diff --git a/pandas/plotting/_misc.py b/pandas/plotting/_misc.py
index 30c5ba0ed94b6..594b95d1937ea 100644
--- a/pandas/plotting/_misc.py
+++ b/pandas/plotting/_misc.py
@@ -117,8 +117,12 @@ def scatter_matrix(
 
     Examples
     --------
-    >>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
-    >>> scatter_matrix(df, alpha=0.2)
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
+        >>> pd.plotting.scatter_matrix(df, alpha=0.2)
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.scatter_matrix(
@@ -179,24 +183,31 @@ def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
 
     Examples
     --------
+
     .. plot::
         :context: close-figs
 
-        >>> df = pd.DataFrame({
-        ...         'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6,
-        ...                         6.7, 4.6],
-        ...         'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2,
-        ...                        3.3, 3.6],
-        ...         'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4,
-        ...                         5.7, 1.0],
-        ...         'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2,
-        ...                        2.1, 0.2],
-        ...         'Category': ['virginica', 'virginica', 'setosa',
-        ...                      'virginica', 'virginica', 'versicolor',
-        ...                      'versicolor', 'setosa', 'virginica',
-        ...                      'setosa']
-        ...     })
-        >>> rad_viz = pd.plotting.radviz(df, 'Category')  # doctest: +SKIP
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
+        ...         'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
+        ...         'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
+        ...         'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
+        ...         'Category': [
+        ...             'virginica',
+        ...             'virginica',
+        ...             'setosa',
+        ...             'virginica',
+        ...             'virginica',
+        ...             'versicolor',
+        ...             'versicolor',
+        ...             'setosa',
+        ...             'virginica',
+        ...             'setosa'
+        ...         ]
+        ...     }
+        ... )
+        >>> pd.plotting.radviz(df, 'Category')
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.radviz(
@@ -243,6 +254,18 @@ def andrews_curves(
     Returns
     -------
     class:`matplotlip.axis.Axes`
+
+    Examples
+    --------
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.read_csv(
+        ...     'https://raw.github.com/pandas-dev/'
+        ...     'pandas/master/pandas/tests/data/iris.csv'
+        ... )
+        >>> pd.plotting.andrews_curves(df, 'Name')
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.andrews_curves(
@@ -298,10 +321,10 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
     This example draws a basic bootstap plot for a Series.
 
     .. plot::
-            :context: close-figs
+        :context: close-figs
 
-            >>> s = pd.Series(np.random.uniform(size=100))
-            >>> fig = pd.plotting.bootstrap_plot(s)  # doctest: +SKIP
+        >>> s = pd.Series(np.random.uniform(size=100))
+        >>> pd.plotting.bootstrap_plot(s)
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.bootstrap_plot(
@@ -358,13 +381,17 @@ def parallel_coordinates(
 
     Examples
     --------
-    >>> from matplotlib import pyplot as plt
-    >>> df = pd.read_csv('https://raw.github.com/pandas-dev/pandas/master'
-                        '/pandas/tests/data/iris.csv')
-    >>> pd.plotting.parallel_coordinates(
-            df, 'Name',
-            color=('#556270', '#4ECDC4', '#C7F464'))
-    >>> plt.show()
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.read_csv(
+        ...     'https://raw.github.com/pandas-dev/'
+        ...     'pandas/master/pandas/tests/data/iris.csv'
+        ... )
+        >>> pd.plotting.parallel_coordinates(
+        ...     df, 'Name', color=('#556270', '#4ECDC4', '#C7F464')
+        ... )
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.parallel_coordinates(
@@ -398,6 +425,28 @@ def lag_plot(series, lag=1, ax=None, **kwds):
     Returns
     -------
     class:`matplotlib.axis.Axes`
+
+    Examples
+    --------
+
+    Lag plots are most commonly used to look for patterns in time series data.
+
+    Given the following time series
+
+    .. plot::
+        :context: close-figs
+
+        >>> np.random.seed(5)
+        >>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))
+        >>> s = pd.Series(x)
+        >>> s.plot()
+
+    A lag plot with ``lag=1`` returns
+
+    .. plot::
+        :context: close-figs
+
+        >>> pd.plotting.lag_plot(s, lag=1)
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
@@ -417,6 +466,20 @@ def autocorrelation_plot(series, ax=None, **kwargs):
     Returns
     -------
     class:`matplotlib.axis.Axes`
+
+    Examples
+    --------
+
+    The horizontal lines in the plot correspond to 95% and 99% confidence bands.
+
+    The dashed line is 99% confidence band.
+
+    .. plot::
+        :context: close-figs
+
+        >>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
+        >>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
+        >>> pd.plotting.autocorrelation_plot(s)
     """
     plot_backend = _get_plot_backend("matplotlib")
     return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)