We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
regionsprops_table is not mentioned in the doc
We should use it in an existing example, or add a new example.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `doc/examples/segmentation/plot_regionprops.py`
Content:
```
1 """
2 =========================
3 Measure region properties
4 =========================
5 
6 This example shows how to measure properties of labelled image regions.
7 
8 """
9 import math
10 import matplotlib.pyplot as plt
11 import numpy as np
12 
13 from skimage.draw import ellipse
14 from skimage.measure import label, regionprops
15 from skimage.transform import rotate
16 
17 
18 image = np.zeros((600, 600))
19 
20 rr, cc = ellipse(300, 350, 100, 220)
21 image[rr, cc] = 1
22 
23 image = rotate(image, angle=15, order=0)
24 
25 label_img = label(image)
26 regions = regionprops(label_img)
27 
28 fig, ax = plt.subplots()
29 ax.imshow(image, cmap=plt.cm.gray)
30 
31 for props in regions:
32     y0, x0 = props.centroid
33     orientation = props.orientation
34     x1 = x0 + math.cos(orientation) * 0.5 * props.major_axis_length
35     y1 = y0 - math.sin(orientation) * 0.5 * props.major_axis_length
36     x2 = x0 - math.sin(orientation) * 0.5 * props.minor_axis_length
37     y2 = y0 - math.cos(orientation) * 0.5 * props.minor_axis_length
38 
39     ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)
40     ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)
41     ax.plot(x0, y0, '.g', markersize=15)
42 
43     minr, minc, maxr, maxc = props.bbox
44     bx = (minc, maxc, maxc, minc, minc)
45     by = (minr, minr, maxr, maxr, minr)
46     ax.plot(bx, by, '-b', linewidth=2.5)
47 
48 ax.axis((0, 600, 600, 0))
49 plt.show()
50
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/doc/examples/segmentation/plot_regionprops.py b/doc/examples/segmentation/plot_regionprops.py
--- a/doc/examples/segmentation/plot_regionprops.py
+++ b/doc/examples/segmentation/plot_regionprops.py
@@ -3,15 +3,17 @@
 Measure region properties
 =========================
 
-This example shows how to measure properties of labelled image regions.
+This example shows how to measure properties of labelled image regions. We
+analyze an image with two ellipses.
 
 """
 import math
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 
 from skimage.draw import ellipse
-from skimage.measure import label, regionprops
+from skimage.measure import label, regionprops, regionprops_table
 from skimage.transform import rotate
 
 
@@ -22,19 +24,27 @@
 
 image = rotate(image, angle=15, order=0)
 
+rr, cc = ellipse(100, 100, 60, 50)
+image[rr, cc] = 1
+
 label_img = label(image)
 regions = regionprops(label_img)
 
+#####################################################################
+# We use the :py:func:`skimage.measure.regionprops` result to draw certain
+# properties on each region. For example, in red, we plot the major and minor
+# axes of each ellipse.
+
 fig, ax = plt.subplots()
 ax.imshow(image, cmap=plt.cm.gray)
 
 for props in regions:
     y0, x0 = props.centroid
     orientation = props.orientation
-    x1 = x0 + math.cos(orientation) * 0.5 * props.major_axis_length
-    y1 = y0 - math.sin(orientation) * 0.5 * props.major_axis_length
-    x2 = x0 - math.sin(orientation) * 0.5 * props.minor_axis_length
-    y2 = y0 - math.cos(orientation) * 0.5 * props.minor_axis_length
+    x1 = x0 + math.cos(orientation) * 0.5 * props.minor_axis_length
+    y1 = y0 - math.sin(orientation) * 0.5 * props.minor_axis_length
+    x2 = x0 - math.sin(orientation) * 0.5 * props.major_axis_length
+    y2 = y0 - math.cos(orientation) * 0.5 * props.major_axis_length
 
     ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)
     ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)
@@ -47,3 +57,22 @@
 
 ax.axis((0, 600, 600, 0))
 plt.show()
+
+#####################################################################
+# We use the :py:func:`skimage.measure.regionprops_table` to compute
+# (selected) properties for each region. Note that
+# ``skimage.measure.regionprops_table`` actually computes the properties,
+# whereas ``skimage.measure.regionprops`` computes them when they come in use
+# (lazy evaluation).
