Q.(Program 4-13) Taking the same data as Program 4-12 (discount = [10, 20, 30, 40, 50], saleInRs = [40000, 45000, 48000, 50000, 100000]), customise the scatter chart: display the size of each bubble as 10 times the discount, and change the colour to red, linewidth to 3, marker to '*' and edgecolor to blue.

Shape of question

This is a code task requiring a matplotlib scatter plot with specific customisation parameters.

Solution

The question asks us to create a scatter plot with the given data and apply five customisations: bubble size proportional to discount, red colour, linewidth 3, star marker, and blue edge colour.

import matplotlib.pyplot as plt

discount = [10, 20, 30, 40, 50]
saleInRs = [40000, 45000, 48000, 50000, 100000]

# Calculate size as 10 times discount
sizes = [10 * d for d in discount]

# Create scatter plot with customisations
plt.scatter(discount, saleInRs, 
           s=sizes,           # size of bubbles
           c='red',           # colour
           linewidth=3,       # border width
           marker='*',        # star marker
           edgecolor='blue')  # edge colour

plt.xlabel('Discount (%)')
plt.ylabel('Sale (in Rs)')
plt.title('Discount vs Sale')
plt.show()

Key lines explained:

• s=sizes sets the marker size; we compute sizes as a list where each element is 10 times the corresponding discount value (so the first bubble has size 100, the second 200, and so on).
• c='red' fills each marker with red.
• marker='*' changes the default circle to a star shape.
• linewidth=3 thickens the border of each marker to 3 points. …

Why scatter plot customisation matters

A scatter plot visualises the relationship between two continuous variables. In matplotlib, plt.scatter() accepts parameters that control every visual aspect of the markers: their size (s), face colour (c or color), edge colour (edgecolors), edge width (linewidths), and shape (marker). By customising these, you turn a plain scatter into an information-rich chart where a third variable (here, discount) is encoded in bubble size, making patterns immediately visible.

The question asks you to map discount to bubble size (scaled by 10), use a star marker, red fill, blue edge, and edge width of 3. Each parameter has a specific role:

• s (size): accepts a scalar or array; here we pass [10×d for d in discount] so each bubble's area reflects its discount.
• marker='*': switches from the default circle to a star.
• c='red' or color='red': sets the fill colour.
• edgecolors='blue': sets the edge (stroke) colour.
• linewidths=3: thickens the edge to 3 points.

The complete program

import matplotlib.pyplot as plt

# Data from Program 4-12
discount = [10, 20, 30, 40, 50]
saleInRs = [40000, 45000, 48000, 50000, 100000]

# Compute bubble sizes: 10 times each discount value
sizes = [10 * d for d in discount]

# Create the customised scatter plot
plt.scatter(discount, saleInRs, 
            s=sizes, 
            color='red', 
            marker='*', 
            edgecolors='blue', 
            linewidths=3)

plt.xlabel('Discount (%)')
plt.ylabel('Sale (₹)')
plt.title('Sales vs Discount with Customised Markers')
plt.grid(True, alpha=0.3)
plt.show()

Expected output

The chart displays:

• X-axis: Discount (%), ranging from 10 to 50.
• Y-axis: Sale (₹), ranging from 40,000 to 100,000.
• Markers: Five red stars (*), each with a thick blue edge (3 pt).
• Size progression: The star at discount=10 has size 100 (smallest), the star at discount=50 has size 500 (largest), making the growth in discount visually obvious.
• Grid: Light grey grid for easier reading.
• Title and labels: As shown in the code.

The final point (50, 100000) stands out both by position (top-right) and by its large star size, immediately signalling that higher discounts correlate with higher sales in this dataset.

Key lines explained

sizes = [10 * d for d in discount]

A list comprehension that scales each discount by 10. Matplotlib interprets s as the marker area in points², so this scaling ensures visible size differences.

plt.scatter(discount, saleInRs, s=sizes, …) …