diff --git a/.travis.yml b/.travis.yml
index 2bd4d5ba9ebb..a8e8ff755515 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -54,7 +54,7 @@ matrix:
     - python: 2.7
       env: MOCK=mock NUMPY=numpy==1.7.1 PANDAS=pandas
     - python: 2.7
-      env: BUILD_DOCS=true
+      env: NUMPY=numpy==1.7.1 BUILD_DOCS=true
     - python: 3.4
       env: PYTHON_ARGS=-OO
     - python: 3.5
diff --git a/examples/misc/rec_join_demo.py b/examples/misc/rec_join_demo.py
deleted file mode 100644
index f91e8f2b8200..000000000000
--- a/examples/misc/rec_join_demo.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from __future__ import print_function
-import numpy as np
-import matplotlib.mlab as mlab
-import matplotlib.cbook as cbook
-
-datafile = cbook.get_sample_data('msft.csv', asfileobj=False)
-print('loading', datafile)
-r = mlab.csv2rec(datafile)
-
-r.sort()
-r1 = r[-10:]
-
-# Create a new array
-r2 = np.empty(12, dtype=[('date', '|O4'), ('high', float), ('marker', float)])
-r2 = r2.view(np.recarray)
-r2.date = r.date[-17:-5]
-r2.high = r.high[-17:-5]
-r2.marker = np.arange(12)
-
-print("r1:")
-print(mlab.rec2txt(r1))
-print("r2:")
-print(mlab.rec2txt(r2))
-
-defaults = {'marker': -1, 'close': np.NaN, 'low': -4444.}
-
-for s in ('inner', 'outer', 'leftouter'):
-    rec = mlab.rec_join(['date', 'high'], r1, r2,
-                        jointype=s, defaults=defaults)
-    print("\n%sjoin :\n%s" % (s, mlab.rec2txt(rec)))
diff --git a/examples/showcase/mandelbrot.py b/examples/showcase/mandelbrot.py
index 877964b80ed3..ed43a087b18f 100644
--- a/examples/showcase/mandelbrot.py
+++ b/examples/showcase/mandelbrot.py
@@ -14,11 +14,11 @@
 
 
 def mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon=2.0):
-    X = np.linspace(xmin, xmax, xn, dtype=np.float32)
-    Y = np.linspace(ymin, ymax, yn, dtype=np.float32)
-    C = X + Y[:, None]*1j
-    N = np.zeros(C.shape, dtype=int)
-    Z = np.zeros(C.shape, np.complex64)
+    X = np.linspace(xmin, xmax, xn).astype(np.float32)
+    Y = np.linspace(ymin, ymax, yn).astype(np.float32)
+    C = X + Y[:, None] * 1j
+    N = np.zeros_like(C, dtype=int)
+    Z = np.zeros_like(C)
     for n in range(maxiter):
         I = np.less(abs(Z), horizon)
         N[I] = n