DOC: Improved the docstring of pandas.plotting.radviz

pandas/plotting/_misc.py

@@ -147,25 +147,66 @@ def _get_marker_compat(marker):
 
 
 def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
-    """RadViz - a multivariate data visualization algorithm
+    """
+    Plot a multidimensional dataset in 2D.
+
+    Each Series in the DataFrame is represented as a evenly distributed
+    slice on a circle. Each data point is rendered in the circle according to
+    the value on each Series. Highly correlated `Series` in the `DataFrame`
+    are placed closer on the unit circle.
+
+    RadViz allow to project a N-dimensional data set into a 2D space where the
+    influence of each dimension can be interpreted as a balance between the
+    influence of all dimensions.
+
+    More info available at the `original article
+    <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.889>`_
+    describing RadViz.
 
     Parameters
     ----------
-    frame: DataFrame
-    class_column: str
-        Column name containing class names
-    ax: Matplotlib axis object, optional
-    color: list or tuple, optional
-        Colors to use for the different classes
-    colormap : str or matplotlib colormap object, default None
-        Colormap to select colors from. If string, load colormap with that name
-        from matplotlib.
-    kwds: keywords
-        Options to pass to matplotlib scatter plotting method
+    frame : `DataFrame`
+        Pandas object holding the data.
+    class_column : str
+        Column name containing the name of the data point category.
+    ax : :class:`matplotlib.axes.Axes`, optional
+        A plot instance to which to add the information.
+    color : list[str] or tuple[str], optional
+        Assign a color to each category. Example: ['blue', 'green'].
+    colormap : str or :class:`matplotlib.colors.Colormap`, default None
+        Colormap to select colors from. If string, load colormap with that
+        name from matplotlib.
+    kwds : optional
+        Options to pass to matplotlib scatter plotting method.
 
     Returns
     -------
-    ax: Matplotlib axis object
+    axes : :class:`matplotlib.axes.Axes`
+
+    See Also
+    --------
+    pandas.plotting.andrews_curves : Plot clustering visualization
+
+    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')
     """
     import matplotlib.pyplot as plt
     import matplotlib.patches as patches