Plot a table of numeric and nominal features

plot_table.py

def plot_table(features, targets):

    for idx, feature in enumerate(features.columns):
        grouped = loans.groupby(targets)

        if idx % 2==0:
            fig = plt.figure(figsize=(16,4))
        
        plt.subplot(1, 2, idx % 2 + 1)

        vals = loans[feature][pd.notnull(loans[feature])]
        dtype = grouped[feature].obj.dtype
    
        if dtype == 'float64':
            left = min(vals)
            right = vals.quantile(0.99)
            if left == right:
                right = left + 0.5
                left = left - 0.5
            delta = (right-left)/10
            bins = np.arange(left, right, delta)
                
            for name, srs in grouped[feature]:
                srs.hist(alpha=0.5, bins=bins, label=name, normed=True,
                         color='b' if name=='GOOD' else 'g')
        else:
            for name, srs in grouped[feature]:
                values = srs.value_counts(normalize=True)
            
                values[:10].plot(alpha=0.5, kind='bar', label=name,
                                 color='b' if name=='GOOD' else 'g')

        plt.legend(loc='best')
        plt.xlabel("{} ({})".format(feature, 'NUMERIC' if dtype=='float64' else 'NOMINAL'))