: _ = D.add_edge(0, 1)
-        sage: from_networkx_graph(G, D)
-        sage: G.edges(sort=True, labels=False)
+        sage: D = networkx.DiGraph()                                                    # optional - networkx
+        sage: _ = D.add_edge(0, 1)                                                      # optional - networkx
+        sage: from_networkx_graph(G, D)                                                 # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1)]
         sage: G = Graph()
-        sage: _ = D.add_edge(1, 0)
-        sage: from_networkx_graph(G, D)
-        sage: G.edges(sort=True, labels=False)
+        sage: _ = D.add_edge(1, 0)                                                      # optional - networkx
+        sage: from_networkx_graph(G, D)                                                 # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1)]
 
     When feeding a :class:`Graph` `G` with a NetworkX ``MultiDiGraph`` `D`, the
@@ -657,50 +657,50 @@ def from_networkx_graph(G, gnx, weighted=None, loops=None, multiedges=None,
     of arcs `(u, v)` and the number of arcs `(v, u)` in D`::
 
         sage: G = Graph()
-        sage: D = networkx.MultiDiGraph()
-        sage: _ = D.add_edge(0, 1)
-        sage: _ = D.add_edge(1, 0)
-        sage: _ = D.add_edge(1, 0)
-        sage: D.edges()
+        sage: D = networkx.MultiDiGraph()                                               # optional - networkx
+        sage: _ = D.add_edge(0, 1)                                                      # optional - networkx
+        sage: _ = D.add_edge(1, 0)                                                      # optional - networkx
+        sage: _ = D.add_edge(1, 0)                                                      # optional - networkx
+        sage: D.edges()                                                                 # optional - networkx
         OutMultiEdgeDataView([(0, 1), (1, 0), (1, 0)])
-        sage: from_networkx_graph(G, D)
-        sage: G.edges(sort=True, labels=False)
+        sage: from_networkx_graph(G, D)                                                 # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1), (0, 1)]
 
     Feeding a :class:`DiGraph` with a NetworkX ``DiGraph``::
 
         sage: from sage.graphs.graph_input import from_networkx_graph
-        sage: import networkx
+        sage: import networkx                                                           # optional - networkx
         sage: G = DiGraph()
-        sage: _ = gnx = networkx.DiGraph()
-        sage: _ = gnx.add_edge(0, 1)
-        sage: _ = gnx.add_edge(1, 2)
-        sage: from_networkx_graph(G, gnx)
-        sage: G.edges(sort=True, labels=False)
+        sage: _ = gnx = networkx.DiGraph()                                              # optional - networkx
+        sage: _ = gnx.add_edge(0, 1)                                                    # optional - networkx
+        sage: _ = gnx.add_edge(1, 2)                                                    # optional - networkx
+        sage: from_networkx_graph(G, gnx)                                               # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1), (1, 2)]
 
     Feeding a :class:`DiGraph` with a NetworkX ``MultiDiGraph``::
 
         sage: G = DiGraph()
-        sage: gnx = networkx.MultiDiGraph()
-        sage: _ = gnx.add_edge(0, 1)
-        sage: _ = gnx.add_edge(0, 1)
-        sage: from_networkx_graph(G, gnx)
-        sage: G.edges(sort=True, labels=False)
+        sage: gnx = networkx.MultiDiGraph()                                             # optional - networkx
+        sage: _ = gnx.add_edge(0, 1)                                                    # optional - networkx
+        sage: _ = gnx.add_edge(0, 1)                                                    # optional - networkx
+        sage: from_networkx_graph(G, gnx)                                               # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1), (0, 1)]
         sage: G = DiGraph()
-        sage: from_networkx_graph(G, gnx, multiedges=False)
-        sage: G.edges(sort=True, labels=False)
+        sage: from_networkx_graph(G, gnx, multiedges=False)                             # optional - networkx
+        sage: G.edges(sort=True, labels=False)                                          # optional - networkx
         [(0, 1)]
 
     When feeding a :class:`DiGraph` `G` with a NetworkX ``Graph`` `H`, `G` has
     both arcs `(u, v)` and `(v, u)` if `G` has edge `(u, v)`::
 
         sage: G = DiGraph()
-        sage: H = networkx.Graph()
-        sage: _ = H.add_edge(0, 1)
-        sage: from_networkx_graph(G, H)
-        sage: G.edges(labels=False, sort=True)
+        sage: H = networkx.Graph()                                                      # optional - networkx
+        sage: _ = H.add_edge(0, 1)                                                      # optional - networkx
+        sage: from_networkx_graph(G, H)                                                 # optional - networkx
+        sage: G.edges(labels=False, sort=True)                                          # optional - networkx
         [(0, 1), (1, 0)]
 
