Hail Seaborn!

The seaborn heatmap perhaps is the best visualization of the correlations in a data set.

 Much better than Axes.matshow():

	import seaborn as sns
	def plot_corr(data, sz=10):
	corr = data.corr()
	fig, ax = plt.subplots(figsize=(sz,sz)) # Sample figsize in inches
	sns.heatmap(corr,
	xticklabels=corr.columns.values,
	yticklabels=corr.columns.values, ax=ax)
	def plot_corr2(df,size=10):
	corr = df.corr()
	fig, ax = plt.subplots(figsize=(size, size))
	ax.matshow(corr)
	plt.xticks(range(len(corr.columns)), corr.columns);
	plt.yticks(range(len(corr.columns)), corr.columns);

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