Learn Building Client Reports with pandas | Reporting and Visualization for Clients

When you deliver results to clients in a digital agency, the quality of your reports can make a big difference in client satisfaction and retention. Client reports need to clearly show campaign performance, highlight key metrics, and summarize results in a way that is easy for clients to understand. The main goals of these reports are to provide transparency, demonstrate the value your agency delivers, and help clients make informed decisions about their marketing strategies. Using Python and the pandas library, you can efficiently aggregate, summarize, and format campaign data, making the reporting process faster and more reliable.


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import pandas as pd

# Sample campaign data
data = {
    "client": ["Acme Corp", "Acme Corp", "Beta LLC", "Beta LLC", "Acme Corp"],
    "campaign": ["Spring Sale", "Summer Sale", "Launch", "Holiday", "Fall Sale"],
    "spend": [1200, 1500, 800, 950, 1100],
    "conversions": [45, 60, 30, 35, 50]
}
df = pd.DataFrame(data)

# Group by client and calculate total spend and total conversions
summary = df.groupby("client").agg({
    "spend": "sum",
    "conversions": "sum"
}).reset_index()

print(summary)

Grouping data by client is a common step when preparing reports, because it allows you to see overall performance at the client level rather than just for individual campaigns. The groupby method in pandas lets you organize your data into groups based on one or more columns, then apply aggregation functions such as sum, mean, or count to calculate totals or averages for each group. This is especially useful for reporting, because clients are interested in their overall spend and conversions, not just the details of each campaign. Summarizing data in this way makes it easier to compare results across clients, spot trends, and communicate clear insights.


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# Format the summary for clear presentation
summary.columns = ["Client", "Total Spend ($)", "Total Conversions"]
summary["Total Spend ($)"] = summary["Total Spend ($)"].map("${:,.2f}".format)

# Display the formatted summary
print(summary.to_string(index=False))

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Section 3. Chapter 1

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              123456789101112131415161718
            
import pandas as pd

# Sample campaign data
data = {
    "client": ["Acme Corp", "Acme Corp", "Beta LLC", "Beta LLC", "Acme Corp"],
    "campaign": ["Spring Sale", "Summer Sale", "Launch", "Holiday", "Fall Sale"],
    "spend": [1200, 1500, 800, 950, 1100],
    "conversions": [45, 60, 30, 35, 50]
}
df = pd.DataFrame(data)

# Group by client and calculate total spend and total conversions
summary = df.groupby("client").agg({
    "spend": "sum",
    "conversions": "sum"
}).reset_index()

print(summary)


              123456
            
# Format the summary for clear presentation
summary.columns = ["Client", "Total Spend ($)", "Total Conversions"]
summary["Total Spend ($)"] = summary["Total Spend ($)"].map("${:,.2f}".format)

# Display the formatted summary
print(summary.to_string(index=False))

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 1

Building Client Reports with pandas

1. What is the purpose of grouping data in client reports?

2. How does the groupby() method work in pandas?

3. Why is data formatting important in reports?

Building Client Reports with pandas

1. What is the purpose of grouping data in client reports?

2. How does the groupby() method work in pandas?

3. Why is data formatting important in reports?