["python","pandas","seaborn","heatmap","correlation"]
Correlation heatmap

I want to represent correlation matrix using a heatmap. There is something called correlogram in R, but I don't think there's such a thing in Python. How can I do this? The values go from -1 to 1, for example: [[ 1. 0.00279981 0.95173379 0.02486161 -0.00324926 -0.00432099] [ 0.00279981 1. 0.17728303 0.64425774 0.30735071 0.37379443] [ 0.95173379 0.17728303 1. 0.27072266 0.02549031 0.03324756] [ 0.02486161 0.64425774 0.27072266 1. 0.18336236 0.18913512] [-0.00324926 0.30735071 0.02549031 0.18336236 1. 0.77678274] [-0.00432099 0.37379443 0.03324756 0.18913512 0.77678274 1. ]] I was able to produce the following heatmap based on another question, but the problem is that my values get 'cut' at 0, so I would like to have a map which goes from blue(-1) to red(1), or something like that, but here values below 0 are not presented in an adequate way. Here's the code for that: plt.imshow(correlation_matrix,cmap='hot',interpolation='nearest')
Another alternative is to use the heatmap function in seaborn to plot the covariance. This example uses the 'mpg' data set from seaborn. import seaborn as sns %matplotlib inline # load the Auto dataset auto_df = sns.load_dataset('mpg') # calculate the correlation matrix on the numeric columns corr = auto_df.select_dtypes('number').corr() # plot the heatmap sns.heatmap(corr) If you wanted to be even more fancy, you can use Pandas Style, for example: 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)\ .format(precision=3)\ .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\ .set_caption("Hover to magify")\ .set_table_styles(magnify())