Aprende 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.


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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()

Tarea

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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".

Solución

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Sección 3. Capítulo 7

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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()