Lære 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.


              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()

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.


              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()

Var alt klart?

Tak for dine kommentarer!

Sektion 2. Kapitel 2

Spørg AI

Spørg om hvad som helst eller prøv et af de foreslåede spørgsmål for at starte vores chat

Suggested prompts:

Can you explain how to interpret the combined bar and line chart?

What other metrics can I visualize alongside impressions and conversion rates?

How can I customize the appearance of these charts further?

Stryg for at vise menuen


              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()

Var alt klart?

Tak for dine kommentarer!

Sektion 2. Kapitel 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?