Market Index Diagonal Correlation Plot-Complete

# Load packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="darkgrid")
%config InlineBackend.figure_format = 'retina'

# Upload your data as CSV and load as data frame
df = pd.read_csv('Market_index_data correlation.csv')
df.head()

# Compute the correlation matrix
corr = df.corr(method = 'pearson')

# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))

# Set up the matplotlib figure
fig, ax = plt.subplots(figsize=(11, 9))                    # Set figure size

# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)

# Draw the heatmap with the mask 
sns.heatmap(corr, 
            mask = mask, 
            cmap = cmap, 
            vmax = 1,                                      # Set scale min value
            vmin = -1,                                     # Set scale min value
            center = 0,                                    # Set scale min value
            square = True,                                 # Ensure perfect squares
            linewidths = 1.5,                              # Set linewidth between squares
            cbar_kws = {"shrink": .9},                     # Set size of color bar
            annot = True                                   # Include values within squares
           );

plt.xticks(rotation=45)                                    # Rotate x labels
plt.yticks(rotation=45)                                    # Rotate y labels
# plt.xlabel('X Axis Title', size=20)                      # Set x axis title
# plt.ylabel('Y Axis Title', size=20)                      # Set y axis title
plt.title('Diagonal Correlation Plot', size=30, y=1.05);   # Set plot title and position