Scatter matrix diagonals

doc/source/visualization.rst

@@ -241,5 +241,8 @@ Scatter plot matrix
    from pandas.tools.plotting import scatter_matrix
    df = DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])
 
-   @savefig scatter_matrix_ex.png width=6in
-   scatter_matrix(df, alpha=0.2, figsize=(8, 8))
+   @savefig scatter_matrix_kde.png width=6in
+   scatter_matrix(df, alpha=0.2, figsize=(8, 8), diagonal='kde')
+
+   @savefig scatter_matrix_hist.png width=6in
+   scatter_matrix(df, alpha=0.2, figsize=(8, 8), diagonal='hist')

pandas/tests/test_graphics.py

@@ -214,6 +214,8 @@ def scat(**kwds):
         _check_plot_works(scat)
         _check_plot_works(scat, marker='+')
         _check_plot_works(scat, vmin=0)
+        _check_plot_works(scat, diagonal='kde')
+        _check_plot_works(scat, diagonal='hist')
 
         def scat2(x, y, by=None, ax=None, figsize=None):
             return plt.scatter_plot(df, x, y, by, ax, figsize=None)

pandas/tools/plotting.py

@@ -3,6 +3,7 @@
 from itertools import izip
 
 import numpy as np
+from scipy import stats
 
 from pandas.util.decorators import cache_readonly
 import pandas.core.common as com
@@ -12,12 +13,19 @@
 from pandas.tseries.offsets import DateOffset
 
 def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
-                   **kwds):
+                   diagonal='hist', **kwds):
     """
     Draw a matrix of scatter plots.
 
     Parameters
     ----------
+    alpha : amount of transparency applied
+    figsize : a tuple (width, height) in inches
+    ax : Matplotlib axis object
+    grid : setting this to True will show the grid
+    diagonal : pick between 'kde' and 'hist' for
+        either Kernel Density Estimation or Histogram
+        plon in the diagonal
     kwds : other plotting keyword arguments
         To be passed to scatter function
 
@@ -36,15 +44,26 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
 
     for i, a in zip(range(n), df.columns):
         for j, b in zip(range(n), df.columns):
-            axes[i, j].scatter(df[b], df[a], alpha=alpha, **kwds)
+            if i == j:
+                # Deal with the diagonal by drawing a histogram there.
+                if diagonal == 'hist':
+                    axes[i, j].hist(df[a])
+                elif diagonal == 'kde':
+                    y = df[a]
+                    gkde = stats.gaussian_kde(y)
+                    ind = np.linspace(min(y), max(y), 1000)
+                    axes[i, j].plot(ind, gkde.evaluate(ind), **kwds)
+            else:
+                axes[i, j].scatter(df[b], df[a], alpha=alpha, **kwds)
+
             axes[i, j].set_xlabel('')
             axes[i, j].set_ylabel('')
             axes[i, j].set_xticklabels([])
             axes[i, j].set_yticklabels([])
             ticks = df.index
 
             is_datetype = ticks.inferred_type in ('datetime', 'date',
-                                                  'datetime64')
+                                              'datetime64')
 
             if ticks.is_numeric() or is_datetype:
                 """
@@ -87,13 +106,6 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
 
             axes[i, j].grid(b=grid)
 
-    # ensure {x,y}lim off diagonal are the same as diagonal
-    for i in range(n):
-        for j in range(n):
-            if i != j:
-                axes[i, j].set_xlim(axes[j, j].get_xlim())
-                axes[i, j].set_ylim(axes[i, i].get_ylim())
-
     return axes
 
 def _gca():