Learn Analyzing Campaign Metrics with pandas | Data Analysis for Campaign Performance

Understanding your campaign's success requires a clear grasp of key metrics and effective data analysis. In digital agencies, common campaign metrics include impressions (the number of times an ad is shown), clicks (how many times users interact with the ad), and conversions (actions like purchases or sign-ups resulting from the ad). Analyzing these metrics helps you measure performance, optimize strategies, and demonstrate value to clients. Without data analysis, you risk missing out on actionable insights that can improve future campaigns and maximize return on investment.


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import pandas as pd

# Create a DataFrame with campaign data
data = {
    "Campaign": ["Spring Sale", "Summer Launch", "Holiday Promo"],
    "Impressions": [15000, 22000, 18000],
    "Clicks": [300, 550, 400],
    "Conversions": [25, 40, 35]
}

df = pd.DataFrame(data)
print(df)

A pandas DataFrame organizes data into rows and columns, much like a spreadsheet or SQL table. In the DataFrame above, each row represents a campaign, while each column represents a metric: campaign name, impressions, clicks, and conversions. This tabular format makes it easy to access and analyze specific columns. For example, you can retrieve the Clicks column to see how each campaign performed in terms of user engagement, or use multiple columns together to calculate new metrics.


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# Calculate Click-Through Rate (CTR) and Conversion Rate for each campaign
df["CTR"] = df["Clicks"] / df["Impressions"] * 100
df["Conversion Rate"] = df["Conversions"] / df["Clicks"] * 100

print(df[["Campaign", "CTR", "Conversion Rate"]])

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Section 2. Chapter 1

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              123456789101112
            
import pandas as pd

# Create a DataFrame with campaign data
data = {
    "Campaign": ["Spring Sale", "Summer Launch", "Holiday Promo"],
    "Impressions": [15000, 22000, 18000],
    "Clicks": [300, 550, 400],
    "Conversions": [25, 40, 35]
}

df = pd.DataFrame(data)
print(df)


              12345
            
# Calculate Click-Through Rate (CTR) and Conversion Rate for each campaign
df["CTR"] = df["Clicks"] / df["Impressions"] * 100
df["Conversion Rate"] = df["Conversions"] / df["Clicks"] * 100

print(df[["Campaign", "CTR", "Conversion Rate"]])

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 1

Analyzing Campaign Metrics with pandas

1. What is the purpose of using pandas DataFrames for campaign data?

2. How is click-through rate (CTR) calculated?

3. Which pandas method retrieves a specific column from a DataFrame?

Analyzing Campaign Metrics with pandas

1. What is the purpose of using pandas DataFrames for campaign data?

2. How is click-through rate (CTR) calculated?

3. Which pandas method retrieves a specific column from a DataFrame?