Build a Sales Performance Dashboard with KPI Analysis

Intermediate40 min7 exercises105 XP

Prerequisites

Pandas GroupBy: Split-Apply-Combine for Powerful Aggregations Matplotlib: Create Line, Bar, Scatter, and Pie Charts

0/7 exercises

Every business runs on sales data. Managers want to know which products are selling, which regions are performing, and whether things are getting better or worse. In this project, you'll build a complete sales analysis pipeline that answers these questions.

You'll start with raw transaction data and build up to a formatted report with KPIs, breakdowns, and growth metrics. This is the kind of work a data analyst does every week.

Step 1: Create the Sales Dataset

Our dataset represents three months of sales transactions for a small electronics company. Each row is a single sale with the date, product, salesperson, region, quantity, and unit price.

The sales dataset

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Notice that we immediately calculate a revenue column by multiplying quantity by unit price. This is a derived column that we'll use throughout the analysis.

Exercise 1: Create and Prepare the Sales DataFrame

Write Code

Create a sales DataFrame from the provided data dictionary. Then:

1. Convert the date column to datetime using pd.to_datetime()

2. Add a revenue column (quantity * unit_price)

3. Add a month column that extracts the month name from the date (use .dt.strftime('%B') to get full month names like "January")

4. Print the total number of transactions and the columns list.

import pandas as pd

sales_data = {
    'date': ['2024-01-05', '2024-01-15', '2024-02-03', '2024-02-18', '2024-03-01', '2024-03-15'],
    'product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Phone', 'Tablet'],
    'quantity': [2, 5, 3, 1, 4, 2],
    'unit_price': [999.99, 699.99, 449.99, 999.99, 699.99, 449.99]
}

# Create DataFrame and add columns

Step 2: Calculate Key Performance Indicators (KPIs)

KPIs are the numbers that tell you how the business is doing at a glance. For a sales team, the most important ones are total revenue, number of transactions, average order value, and total units sold. Let's calculate them all.

Calculating basic KPIs

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Exercise 2: Calculate Sales KPIs

Write Code

Write a function calculate_kpis(df) that takes a sales DataFrame (with a revenue and quantity column) and returns a dictionary with these keys:

total_revenue: sum of all revenue

transaction_count: number of rows

avg_order_value: mean revenue per transaction

total_units: sum of all quantities

avg_unit_price: total revenue divided by total units

Print each KPI formatted with 2 decimal places.

import pandas as pd

def calculate_kpis(df):
    # Your code here
    pass

data = {
    'product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Phone'],
    'quantity': [2, 5, 3, 1, 4],
    'unit_price': [999.99, 699.99, 449.99, 999.99, 699.99],
}
df = pd.DataFrame(data)
df['revenue'] = df['quantity'] * df['unit_price']

kpis = calculate_kpis(df)
for key, value in kpis.items():
    print(f'{key}: {value:.2f}')

Step 3: Break Down Performance by Product and Region

Overall numbers are useful, but managers always want to drill down. Which product brings in the most revenue? Which region is underperforming? The groupby() method is how you answer these questions.

Exercise 3: Revenue by Product and Region

Write Code

Write a function breakdown_by(df, column) that:

1. Groups the DataFrame by the given column

2. Calculates total revenue, total units sold, and transaction count per group

3. Sorts by total revenue descending

4. Returns the result DataFrame

The returned DataFrame should have columns: total_revenue, total_units, transactions.

Call it for both 'product' and 'region' and print the results.

import pandas as pd

def breakdown_by(df, column):
    # Your code here
    pass

data = {
    'product': ['Laptop', 'Phone', 'Tablet', 'Laptop', 'Phone', 'Tablet'],
    'region': ['North', 'South', 'North', 'East', 'South', 'East'],
    'quantity': [2, 5, 3, 1, 4, 2],
    'unit_price': [999.99, 699.99, 449.99, 999.99, 699.99, 449.99]
}
df = pd.DataFrame(data)
df['revenue'] = df['quantity'] * df['unit_price']

print('=== By Product ===')
print(breakdown_by(df, 'product'))
print('\n=== By Region ===')
print(breakdown_by(df, 'region'))

Step 4: Find Top Performers

Sales managers love rankings. Who's the top salesperson? What's the single biggest sale? Let's write code that answers both.

