Learn Challenge: Visualize Automated Metrics | Automating Product Management Workflows

As a product manager, you often need to present a snapshot of key product metrics—such as feature adoption rates, churn, and daily active users (DAU)—in a clear, visual format. Python, combined with the matplotlib library, enables you to quickly generate summary charts that can be embedded in reports or shared with stakeholders. Recapping what you have learned so far, visualizing multiple metrics in a single chart helps you compare trends at a glance and communicate results effectively. Saving these charts as image files ensures you have ready-to-use visuals for product documentation or presentations.


              12345678910111213141516171819202122232425262728293031
            
import matplotlib.pyplot as plt

# Hardcoded product metrics
metrics = {
    "Feature Adoption Rate": 0.67,
    "Churn Rate": 0.13,
    "DAU": 3200
}

# Prepare data for the bar chart
labels = list(metrics.keys())
values = list(metrics.values())

# Create the bar chart
plt.figure(figsize=(8, 5))
bars = plt.bar(labels, values, color=['skyblue', 'salmon', 'lightgreen'])

# Add labels and title
plt.ylabel("Value")
plt.title("Product Metrics Overview")

# Add value labels above bars
for bar in bars:
    height = bar.get_height()
    plt.annotate(f"{height}", xy=(bar.get_x() + bar.get_width() / 2, height),
                 xytext=(0, 3), textcoords="offset points", ha='center', va='bottom')

# Save the chart as an image file
plt.tight_layout()
plt.savefig("product_metrics_overview.png")
plt.close()

Task

Swipe to start coding

Write a script that visualizes and saves a summary chart of product metrics. You will use a dictionary of metrics with three key metrics: "Feature Adoption Rate", "Churn Rate", and "DAU".

Create a bar chart where each bar represents one of the metrics.
Add axis labels and a chart title.
Display the value of each metric above its respective bar.
Save the chart as an image file named "summary_product_metrics.png".

Solution

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 7

single

Ask AI

Ask anything or try one of the suggested questions to begin our chat

Swipe to show menu


              12345678910111213141516171819202122232425262728293031
            
import matplotlib.pyplot as plt

# Hardcoded product metrics
metrics = {
    "Feature Adoption Rate": 0.67,
    "Churn Rate": 0.13,
    "DAU": 3200
}

# Prepare data for the bar chart
labels = list(metrics.keys())
values = list(metrics.values())

# Create the bar chart
plt.figure(figsize=(8, 5))
bars = plt.bar(labels, values, color=['skyblue', 'salmon', 'lightgreen'])

# Add labels and title
plt.ylabel("Value")
plt.title("Product Metrics Overview")

# Add value labels above bars
for bar in bars:
    height = bar.get_height()
    plt.annotate(f"{height}", xy=(bar.get_x() + bar.get_width() / 2, height),
                 xytext=(0, 3), textcoords="offset points", ha='center', va='bottom')

# Save the chart as an image file
plt.tight_layout()
plt.savefig("product_metrics_overview.png")
plt.close()