     When feeding a :class:`DiGraph` `G` with a NetworkX ``MultiGraph`` `H`, `G`
@@ -708,18 +708,18 @@ def from_networkx_graph(G, gnx, weighted=None, loops=None, multiedges=None,
     unless parameter ``multiedges`` is set to ``False``::
 
         sage: G = DiGraph()
-        sage: H = networkx.MultiGraph()
-        sage: _ = H.add_edge(0, 1)
-        sage: _ = H.add_edge(0, 1)
-        sage: _ = H.add_edge(0, 1)
-        sage: H.edges()
+        sage: H = networkx.MultiGraph()                                                 # optional - networkx
+        sage: _ = H.add_edge(0, 1)                                                      # optional - networkx
+        sage: _ = H.add_edge(0, 1)                                                      # optional - networkx
+        sage: _ = H.add_edge(0, 1)                                                      # optional - networkx
+        sage: H.edges()                                                                 # optional - networkx
         MultiEdgeDataView([(0, 1), (0, 1), (0, 1)])
-        sage: from_networkx_graph(G, H)
-        sage: G.edges(labels=False, sort=True)
+        sage: from_networkx_graph(G, H)                                                 # optional - networkx
+        sage: G.edges(labels=False, sort=True)                                          # optional - networkx
         [(0, 1), (0, 1), (0, 1), (1, 0), (1, 0), (1, 0)]
         sage: G = DiGraph()
-        sage: from_networkx_graph(G, H, multiedges=False)
-        sage: G.edges(labels=False, sort=True)
+        sage: from_networkx_graph(G, H, multiedges=False)                               # optional - networkx
+        sage: G.edges(labels=False, sort=True)                                          # optional - networkx
         [(0, 1), (1, 0)]
 
     TESTS:
@@ -736,7 +736,7 @@ def from_networkx_graph(G, gnx, weighted=None, loops=None, multiedges=None,
       ``DiGraph`` or ``MultiDiGraph``::
 
         sage: from sage.graphs.graph_input import from_networkx_graph
-        sage: from_networkx_graph(Graph(), "bar")
+        sage: from_networkx_graph(Graph(), "bar")                                       # optional - networkx
         Traceback (most recent call last):
         ...
         ValueError: the second parameter must be a NetworkX (Multi)(Di)Graph
diff --git a/src/sage/graphs/planarity.pyx b/src/sage/graphs/planarity.pyx
index b5a78f0bf14..839039a2bde 100644
--- a/src/sage/graphs/planarity.pyx
+++ b/src/sage/graphs/planarity.pyx
@@ -72,17 +72,17 @@ def is_planar(g, kuratowski=False, set_pos=False, set_embedding=False, circular=
     vertices.  In fact, to try to track down a segfault, we do it
     twice. ::
 
-        sage: import networkx.generators.atlas  # long time
-        sage: atlas_graphs = [Graph(i) for i in networkx.generators.atlas.graph_atlas_g()] # long time
-        sage: a = [i for i in [1..1252] if atlas_graphs[i].is_planar()] # long time
-        sage: b = [i for i in [1..1252] if atlas_graphs[i].is_planar()] # long time
-        sage: a == b # long time
+        sage: import networkx.generators.atlas  # long time                                             # optional - networkx
+        sage: atlas_graphs = [Graph(i) for i in networkx.generators.atlas.graph_atlas_g()] # long time  # optional - networkx
+        sage: a = [i for i in [1..1252] if atlas_graphs[i].is_planar()] # long time                     # optional - networkx
+        sage: b = [i for i in [1..1252] if atlas_graphs[i].is_planar()] # long time                     # optional - networkx
+        sage: a == b # long time                                                                        # optional - networkx
         True
 
     There were some problems with ``set_pos`` stability in the past,
     so let's check if this runs without exception::
 
-        sage: for i, g in enumerate(atlas_graphs):           # long time
+        sage: for i, g in enumerate(atlas_graphs):           # long time                                # optional - networkx
         ....:     if (not g.is_connected() or i == 0):
         ....:         continue
         ....:     _ = g.is_planar(set_embedding=True, set_pos=True)