+
+props = regionprops_table(label_img, properties=('centroid',
+                                                 'orientation',
+                                                 'major_axis_length',
+                                                 'minor_axis_length'))
+
+#####################################################################
+# We now display a table of these selected properties (one region per row),
+# the ``skimage.measure.regionprops_table`` result being a pandas-compatible
+# dict.
+
+pd.DataFrame(props)

{"golden_diff": "diff --git a/doc/examples/segmentation/plot_regionprops.py b/doc/examples/segmentation/plot_regionprops.py\n--- a/doc/examples/segmentation/plot_regionprops.py\n+++ b/doc/examples/segmentation/plot_regionprops.py\n@@ -3,15 +3,17 @@\n Measure region properties\n =========================\n \n-This example shows how to measure properties of labelled image regions.\n+This example shows how to measure properties of labelled image regions. We\n+analyze an image with two ellipses.\n \n \"\"\"\n import math\n import matplotlib.pyplot as plt\n import numpy as np\n+import pandas as pd\n \n from skimage.draw import ellipse\n-from skimage.measure import label, regionprops\n+from skimage.measure import label, regionprops, regionprops_table\n from skimage.transform import rotate\n \n \n@@ -22,19 +24,27 @@\n \n image = rotate(image, angle=15, order=0)\n \n+rr, cc = ellipse(100, 100, 60, 50)\n+image[rr, cc] = 1\n+\n label_img = label(image)\n regions = regionprops(label_img)\n \n+#####################################################################\n+# We use the :py:func:`skimage.measure.regionprops` result to draw certain\n+# properties on each region. For example, in red, we plot the major and minor\n+# axes of each ellipse.\n+\n fig, ax = plt.subplots()\n ax.imshow(image, cmap=plt.cm.gray)\n \n for props in regions:\n     y0, x0 = props.centroid\n     orientation = props.orientation\n-    x1 = x0 + math.cos(orientation) * 0.5 * props.major_axis_length\n-    y1 = y0 - math.sin(orientation) * 0.5 * props.major_axis_length\n-    x2 = x0 - math.sin(orientation) * 0.5 * props.minor_axis_length\n-    y2 = y0 - math.cos(orientation) * 0.5 * props.minor_axis_length\n+    x1 = x0 + math.cos(orientation) * 0.5 * props.minor_axis_length\n+    y1 = y0 - math.sin(orientation) * 0.5 * props.minor_axis_length\n+    x2 = x0 - math.sin(orientation) * 0.5 * props.major_axis_length\n+    y2 = y0 - math.cos(orientation) * 0.5 * props.major_axis_length\n \n     ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)\n     ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)\n@@ -47,3 +57,22 @@\n \n ax.axis((0, 600, 600, 0))\n plt.show()\n+\n+#####################################################################\n+# We use the :py:func:`skimage.measure.regionprops_table` to compute\n+# (selected) properties for each region. Note that\n+# ``skimage.measure.regionprops_table`` actually computes the properties,\n+# whereas ``skimage.measure.regionprops`` computes them when they come in use\n+# (lazy evaluation).\n+\n+props = regionprops_table(label_img, properties=('centroid',\n+                                                 'orientation',\n+                                                 'major_axis_length',\n+                                                 'minor_axis_length'))\n+\n+#####################################################################\n+# We now display a table of these selected properties (one region per row),\n+# the ``skimage.measure.regionprops_table`` result being a pandas-compatible\n+# dict.\n+\n+pd.DataFrame(props)\n", "issue": "regionsprops_table is not mentioned in the doc\nWe should use it in an existing example, or add a new example.\n", "before_files": [{"content": "\"\"\"\n=========================\nMeasure region properties\n=========================\n\nThis example shows how to measure properties of labelled image regions.