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For the following dataframe of a chi2 correlation study, i started to plot a heatmap:

import pandas as pd
import numpy as np


columns = ['A', 'B', 'C', 'D', 'E', 'F', 'G']


results = np.array([[0.70709269, 0.17683162, 0.38328705, 0.61449242, 0.43709035, 0.33675627, 0.2661715 ],
                    [0.17683162, 0.70709268, 0.20520211, 0.16044232, 0.07607822, 0.13364355, 0.13093324],
                    [0.38328705, 0.20520211, 0.81649658, 0.37683897, 0.17308779, 0.29541159, 0.29975079],
                    [0.61449242, 0.16044232, 0.37683897, 0.81649658, 0.4991043 , 0.34257853, 0.2786975 ],
                    [0.43709035, 0.07607822, 0.17308779, 0.4991043 , 0.81649658, 0.22700152, 0.17041603],
                    [0.33675627, 0.13364355, 0.29541159, 0.34257853, 0.22700152, 0.81649658, 0.22018705],
                    [0.2661715 , 0.13093324, 0.29975079, 0.2786975 , 0.17041603, 0.22018705, 0.81649658]])


df_matrix = pd.DataFrame(results, columns=columns)
category_bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
categories = ['Very Weak', 'Weak', 'Moderate', 'Strong', 'Very Strong']


df_heatmap = pd.DataFrame(df_matrix, index=df_matrix.index, columns=df_matrix.columns)


colors = sns.color_palette('coolwarm', len(categories))
cmap = ListedColormap(colors)


fig, ax = plt.subplots(figsize=(10, 8))


sns.heatmap(df_heatmap, annot=True, cmap=cmap, fmt=".3f", cbar=False, ax=ax, linecolor='white')


plt.subplots_adjust(left=0.25, top=0.95)



plt.show()

But, for some reason (I suppose it is due to rounding the values), 0.71 and 0.82 are plotting in the same color. Can someone give me some guidance on what the problem is?

enter image description here

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3 Answers 3

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The issue you're facing with the heatmap is likely due to the limited number of colors in the coolwarm colormap. Since you have five categories ('Very Weak', 'Weak', 'Moderate', 'Strong', 'Very Strong'), you need five distinct colors in the colormap to represent each category uniquely.

One way to fix this issue is to use a different colormap that has enough distinct colors to represent all your categories. You can use viridis or plasma colormap, which are perceptually uniform and have enough colors for your categories.

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


df_matrix = pd.DataFrame(results, columns=columns)
category_bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
categories = ['Very Weak', 'Weak', 'Moderate', 'Strong', 'Very Strong']

df_heatmap = pd.DataFrame(df_matrix, index=df_matrix.index, columns=df_matrix.columns)

# Using 'viridis' colormap
cmap = 'viridis'

fig, ax = plt.subplots(figsize=(10, 8))

sns.heatmap(df_heatmap, 
            annot=True, 
            cmap=cmap, 
            fmt=".3f", 
            cbar=False, 
            ax=ax, 
            linecolor='white')

plt.subplots_adjust(left=0.25, top=0.95)
plt.show()

enter image description here

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  • $\begingroup$ Hey @Pluviophile, i appreciate your comment, but the problem doesn't seem to be solved. Note that, in the print you gave me, 0.076 and 0.16 have different colors, even though they are in the 0.0-0.2 range. I think my problem reside in the fact that heatmap normalises the data to color them (please correct me if im mistaken). It looks like an obvious problem but i still can't get it right for some reason. $\endgroup$ Jul 6, 2023 at 18:10
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Setting the vmin and the vmax will make sure that the bounds of the values are spaced evenly by 0.2 with the 5 categories specified.

cmap = sns.color_palette('mako', len(categories), as_cmap=True)


fig, ax = plt.subplots(figsize=(10, 8))


sns.heatmap(df_heatmap, annot=True, cmap=cmap, fmt=".3f", cbar=False, ax=ax, linecolor='white', vmin=0, vmax=1)

enter image description here

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  • $\begingroup$ I was about to post the solution, i did the exact same thing. Thank's a lot either way! Your solution still looks a little off, note that the colors looks continuos, not discrete. I will post the code that gave me the right thing i was looking for $\endgroup$ Jul 10, 2023 at 20:14
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The problem, as @yuckyh said, was that seaborn normalizes the variables to produce the colors. To avoid that, setting vmin and vmax was necessary:

colors = sns.color_palette('coolwarm', len(categories))
cmap = ListedColormap(colors)

# Adjust the figure size to avoid overlapping y-axis labels
fig, ax = plt.subplots(figsize=(10, 8))

# Plot the heatmap
sns.heatmap(df_matrix,
            cmap=cmap,
            fmt=".3f",
            cbar=False,
            ax=ax,
            linecolor='white',
            vmin=0,
            vmax=1,
           annot = True)


# Adjust the position of the lines indicating the edges of the rectangles
ax.hlines(np.arange(n_variables+1), *ax.get_xlim(), color='white', linewidth=1)
ax.vlines(np.arange(n_variables+1), *ax.get_ylim(), color='white', linewidth=1)

ax.set_xticks(np.arange(df_matrix.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(df_matrix.shape[0]) + 0.5, minor=False)

# Configure the tick labels
ax.set_xticklabels(df_heatmap.columns, rotation=45, ha="right")
ax.set_yticklabels(df_heatmap.index, rotation=0)

# Create a custom legend
legend_labels = [f'{category}: {category_bounds[i]:.1f} - {category_bounds[i+1]:.1f}' for i, category in enumerate(categories)]
legend_elements = [plt.Rectangle((0, 0), 1, 1, fc=colors[i]) for i in range(len(categories))]

# Reverse the order of the legend elements and labels
legend_elements = legend_elements[::-1]
legend_labels = legend_labels[::-1]

# Add the legend
ax.legend(handles=legend_elements, labels=legend_labels, loc='center left', bbox_to_anchor=(1, 0.5))

# Adjust the spacing to avoid overlapping y-axis labels
plt.subplots_adjust(left=0.25, top=0.95)

plt.show()

enter image description here

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