Calculation and Visualization of Correlation Matrix with Pandas
Another alternative is to use the heatmap function in seaborn to plot the covariance. This example uses the Auto data set from the ISLR package in R (the same as in the example you showed).

import pandas.rpy.common as com
import seaborn as sns
%matplotlib inline

# load the R package ISLR
infert = com.importr("ISLR")

# load the Auto dataset
auto_df = com.load_data('Auto')

# calculate the correlation matrix
corr = auto_df.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns)




If you wanted to be even more fancy, you can use Pandas Style, for example:

cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)

def magnify():
    return [dict(selector="th",
                 props=[("font-size", "7pt")]),
            dict(selector="td",
                 props=[('padding', "0em 0em")]),
            dict(selector="th:hover",
                 props=[("font-size", "12pt")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '12pt')])
]

corr.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\
    .set_caption("Hover to magify")\
    .set_precision(2)\
    .set_table_styles(magnify())