Comparing Business Metrics with Multiple Charts
Comparing multiple business metrics side by side is a powerful approach for business analysts. When you want to understand the relationship between different metrics—such as sales and profit for a range of products—visualizing them together helps you spot trends, outliers, and patterns that might be missed if you looked at each metric in isolation. For example, you might discover that a product with high sales does not necessarily have high profit, which could prompt a deeper investigation into costs or pricing strategies. This kind of comparison is especially valuable when making decisions about inventory, marketing, or resource allocation.
123456789101112131415161718192021222324import matplotlib.pyplot as plt # Example data products = ["A", "B", "C", "D"] sales = [12000, 18000, 9000, 22000] profit = [3000, 4000, 1200, 5000] # Create subplots: 1 row, 2 columns fig, axes = plt.subplots(1, 2, figsize=(10, 5)) # Sales subplot axes[0].bar(products, sales, color="skyblue") axes[0].set_title("Sales by Product") axes[0].set_ylabel("Sales ($)") axes[0].set_xlabel("Product") # Profit subplot axes[1].bar(products, profit, color="lightgreen") axes[1].set_title("Profit by Product") axes[1].set_ylabel("Profit ($)") axes[1].set_xlabel("Product") plt.tight_layout() plt.show()
To effectively compare metrics, you will often use subplots in matplotlib. Begin by preparing your data so that each metric you want to compare is organized in lists or arrays. Then, use the plt.subplots() function to create a figure with multiple axes—these axes are your individual charts. You can decide whether to arrange your subplots in rows, columns, or a grid, depending on how many metrics you are comparing. Sharing axes is useful when the scale or units are the same, as it makes direct comparison easier; you can do this by setting the sharex or sharey parameters in plt.subplots(). For clarity, always label each axis, add titles to your subplots, and consider using different colors or chart types for each metric. Customizing each chart—such as adjusting tick marks, adding grid lines, or highlighting specific data points—further improves readability and makes your visualizations more useful for business decisions.
1234567891011121314151617181920212223import matplotlib.pyplot as plt products = ["A", "B", "C", "D"] sales = [12000, 18000, 9000, 22000] profit = [3000, 4000, 1200, 5000] fig, axes = plt.subplots(1, 2, figsize=(10, 5), sharex=True) # Sales subplot axes[0].bar(products, sales, color="skyblue", label="Sales") axes[0].set_title("Sales by Product") axes[0].set_ylabel("Sales ($)") axes[0].legend() # Profit subplot axes[1].bar(products, profit, color="lightgreen", label="Profit") axes[1].set_title("Profit by Product") axes[1].set_ylabel("Profit ($)") axes[1].legend() plt.suptitle("Comparison of Sales and Profit Across Products") plt.tight_layout(rect=[0, 0.03, 1, 0.95]) plt.show()
1. What is the advantage of using subplots in business data visualization?
2. How can legends improve the readability of business charts?
3. Fill in the blanks: To create multiple charts in one figure, use the ____ function in matplotlib.
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Can you explain how sharing axes improves comparison in these charts?
What are some other ways to customize subplots for better clarity?
How can I compare more than two metrics using subplots?
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Comparing multiple business metrics side by side is a powerful approach for business analysts. When you want to understand the relationship between different metrics—such as sales and profit for a range of products—visualizing them together helps you spot trends, outliers, and patterns that might be missed if you looked at each metric in isolation. For example, you might discover that a product with high sales does not necessarily have high profit, which could prompt a deeper investigation into costs or pricing strategies. This kind of comparison is especially valuable when making decisions about inventory, marketing, or resource allocation.
123456789101112131415161718192021222324import matplotlib.pyplot as plt # Example data products = ["A", "B", "C", "D"] sales = [12000, 18000, 9000, 22000] profit = [3000, 4000, 1200, 5000] # Create subplots: 1 row, 2 columns fig, axes = plt.subplots(1, 2, figsize=(10, 5)) # Sales subplot axes[0].bar(products, sales, color="skyblue") axes[0].set_title("Sales by Product") axes[0].set_ylabel("Sales ($)") axes[0].set_xlabel("Product") # Profit subplot axes[1].bar(products, profit, color="lightgreen") axes[1].set_title("Profit by Product") axes[1].set_ylabel("Profit ($)") axes[1].set_xlabel("Product") plt.tight_layout() plt.show()
To effectively compare metrics, you will often use subplots in matplotlib. Begin by preparing your data so that each metric you want to compare is organized in lists or arrays. Then, use the plt.subplots() function to create a figure with multiple axes—these axes are your individual charts. You can decide whether to arrange your subplots in rows, columns, or a grid, depending on how many metrics you are comparing. Sharing axes is useful when the scale or units are the same, as it makes direct comparison easier; you can do this by setting the sharex or sharey parameters in plt.subplots(). For clarity, always label each axis, add titles to your subplots, and consider using different colors or chart types for each metric. Customizing each chart—such as adjusting tick marks, adding grid lines, or highlighting specific data points—further improves readability and makes your visualizations more useful for business decisions.
1234567891011121314151617181920212223import matplotlib.pyplot as plt products = ["A", "B", "C", "D"] sales = [12000, 18000, 9000, 22000] profit = [3000, 4000, 1200, 5000] fig, axes = plt.subplots(1, 2, figsize=(10, 5), sharex=True) # Sales subplot axes[0].bar(products, sales, color="skyblue", label="Sales") axes[0].set_title("Sales by Product") axes[0].set_ylabel("Sales ($)") axes[0].legend() # Profit subplot axes[1].bar(products, profit, color="lightgreen", label="Profit") axes[1].set_title("Profit by Product") axes[1].set_ylabel("Profit ($)") axes[1].legend() plt.suptitle("Comparison of Sales and Profit Across Products") plt.tight_layout(rect=[0, 0.03, 1, 0.95]) plt.show()
1. What is the advantage of using subplots in business data visualization?
2. How can legends improve the readability of business charts?
3. Fill in the blanks: To create multiple charts in one figure, use the ____ function in matplotlib.
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