\n\n\"\"\"\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom skimage.draw import ellipse\nfrom skimage.measure import label, regionprops\nfrom skimage.transform import rotate\n\n\nimage = np.zeros((600, 600))\n\nrr, cc = ellipse(300, 350, 100, 220)\nimage[rr, cc] = 1\n\nimage = rotate(image, angle=15, order=0)\n\nlabel_img = label(image)\nregions = regionprops(label_img)\n\nfig, ax = plt.subplots()\nax.imshow(image, cmap=plt.cm.gray)\n\nfor props in regions:\n    y0, x0 = props.centroid\n    orientation = props.orientation\n    x1 = x0 + math.cos(orientation) * 0.5 * props.major_axis_length\n    y1 = y0 - math.sin(orientation) * 0.5 * props.major_axis_length\n    x2 = x0 - math.sin(orientation) * 0.5 * props.minor_axis_length\n    y2 = y0 - math.cos(orientation) * 0.5 * props.minor_axis_length\n\n    ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)\n    ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)\n    ax.plot(x0, y0, '.g', markersize=15)\n\n    minr, minc, maxr, maxc = props.bbox\n    bx = (minc, maxc, maxc, minc, minc)\n    by = (minr, minr, maxr, maxr, minr)\n    ax.plot(bx, by, '-b', linewidth=2.5)\n\nax.axis((0, 600, 600, 0))\nplt.show()\n", "path": "doc/examples/segmentation/plot_regionprops.py"}], "after_files": [{"content": "\"\"\"\n=========================\nMeasure region properties\n=========================\n\nThis example shows how to measure properties of labelled image regions. We\nanalyze an image with two ellipses.\n\n\"\"\"\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom skimage.draw import ellipse\nfrom skimage.measure import label, regionprops, regionprops_table\nfrom skimage.transform import rotate\n\n\nimage = np.zeros((600, 600))\n\nrr, cc = ellipse(300, 350, 100, 220)\nimage[rr, cc] = 1\n\nimage = rotate(image, angle=15, order=0)\n\nrr, cc = ellipse(100, 100, 60, 50)\nimage[rr, cc] = 1\n\nlabel_img = label(image)\nregions = regionprops(label_img)\n\n#####################################################################\n# We use the :py:func:`skimage.measure.regionprops` result to draw certain\n# properties on each region. For example, in red, we plot the major and minor\n# axes of each ellipse.\n\nfig, ax = plt.subplots()\nax.imshow(image, cmap=plt.cm.gray)\n\nfor props in regions:\n    y0, x0 = props.centroid\n    orientation = props.orientation\n    x1 = x0 + math.cos(orientation) * 0.5 * props.minor_axis_length\n    y1 = y0 - math.sin(orientation) * 0.5 * props.minor_axis_length\n    x2 = x0 - math.sin(orientation) * 0.5 * props.major_axis_length\n    y2 = y0 - math.cos(orientation) * 0.5 * props.major_axis_length\n\n    ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)\n    ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)\n    ax.plot(x0, y0, '.g', markersize=15)\n\n    minr, minc, maxr, maxc = props.bbox\n    bx = (minc, maxc, maxc, minc, minc)\n    by = (minr, minr, maxr, maxr, minr)\n    ax.plot(bx, by, '-b', linewidth=2.5)\n\nax.axis((0, 600, 600, 0))\nplt.show()\n\n#####################################################################\n# We use the :py:func:`skimage.measure.regionprops_table` to compute\n# (selected) properties for each region. Note that\n# ``skimage.measure.regionprops_table`` actually computes the properties,\n# whereas ``skimage.measure.regionprops`` computes them when they come in use\n# (lazy evaluation).\n\nprops = regionprops_table(label_img, properties=('centroid',\n                                                 'orientation',\n                                                 'major_axis_length',\n                                                 'minor_axis_length'))\n\n#####################################################################\n# We now display a table of these selected properties (one region per row),\n# the ``skimage.measure.regionprops_table`` result being a pandas-compatible\n# dict.\n\npd.DataFrame(props)\n", "path": "doc/examples/segmentation/plot_regionprops.py"}]}