Merge PR #1566

Author: wesm

RELEASE.rst

@@ -30,7 +30,8 @@ pandas 0.8.1
 **New features**
 
   - Can pass dict of per-column line styles to DataFrame.plot (#1559)
-  - Add new ``bootstrap_plot`` function
+  - Add new ``bootstrap_plot`` plot function
+  - Add new ``parallel_coordinates`` plot function (#1488)
 
 **Improvements to existing features**
 

doc/source/visualization.rst

@@ -368,7 +368,7 @@ Bootstrap Plot
 
 Bootstrap plots are used to visually assess the uncertainty of a statistic, such
 as mean, median, midrange, etc. A random subset of a specified size is selected
-from a data set, the statistic in question is computed for this subset and the 
+from a data set, the statistic in question is computed for this subset and the
 process is repeated a specified number of times. Resulting plots and histograms
 are what constitutes the bootstrap plot.
 
@@ -380,3 +380,29 @@ are what constitutes the bootstrap plot.
 
    @savefig bootstrap_plot.png width=8in
    bootstrap_plot(data, size=50, samples=500, color='grey')
+
+RadViz
+~~~~~~
+
+RadViz is a way of visualizing multi-variate data. It is based on a simple
+spring tension minimization algorithm. Basically you set up a bunch of points in
+a plane. In our case they are equally spaced on a unit circle. Each point
+represents a single attribute. You then pretend that each sample in the data set
+is attached to each of these points by a spring, the stiffness of which is
+proportional to the numerical value of that attribute (they are normalized to
+unit interval). The point in the plane, where our sample settles to (where the
+forces acting on our sample are at an equilibrium) is where a dot representing
+our sample will be drawn. Depending on which class that sample belongs it will
+be colored differently.
+
+.. ipython:: python
+
+   from pandas import read_csv
+   from pandas.tools.plotting import radviz
+
+   data = read_csv('data/iris.data')
+
+   plt.figure()
+
+   @savefig radviz.png width=6in
+   radviz(data, 'Name')

pandas/tests/test_graphics.py

@@ -327,6 +327,14 @@ def test_parallel_coordinates(self):
         df = read_csv(path)
         _check_plot_works(parallel_coordinates, df, 'Name')
 
+    @slow
+    def test_radviz(self):
+        from pandas import read_csv
+        from pandas.tools.plotting import radviz
+        path = os.path.join(curpath(), 'data/iris.csv')
+        df = read_csv(path)
+        _check_plot_works(radviz, df, 'Name')
+
     @slow
     def test_plot_int_columns(self):
         df = DataFrame(np.random.randn(100, 4)).cumsum()

pandas/tools/plotting.py

@@ -147,6 +147,65 @@ def _get_marker_compat(marker):
         return 'o'
     return marker
 
+def radviz(frame, class_column, ax=None, **kwds):
+    """RadViz - a multivariate data visualization algorithm
+
+    Parameters:
+    -----------
+    frame: DataFrame object
+    class_column: Column name that contains information about class membership
+    ax: Matplotlib axis object, optional
+    kwds: Matplotlib scatter method keyword arguments, optional
+
+    Returns:
+    --------
+    ax: Matplotlib axis object
+    """
+    import matplotlib.pyplot as plt
+    import matplotlib.patches as patches
+    import matplotlib.text as text
+    import random
+    def random_color(column):
+        random.seed(column)
+        return [random.random() for _ in range(3)]
+    def normalize(series):
+        a = min(series)
+        b = max(series)
+        return (series - a) / (b - a)
+    column_names = [column_name for column_name in frame.columns if column_name != class_column]
+    columns = [normalize(frame[column_name]) for column_name in column_names]
+    if ax == None:
+        ax = plt.gca(xlim=[-1, 1], ylim=[-1, 1])
+    classes = set(frame[class_column])
+    to_plot = {}
+    for class_ in classes:
+        to_plot[class_] = [[], []]
+    n = len(frame.columns) - 1
+    s = np.array([(np.cos(t), np.sin(t)) for t in [2.0 * np.pi * (i / float(n)) for i in range(n)]])
+    for i in range(len(frame)):
+        row = np.array([column[i] for column in columns])
+        row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1)
+        y = (s * row_).sum(axis=0) / row.sum()
+        class_name = frame[class_column][i]
+        to_plot[class_name][0].append(y[0])
+        to_plot[class_name][1].append(y[1])
+    for class_ in classes:
+        ax.scatter(to_plot[class_][0], to_plot[class_][1], color=random_color(class_), label=str(class_), **kwds)
+    ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor='none'))
+    for xy, name in zip(s, column_names):
+        ax.add_patch(patches.Circle(xy, radius=0.025, facecolor='gray'))
+        if xy[0] < 0.0 and xy[1] < 0.0:
+            ax.text(xy[0] - 0.025, xy[1] - 0.025, name, ha='right', va='top', size='small')
+        elif xy[0] < 0.0 and xy[1] >= 0.0:
+            ax.text(xy[0] - 0.025, xy[1] + 0.025, name, ha='right', va='bottom', size='small')
+        elif xy[0] >= 0.0 and xy[1] < 0.0:
+            ax.text(xy[0] + 0.025, xy[1] - 0.025, name, ha='left', va='top', size='small')
+        elif xy[0] >= 0.0 and xy[1] >= 0.0:
+            ax.text(xy[0] + 0.025, xy[1] + 0.025, name, ha='left', va='bottom', size='small')
+    ax.legend(loc='upper right')
+    ax.axis('equal')
+    return ax
+
 def andrews_curves(data, class_column, ax=None, samples=200):
     """
     Parameters: