If your main goal is to visualize the correlation matrix, rather than creating a plot per se, the convenient pandas styling options is a viable built-in solution:

import pandas as pd
import numpy as np

rs = np.random.RandomState(0)
df = pd.DataFrame(rs.rand(10, 10))
corr = df.corr()
corr.style.background_gradient(cmap='coolwarm')
# 'RdBu_r', 'BrBG_r', & PuOr_r are other good diverging colormaps

Note that this needs to be in a backend that supports rendering HTML, such as the JupyterLab Notebook.

Styling

You can easily limit the digit precision (this is now .format(precision=2) in pandas 2.*):

corr.style.background_gradient(cmap='coolwarm').set_precision(2)

Or get rid of the digits altogether if you prefer the matrix without annotations:

corr.style.background_gradient(cmap='coolwarm').set_properties(**{'font-size': '0pt'})

The styling documentation also includes instructions of more advanced styles, such as how to change the display of the cell the mouse pointer is hovering over.

Time comparison

In my testing, style.background_gradient() was 4x faster than plt.matshow() and 120x faster than sns.heatmap() with a 10x10 matrix. Unfortunately it doesn't scale as well as plt.matshow(): the two take about the same time for a 100x100 matrix, and plt.matshow() is 10x faster for a 1000x1000 matrix.

Saving

There are a few possible ways to save the stylized dataframe:

• Return the HTML by appending the render() method and then write the output to a file.
• Save as an .xslx file with conditional formatting by appending the to_excel() method.
• Combine with imgkit to save a bitmap
• Take a screenshot (like I have done here).

Normalize colors across the entire matrix (pandas >= 0.24)

By setting axis=None, it is now possible to compute the colors based on the entire matrix rather than per column or per row:

corr.style.background_gradient(cmap='coolwarm', axis=None)

Single corner heatmap

Since many people are reading this answer I thought I would add a tip for how to only show one corner of the correlation matrix. I find this easier to read myself, since it removes the redundant information.

# Fill diagonal and upper half with NaNs
mask = np.zeros_like(corr, dtype=bool)
mask[np.triu_indices_from(mask)] = True
corr[mask] = np.nan
(corr
 .style
 .background_gradient(cmap='coolwarm', axis=None, vmin=-1, vmax=1)
 .highlight_null(color='#f1f1f1')  # Color NaNs grey
 .format(precision=2))

You can also use np.array if you don't want to write your matrix all over again.

import numpy as np
a = np.array([ [0.1, .32, .2,  0.4, 0.8], [.23, .18, .56, .61, .12], [.9,   .3,  .6,  .5,  .3],  [.34, .75, .91, .19, .21]]) 
b = np.corrcoef(a)
print b