I have computed a covariance of 26 inputs from another software. I have an existing table of the results. See image below:
What I want to do is enter the table as a pandas dataframe and plot the matrix. I have seen the thread here: Plot correlation matrix using pandas. However, the aforementioned example, computed the covariance first and plotted the 'covariance' object. In my case, I want to plot the dataframe object to look like the covariance matrix in the example.
Link to data: HERE.
a clean option, in my opinion, from this other answer: How to plot only the lower triangle of a seaborn heatmap?
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_excel('Covariance Matrix.xlsx', header=None)
# Getting the Upper Triangle of the co-relation matrix
matrix = np.triu(df)
# using the upper triangle matrix as mask
fig, ax = plt.subplots(figsize=(12,8))
sns.heatmap(df, ax=ax, fmt='.1g', annot=True, mask=matrix)
plt.show()
hope this helps
IIUC, you can use seaborn.heatmap with annot=True :
plt.figure(figsize=(6, 4))
(
pd.read_excel("/tmp/Covariance Matrix.xlsx", header=None)
.pipe(lambda df: sns.heatmap(df.sample(10).sample(10, axis=1), annot=True, fmt=".1f"))
);
# for a sample of 10 rows / 10 columns
Output :
And, as suggested by stukituk in the comments, you can add cmap="coolwarm" for colors :
