Learn Visualizing Campaign Results with matplotlib | Data Analysis for Campaign Performance

Visualizing data is a key part of communicating campaign performance to clients and team members. Well-designed charts and graphs can quickly highlight trends, successes, and areas for improvement, making complex results understandable at a glance. For digital agencies, effective visualizations support compelling client presentations and drive better internal analysis, helping you make informed decisions and clearly demonstrate your value.


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import pandas as pd
import matplotlib.pyplot as plt

# Sample campaign data
data = {
    "Campaign": ["Spring Launch", "Summer Sale", "Fall Promo", "Winter Closeout"],
    "Impressions": [12000, 18000, 15000, 10000]
}
df = pd.DataFrame(data)

# Create a bar chart for campaign impressions
plt.figure(figsize=(8, 5))
plt.bar(df["Campaign"], df["Impressions"], color="skyblue")
plt.title("Campaign Impressions")
plt.xlabel("Campaign")
plt.ylabel("Impressions")
plt.tight_layout()
plt.show()

The process of plotting with matplotlib begins by preparing your data, often with pandas. You then choose the appropriate chart type—such as a bar chart for comparing campaign impressions. Customizing your chart is important for clarity: set a descriptive title, label the axes, and select colors that make the information easy to interpret. Using plt.tight_layout() ensures nothing overlaps, and plt.show() displays the final result. These steps help you create professional visuals tailored to your audience.


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import pandas as pd
import matplotlib.pyplot as plt

# Sample campaign data with conversion rates
data = {
    "Campaign": ["Spring Launch", "Summer Sale", "Fall Promo", "Winter Closeout"],
    "Impressions": [12000, 18000, 15000, 10000],
    "Conversion Rate": [0.04, 0.05, 0.045, 0.03]
}
df = pd.DataFrame(data)

fig, ax1 = plt.subplots(figsize=(8, 5))

# Bar chart for impressions
bars = ax1.bar(df["Campaign"], df["Impressions"], color="skyblue", label="Impressions")
ax1.set_xlabel("Campaign")
ax1.set_ylabel("Impressions")
ax1.set_title("Campaign Impressions and Conversion Rates")

# Line plot for conversion rates
ax2 = ax1.twinx()
ax2.plot(df["Campaign"], df["Conversion Rate"], color="red", marker="o", label="Conversion Rate")
ax2.set_ylabel("Conversion Rate")

# Add legends
ax1.legend(loc="upper left")
ax2.legend(loc="upper right")

plt.tight_layout()
plt.show()

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Section 2. Chapter 2

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              123456789101112131415161718
            
import pandas as pd
import matplotlib.pyplot as plt

# Sample campaign data
data = {
    "Campaign": ["Spring Launch", "Summer Sale", "Fall Promo", "Winter Closeout"],
    "Impressions": [12000, 18000, 15000, 10000]
}
df = pd.DataFrame(data)

# Create a bar chart for campaign impressions
plt.figure(figsize=(8, 5))
plt.bar(df["Campaign"], df["Impressions"], color="skyblue")
plt.title("Campaign Impressions")
plt.xlabel("Campaign")
plt.ylabel("Impressions")
plt.tight_layout()
plt.show()


              123456789101112131415161718192021222324252627282930
            
import pandas as pd
import matplotlib.pyplot as plt

# Sample campaign data with conversion rates
data = {
    "Campaign": ["Spring Launch", "Summer Sale", "Fall Promo", "Winter Closeout"],
    "Impressions": [12000, 18000, 15000, 10000],
    "Conversion Rate": [0.04, 0.05, 0.045, 0.03]
}
df = pd.DataFrame(data)

fig, ax1 = plt.subplots(figsize=(8, 5))

# Bar chart for impressions
bars = ax1.bar(df["Campaign"], df["Impressions"], color="skyblue", label="Impressions")
ax1.set_xlabel("Campaign")
ax1.set_ylabel("Impressions")
ax1.set_title("Campaign Impressions and Conversion Rates")

# Line plot for conversion rates
ax2 = ax1.twinx()
ax2.plot(df["Campaign"], df["Conversion Rate"], color="red", marker="o", label="Conversion Rate")
ax2.set_ylabel("Conversion Rate")

# Add legends
ax1.legend(loc="upper left")
ax2.legend(loc="upper right")

plt.tight_layout()
plt.show()

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 2

Visualizing Campaign Results with matplotlib

1. Why are visualizations important in campaign reporting?

2. What does plt.bar() do in matplotlib?

3. How can you display multiple metrics on a single chart?

Visualizing Campaign Results with matplotlib

1. Why are visualizations important in campaign reporting?

2. What does plt.bar() do in matplotlib?

3. How can you display multiple metrics on a single chart?