What is a Stacked Bar Chart?

A stacked bar chart is a type of bar chart where each bar is divided into multiple sections, with each section representing a different category. These sections are stacked on top of one another, making it easy to see the proportion each category contributes to the total. Stacked bar charts are especially useful for displaying breakdowns within categories.

Challenges of Creating Stacked Bar Charts

• Data Formatting: Ensuring that data aligns correctly when stacked is crucial for accuracy.
• Color Choices: Distinct colors are needed to differentiate each category.
• Labeling: Properly labeling each subcategory within the bar can be challenging but is essential for clarity.

Step 1: Import Required Libraries

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

Step 2: Create the Dataset

Let’s create a dataset representing quarterly sales for three different product categories.

# Sample data
data = {
    'Quarter': ['Q1', 'Q2', 'Q3', 'Q4'],
    'Product_A': [150, 200, 300, 250],
    'Product_B': [180, 210, 320, 220],
    'Product_C': [100, 130, 250, 280]
}
df = pd.DataFrame(data)

Step 3: Define Colors and Chart Style

To make each product category distinct, choose a set of colors that work well together.

# Define colors for each category
colors = ['#FFA07A', '#20B2AA', '#778899']  # Colors for Product A, B, C

Step 4: Plot the Stacked Bar Chart

Using plt.bar(), we plot each product category as a separate bar layer and stack them.

# Set the figure size
plt.figure(figsize=(10, 6))

# Calculate the bottom positions for each layer
bar_width = 0.6  # Set bar width
bottoms = np.zeros(len(df))  # Initialize the bottom positions at zero

# Loop through each product category and stack the bars
for idx, product in enumerate(['Product_A', 'Product_B', 'Product_C']):
    plt.bar(df['Quarter'], df[product], bottom=bottoms, color=colors[idx], label=product, width=bar_width)
    bottoms += df[product]  # Update the bottom position for stacking

# Add legend, labels, and title
plt.xlabel('Quarter')
plt.ylabel('Sales')
plt.title('Quarterly Sales by Product Category')
plt.legend(title='Product Category')
plt.show()

Step 5: Add Data Labels

Adding data labels above each section of the stacked bar makes it easier to see exact values at a glance.

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

# Sample data
data = {
    'Quarter': ['Q1', 'Q2', 'Q3', 'Q4'],
    'Product_A': [150, 200, 300, 250],
    'Product_B': [180, 210, 320, 220],
    'Product_C': [100, 130, 250, 280]
}
df = pd.DataFrame(data)

# Define colors
colors = ['#FFA07A', '#20B2AA', '#778899']

# Plot the stacked bar chart
plt.figure(figsize=(10, 6))
bar_width = 0.6
bottoms = np.zeros(len(df))  # Initialize bottom positions for stacking

# Loop through each product category, stack the bars, and add data labels
for idx, product in enumerate(['Product_A', 'Product_B', 'Product_C']):
    bars = plt.bar(
        df['Quarter'], df[product], bottom=bottoms, 
        color=colors[idx], label=product, width=bar_width
    )

    # Add data labels to the bars
    for bar in bars:
        height = bar.get_height()  # Get the height of the bar
        label_y = bar.get_y() + height / 2  # Compute y position for label
        plt.text(
            bar.get_x() + bar.get_width() / 2, label_y, 
            f'{int(height)}', ha='center', va='center', color='white', fontsize=10
        )

    # Update the bottom position for stacking
    bottoms += df[product]

# Add legend and titles
plt.xlabel('Quarter')
plt.ylabel('Sales')
plt.title('Quarterly Sales by Product Category')
plt.legend(title='Product Category')
plt.show()