--- networkx/algorithms/centrality/eigenvector.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/algorithms/centrality/eigenvector.py	2020-03-09 11:41:34.375972965 -0600
@@ -224,6 +224,7 @@ def eigenvector_centrality_numpy(G, weig
        Networks: An Introduction.
        Oxford University Press, USA, 2010, pp. 169.
     """
+    import numpy as np
     import scipy as sp
     from scipy.sparse import linalg
     if len(G) == 0:
@@ -234,7 +235,7 @@ def eigenvector_centrality_numpy(G, weig
     eigenvalue, eigenvector = linalg.eigs(M.T, k=1, which='LR',
                                           maxiter=max_iter, tol=tol)
     largest = eigenvector.flatten().real
-    norm = sp.sign(largest.sum()) * sp.linalg.norm(largest)
+    norm = np.sign(largest.sum()) * sp.linalg.norm(largest)
     return dict(zip(G, largest / norm))
 
 
--- networkx/algorithms/centrality/subgraph_alg.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/algorithms/centrality/subgraph_alg.py	2020-03-09 11:42:44.854815882 -0600
@@ -226,6 +226,7 @@ def communicability_betweenness_centrali
     >>> G = nx.Graph([(0,1),(1,2),(1,5),(5,4),(2,4),(2,3),(4,3),(3,6)])
     >>> cbc = nx.communicability_betweenness_centrality(G)
     """
+    import numpy
     import scipy
     import scipy.linalg
     nodelist = list(G)  # ordering of nodes in matrix
@@ -247,7 +248,7 @@ def communicability_betweenness_centrali
         # sum with row/col of node v and diag set to zero
         B[i, :] = 0
         B[:, i] = 0
-        B -= scipy.diag(scipy.diag(B))
+        B -= numpy.diag(numpy.diag(B))
         cbc[v] = float(B.sum())
         # put row and col back
         A[i, :] = row
--- networkx/algorithms/link_analysis/hits_alg.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/algorithms/link_analysis/hits_alg.py	2020-03-09 11:43:48.909764257 -0600
@@ -286,7 +286,7 @@ def hits_scipy(G, max_iter=100, tol=1.0e
     M = nx.to_scipy_sparse_matrix(G, nodelist=list(G))
     (n, m) = M.shape  # should be square
     A = M.T * M  # authority matrix
-    x = scipy.ones((n, 1)) / n  # initial guess
+    x = np.ones((n, 1)) / n  # initial guess
     # power iteration on authority matrix
     i = 0
     while True:
@@ -294,7 +294,7 @@ def hits_scipy(G, max_iter=100, tol=1.0e
         x = A * x
         x = x / x.max()
         # check convergence, l1 norm
-        err = scipy.absolute(x - xlast).sum()
+        err = np.absolute(x - xlast).sum()
         if err < tol:
             break
         if i > max_iter:
--- networkx/algorithms/link_analysis/pagerank_alg.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/algorithms/link_analysis/pagerank_alg.py	2020-03-09 11:46:41.977922907 -0600
@@ -420,6 +420,7 @@ def pagerank_scipy(G, alpha=0.85, person
        The PageRank citation ranking: Bringing order to the Web. 1999
        http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf
     """
+    import numpy
     import scipy.sparse
 
     N = len(G)
@@ -429,23 +430,23 @@ def pagerank_scipy(G, alpha=0.85, person
     nodelist = list(G)
     M = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
                                   dtype=float)
-    S = scipy.array(M.sum(axis=1)).flatten()
+    S = numpy.array(M.sum(axis=1)).flatten()
     S[S != 0] = 1.0 / S[S != 0]
     Q = scipy.sparse.spdiags(S.T, 0, *M.shape, format='csr')
     M = Q * M
 
     # initial vector
     if nstart is None:
-        x = scipy.repeat(1.0 / N, N)
+        x = numpy.repeat(1.0 / N, N)
     else:
-        x = scipy.array([nstart.get(n, 0) for n in nodelist], dtype=float)
+        x = numpy.array([nstart.get(n, 0) for n in nodelist], dtype=float)
         x = x / x.sum()
 
     # Personalization vector
     if personalization is None:
-        p = scipy.repeat(1.0 / N, N)
+        p = numpy.repeat(1.0 / N, N)
     else:
-        p = scipy.array([personalization.get(n, 0) for n in nodelist], dtype=float)
+        p = numpy.array([personalization.get(n, 0) for n in nodelist], dtype=float)
         p = p / p.sum()
 
     # Dangling nodes
@@ -453,10 +454,10 @@ def pagerank_scipy(G, alpha=0.85, person
         dangling_weights = p
     else:
         # Convert the dangling dictionary into an array in nodelist order
-        dangling_weights = scipy.array([dangling.get(n, 0) for n in nodelist],
+        dangling_weights = numpy.array([dangling.get(n, 0) for n in nodelist],
                                        dtype=float)
         dangling_weights /= dangling_weights.sum()
-    is_dangling = scipy.where(S == 0)[0]
+    is_dangling = numpy.where(S == 0)[0]
 
