Heatmaps with imshow¶

The ax.imshow() method displays 2D data as a color-coded image, ideal for matrices, correlation tables, and gridded data.

Basic Heatmap¶

Create a simple heatmap from a 2D array.

1. Import and Setup¶

import matplotlib.pyplot as plt
import numpy as np

2. Create 2D Data¶

np.random.seed(42)
data = np.random.rand(10, 10)

3. Display with imshow¶

fig, ax = plt.subplots()
im = ax.imshow(data)
plt.colorbar(im)
plt.show()

Colormap Selection¶

The cmap keyword controls the color scheme.

1. Sequential Colormaps¶

fig, axes = plt.subplots(1, 3, figsize=(12, 4))

for ax, cmap in zip(axes, ['viridis', 'plasma', 'Blues']):
    im = ax.imshow(data, cmap=cmap)
    ax.set_title(cmap)
    plt.colorbar(im, ax=ax)

plt.tight_layout()
plt.show()

2. Diverging Colormaps¶

data_centered = np.random.randn(10, 10)

fig, ax = plt.subplots()
im = ax.imshow(data_centered, cmap='RdBu', vmin=-2, vmax=2)
plt.colorbar(im)
plt.show()

3. Common Colormaps¶

# Sequential: 'viridis', 'plasma', 'inferno', 'magma', 'Blues', 'Greens'
# Diverging: 'RdBu', 'coolwarm', 'seismic', 'PiYG'
# Qualitative: 'Set1', 'Set2', 'tab10', 'tab20'

Value Range¶

Control the mapping between data values and colors.

1. Auto Range (Default)¶

ax.imshow(data)  # Maps min to bottom, max to top of colormap

2. Fixed Range¶

ax.imshow(data, vmin=0, vmax=1)

3. Centered at Zero¶

max_abs = np.abs(data_centered).max()
ax.imshow(data_centered, cmap='RdBu', vmin=-max_abs, vmax=max_abs)

Aspect Ratio¶

The aspect keyword controls pixel shape.

1. Equal Aspect (Default)¶

ax.imshow(data, aspect='equal')  # Square pixels

2. Auto Aspect¶

ax.imshow(data, aspect='auto')  # Fills axes, may stretch

3. Numeric Aspect¶

ax.imshow(data, aspect=2)  # Height = 2 × width per pixel

Axis Labels and Ticks¶

Customize tick positions and labels for matrix visualization.

1. Set Tick Positions¶

fig, ax = plt.subplots()
im = ax.imshow(data)

ax.set_xticks(np.arange(10))
ax.set_yticks(np.arange(10))
plt.show()

2. Custom Labels¶

row_labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
col_labels = [f'Col {i}' for i in range(10)]

ax.set_xticks(np.arange(10))
ax.set_yticks(np.arange(10))
ax.set_xticklabels(col_labels, rotation=45, ha='right')
ax.set_yticklabels(row_labels)

3. Move X Labels to Top¶

ax.xaxis.set_ticks_position('top')
ax.xaxis.set_label_position('top')

Annotating Cells¶

Add text values to each cell.

1. Basic Annotation¶

fig, ax = plt.subplots()
im = ax.imshow(data, cmap='Blues')

for i in range(data.shape[0]):
    for j in range(data.shape[1]):
        ax.text(j, i, f'{data[i, j]:.2f}', ha='center', va='center', fontsize=8)

plt.show()

2. Contrast Text Color¶

threshold = data.max() / 2

for i in range(data.shape[0]):
    for j in range(data.shape[1]):
        color = 'white' if data[i, j] > threshold else 'black'
        ax.text(j, i, f'{data[i, j]:.2f}', ha='center', va='center', 
                color=color, fontsize=8)

3. Integer Annotation¶

int_data = np.random.randint(0, 100, (5, 5))

for i in range(int_data.shape[0]):
    for j in range(int_data.shape[1]):
        ax.text(j, i, int_data[i, j], ha='center', va='center')

Correlation Matrix¶

A common use case for imshow heatmaps.

1. Compute Correlation¶

np.random.seed(42)
df_data = np.random.randn(100, 5)
corr_matrix = np.corrcoef(df_data.T)

2. Display Correlation Heatmap¶

fig, ax = plt.subplots(figsize=(6, 5))

im = ax.imshow(corr_matrix, cmap='RdBu', vmin=-1, vmax=1)

labels = ['Var A', 'Var B', 'Var C', 'Var D', 'Var E']
ax.set_xticks(np.arange(5))
ax.set_yticks(np.arange(5))
ax.set_xticklabels(labels, rotation=45, ha='right')
ax.set_yticklabels(labels)

for i in range(5):
    for j in range(5):
        color = 'white' if abs(corr_matrix[i, j]) > 0.5 else 'black'
        ax.text(j, i, f'{corr_matrix[i, j]:.2f}', ha='center', va='center', color=color)

plt.colorbar(im, label='Correlation')
plt.tight_layout()
plt.show()

3. Mask Upper Triangle¶

mask = np.triu(np.ones_like(corr_matrix, dtype=bool), k=1)
masked_corr = np.ma.masked_array(corr_matrix, mask)

ax.imshow(masked_corr, cmap='RdBu', vmin=-1, vmax=1)

Interpolation¶

Control how pixel boundaries are rendered.

1. No Interpolation (Default for Small Data)¶

ax.imshow(data, interpolation='nearest')

2. Smooth Interpolation¶

ax.imshow(data, interpolation='bilinear')

3. Common Options¶

# 'nearest': Sharp pixel boundaries
# 'bilinear': Smooth linear interpolation
# 'bicubic': Smoother cubic interpolation
# 'gaussian': Gaussian smoothing

Runnable Example: `seaborn_matrix_plots.py`¶

"""
Tutorial 06: Matrix Plots
Heatmaps, cluster maps, correlation matrices
Level: Intermediate
"""
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# =============================================================================
# Main
# =============================================================================

if __name__ == "__main__":

    sns.set_style("white")
    tips = sns.load_dataset('tips')

    # Correlation heatmap
    plt.figure(figsize=(8, 6))
    numeric_cols = tips.select_dtypes(include=[np.number])
    corr = numeric_cols.corr()
    sns.heatmap(corr, annot=True, cmap='coolwarm', center=0, square=True, linewidths=1)
    plt.title('Correlation Heatmap', fontsize=14, fontweight='bold')
    plt.tight_layout()
    plt.show()

    # Pivot table heatmap
    pivot_data = tips.pivot_table(values='tip', index='day', columns='time', aggfunc='mean')
    plt.figure(figsize=(8, 6))
    sns.heatmap(pivot_data, annot=True, fmt='.2f', cmap='YlOrRd')
    plt.title('Average Tip: Day vs Time', fontsize=14, fontweight='bold')
    plt.tight_layout()
    plt.show()

    # Clustermap - with hierarchical clustering
    plt.figure(figsize=(10, 8))
    sns.clustermap(corr, cmap='coolwarm', center=0, linewidths=1, annot=True)
    plt.show()

    print("Tutorial 06 demonstrates matrix visualizations")
    print("Key functions: heatmap(), clustermap()")