diff --git a/skimage/feature/_canny.py b/skimage/feature/_canny.py
index e98b88e2..4788b94d 100644
--- a/skimage/feature/_canny.py
+++ b/skimage/feature/_canny.py
@@ -109,15 +109,15 @@ def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
 
     Examples
     --------
-    >>> from skimage import filters
+    >>> from skimage import feature
     >>> # Generate noisy image of a square
     >>> im = np.zeros((256, 256))
     >>> im[64:-64, 64:-64] = 1
     >>> im += 0.2 * np.random.rand(*im.shape)
     >>> # First trial with the Canny filter, with the default smoothing
-    >>> edges1 = filter.canny(im)
+    >>> edges1 = feature.canny(im)
     >>> # Increase the smoothing for better results
-    >>> edges2 = filter.canny(im, sigma=3)
+    >>> edges2 = feature.canny(im, sigma=3)
     """
 
     #
diff --git a/skimage/measure/_ccomp.pyx b/skimage/measure/_ccomp.pyx
index a9c4ab55..66c7f75f 100644
--- a/skimage/measure/_ccomp.pyx
+++ b/skimage/measure/_ccomp.pyx
@@ -409,18 +409,19 @@ def label(input, neighbors=None, background=None, return_num=False,
 
     Examples
     --------
+    >>> import numpy as np
     >>> x = np.eye(3).astype(int)
     >>> print(x)
     [[1 0 0]
      [0 1 0]
      [0 0 1]]
-
-    >>> print(m.label(x, connectivity=1))
+    >>> from skimage.measure import label
+    >>> print(label(x, neighbors=4))
     [[0 1 1]
      [2 3 1]
      [2 2 4]]
 
-    >>> print(m.label(x, connectivity=2))
+    >>> print(label(x, neighbors=8))
     [[0 1 1]
      [1 0 1]
      [1 1 0]]
@@ -429,7 +430,7 @@ def label(input, neighbors=None, background=None, return_num=False,
     ...               [1, 1, 5],
     ...               [0, 0, 0]])
 
-    >>> print(m.label(x, background=0))
+    >>> print(label(x, background=0))
     [[ 0 -1 -1]
      [ 0  0  1]
      [-1 -1 -1]]
diff --git a/skimage/restoration/_denoise.py b/skimage/restoration/_denoise.py
index a73f50e3..f8401808 100644
--- a/skimage/restoration/_denoise.py
+++ b/skimage/restoration/_denoise.py
@@ -314,7 +314,7 @@ def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200,
 
     >>> from skimage import color, data
     >>> img = color.rgb2gray(data.astronaut())[:50, :50]
-    >>> img += 0.5 * img.std() * np.random.randn(*astro.shape)
+    >>> img += 0.5 * img.std() * np.random.randn(*img.shape)
     >>> denoised_img = denoise_tv_chambolle(img, weight=60)
 
     3D example on synthetic data:
diff --git a/skimage/transform/_geometric.py b/skimage/transform/_geometric.py
index e6e26930..78387cf1 100644
--- a/skimage/transform/_geometric.py
+++ b/skimage/transform/_geometric.py
@@ -1111,8 +1111,8 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
 
     >>> scale = 0.1
     >>> output_shape = (scale * cube_shape).astype(int)
-    >>> coords0, coords1, coords2 = \
-    ...     np.mgrid[:output_shape[0], :output_shape[1], :output_shape[2]]
+    >>> coords0, coords1, coords2 = np.mgrid[:output_shape[0],
+    ...                    :output_shape[1], :output_shape[2]]
     >>> coords = np.array([coords0, coords1, coords2])
 
     Assume that the cube contains spatial data, where the first array element