     # power iteration: make up to max_iter iterations
     for _ in range(max_iter):
@@ -464,7 +465,7 @@ def pagerank_scipy(G, alpha=0.85, person
         x = alpha * (x * M + sum(x[is_dangling]) * dangling_weights) + \
             (1 - alpha) * p
         # check convergence, l1 norm
-        err = scipy.absolute(x - xlast).sum()
+        err = numpy.absolute(x - xlast).sum()
         if err < N * tol:
             return dict(zip(nodelist, map(float, x)))
     raise nx.PowerIterationFailedConvergence(max_iter)
--- networkx/drawing/layout.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/drawing/layout.py	2020-03-09 10:37:14.554300047 -0600
@@ -947,7 +947,7 @@ def planar_layout(G, scale=1, center=Non
             raise nx.NetworkXException("G is not planar.")
     pos = nx.combinatorial_embedding_to_pos(embedding)
     node_list = list(embedding)
-    pos = np.row_stack((pos[x] for x in node_list))
+    pos = np.row_stack(list(pos[x] for x in node_list))
     pos = pos.astype(np.float64)
     pos = rescale_layout(pos, scale=scale) + center
     return dict(zip(node_list, pos))
--- networkx/drawing/tests/test_pylab.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/drawing/tests/test_pylab.py	2020-03-09 10:44:23.573357737 -0600
@@ -4,7 +4,7 @@ import itertools
 import pytest
 
 mpl = pytest.importorskip('matplotlib')
-mpl.use('PS', warn=False)
+mpl.use('PS')
 plt = pytest.importorskip('matplotlib.pyplot')
 plt.rcParams['text.usetex'] = False
 
@@ -172,7 +172,7 @@ class TestPylab(object):
         # See Issue #3295
         G = nx.path_graph(3, create_using=nx.MultiDiGraph)
         nx.draw_networkx(G, edgelist=[(0, 1, 0)])
-        nx.draw_networkx(G, edgelist=[(0, 1, 0)], node_size=[10, 20])
+        nx.draw_networkx(G, edgelist=[(0, 1, 0)], node_size=[10, 20, 0])
 
     def test_alpha_iter(self):
         pos = nx.random_layout(self.G)
--- networkx/linalg/laplacianmatrix.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/linalg/laplacianmatrix.py	2020-03-09 11:50:44.872221831 -0600
@@ -116,6 +116,8 @@ def normalized_laplacian_matrix(G, nodel
        Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
        March 2007.
     """
+    import numpy
+    import numpy.lib.scimath
     import scipy
     import scipy.sparse
     if nodelist is None:
@@ -127,8 +129,8 @@ def normalized_laplacian_matrix(G, nodel
     D = scipy.sparse.spdiags(diags, [0], m, n, format='csr')
     L = D - A
     with scipy.errstate(divide='ignore'):
-        diags_sqrt = 1.0 / scipy.sqrt(diags)
-    diags_sqrt[scipy.isinf(diags_sqrt)] = 0
+        diags_sqrt = 1.0 / numpy.lib.scimath.sqrt(diags)
+    diags_sqrt[numpy.isinf(diags_sqrt)] = 0
     DH = scipy.sparse.spdiags(diags_sqrt, [0], m, n, format='csr')
     return DH.dot(L.dot(DH))
 
@@ -196,6 +198,8 @@ def directed_laplacian_matrix(G, nodelis
        Laplacians and the Cheeger inequality for directed graphs.
        Annals of Combinatorics, 9(1), 2005
     """
+    import numpy as np
+    import numpy.lib.scimath
     import scipy as sp
     from scipy.sparse import spdiags, linalg
 
@@ -207,9 +211,9 @@ def directed_laplacian_matrix(G, nodelis
     evals, evecs = linalg.eigs(P.T, k=1)
     v = evecs.flatten().real
     p = v / v.sum()
-    sqrtp = sp.sqrt(p)
+    sqrtp = numpy.lib.scimath.sqrt(p)
     Q = spdiags(sqrtp, [0], n, n) * P * spdiags(1.0 / sqrtp, [0], n, n)
-    I = sp.identity(len(G))
+    I = np.identity(len(G))
 
     return I - (Q + Q.T) / 2.0
 
@@ -329,6 +333,7 @@ def _transition_matrix(G, nodelist=None,
         If walk_type not specified or alpha not in valid range
     """
 
+    import numpy as np
     import scipy as sp
     from scipy.sparse import identity, spdiags
     if walk_type is None:
@@ -344,7 +349,7 @@ def _transition_matrix(G, nodelist=None,
                                   dtype=float)
     n, m = M.shape
     if walk_type in ["random", "lazy"]:
-        DI = spdiags(1.0 / sp.array(M.sum(axis=1).flat), [0], n, n)
+        DI = spdiags(1.0 / np.array(M.sum(axis=1).flat), [0], n, n)
         if walk_type == "random":
             P = DI * M
         else:
@@ -357,7 +362,7 @@ def _transition_matrix(G, nodelist=None,
         # this is using a dense representation
         M = M.todense()
         # add constant to dangling nodes' row
-        dangling = sp.where(M.sum(axis=1) == 0)
+        dangling = np.where(M.sum(axis=1) == 0)
         for d in dangling[0]:
             M[d] = 1.0 / n
         # normalize
--- networkx/readwrite/tests/test_graphml.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/readwrite/tests/test_graphml.py	2020-03-09 11:37:53.114605527 -0600
@@ -878,7 +878,7 @@ class TestWriteGraphML(BaseGraphML):
 
         xml = parse(fh)
         # Children are the key elements, and the graph element
-        children = xml.getroot().getchildren()
+        children = list(xml.getroot())
         assert len(children) == 3
 
         keys = [child.items() for child in children[:2]]
--- networkx/tests/test_convert_pandas.py.orig	2019-10-16 20:03:56.000000000 -0600
+++ networkx/tests/test_convert_pandas.py	2020-03-09 11:51:57.623748599 -0600
@@ -8,7 +8,8 @@ from networkx.testing import assert_node
 
 class TestConvertPandas(object):
     def setup_method(self):
-        self.rng = pd.np.random.RandomState(seed=5)
+        import numpy
+        self.rng = numpy.random.RandomState(seed=5)
         ints = self.rng.randint(1, 11, size=(3, 2))
         a = ['A', 'B', 'C']
         b = ['D', 'A', 'E']