Exercise 4: Find Top Performers

Write Code

Write a function find_top_performers(df) that prints:

1. The top salesperson by total revenue (print: Top salesperson: NAME ($REVENUE))

2. The top product by total units sold (print: Top product: NAME (UNITS units))

3. The largest single sale by revenue (print: Largest sale: $REVENUE by NAME)

Format all dollar amounts with 2 decimal places.

import pandas as pd

def find_top_performers(df):
    # Your code here
    pass

data = {
    'salesperson': ['Alice', 'Bob', 'Alice', 'Charlie', 'Bob'],
    'product': ['Laptop', 'Phone', 'Phone', 'Laptop', 'Tablet'],
    'quantity': [2, 5, 3, 1, 4],
    'unit_price': [999.99, 699.99, 699.99, 999.99, 449.99]
}
df = pd.DataFrame(data)
df['revenue'] = df['quantity'] * df['unit_price']

find_top_performers(df)

Step 5: Calculate Month-over-Month Growth

Trends matter more than snapshots. A $50,000 month sounds great, but if last month was $80,000, there's a problem. Month-over-month growth shows whether the business is improving or declining.

Calculating percentage change

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Exercise 5: Month-over-Month Growth

Write Code

Write a function monthly_growth(df) that:

1. Groups the DataFrame by the month column

2. Calculates total revenue per month

3. Calculates the percentage change from one month to the next

4. Prints each month's revenue and growth rate

Format: MONTH: $REVENUE (GROWTH%) where growth shows +/- sign.

For the first month (no previous month), print: MONTH: $REVENUE

The months should appear in order: January, February, March.

import pandas as pd

def monthly_growth(df):
    # Your code here
    pass

data = {
    'date': ['2024-01-05', '2024-01-20', '2024-02-10', '2024-02-25',
             '2024-03-05', '2024-03-15', '2024-03-25'],
    'product': ['A', 'B', 'A', 'B', 'A', 'B', 'A'],
    'quantity': [2, 3, 4, 2, 5, 3, 2],
    'unit_price': [100, 200, 100, 200, 100, 200, 100]
}
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df['revenue'] = df['quantity'] * df['unit_price']
df['month'] = df['date'].dt.strftime('%B')

monthly_growth(df)

Step 6: Create a Summary Report Function

Time to combine everything into a single report function. A good report function runs all the analysis and returns organized results. This makes it easy to generate reports for different time periods or datasets.

Exercise 6: Create a Summary Report

Write Code

Write a function create_report(df) that returns a dictionary with:

kpis: dict with total_revenue, transaction_count, avg_order_value

top_product: name of the product with highest total revenue

top_salesperson: name of the salesperson with highest total revenue

best_region: name of the region with highest total revenue

Then print the report in a readable format.

import pandas as pd

def create_report(df):
    # Your code here
    pass

data = {
    'product': ['Laptop', 'Phone', 'Laptop', 'Tablet', 'Phone'],
    'salesperson': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob'],
    'region': ['North', 'South', 'East', 'North', 'South'],
    'quantity': [2, 5, 1, 3, 4],
    'unit_price': [999.99, 699.99, 999.99, 449.99, 699.99]
}
df = pd.DataFrame(data)
df['revenue'] = df['quantity'] * df['unit_price']

report = create_report(df)
print(f'Total Revenue: ${report["kpis"]["total_revenue"]:.2f}')
print(f'Transactions: {report["kpis"]["transaction_count"]}')
print(f'Avg Order: ${report["kpis"]["avg_order_value"]:.2f}')
print(f'Top Product: {report["top_product"]}')
print(f'Top Salesperson: {report["top_salesperson"]}')
print(f'Best Region: {report["best_region"]}')

Step 7: Generate a Formatted Report

Data analysis is only useful if people can read it. The final step is turning raw numbers into a nicely formatted text report. Good formatting uses alignment, separators, and clear labels to make the data scannable.

Exercise 7: Generate a Formatted Report

Write Code

Write a function format_report(df) that prints a complete formatted sales report. The report should include:

1. A header with the title "SALES PERFORMANCE REPORT"

2. KPI section showing total revenue, transaction count, and average order value

3. Top performers section showing the top product and top salesperson

4. Use = and - characters as separators

Match this exact format:

================================
  SALES PERFORMANCE REPORT
================================
Total Revenue:    $8849.86
Transactions:     5
Avg Order Value:  $1769.97
--------------------------------
Top Product:      Phone
Top Salesperson:  Bob
================================

import pandas as pd

def format_report(df):
    # Your code here
    pass

data = {
    'product': ['Laptop', 'Phone', 'Laptop', 'Tablet', 'Phone'],
    'salesperson': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob'],
    'quantity': [2, 5, 1, 3, 4],
    'unit_price': [999.99, 699.99, 999.99, 449.99, 699.99]
}
df = pd.DataFrame(data)
df['revenue'] = df['quantity'] * df['unit_price']

format_report(df)

What You Built

You built a complete sales analysis pipeline that transforms raw transaction data into actionable insights:
---	---	---
Data prep	Convert types, add derived columns	`pd.to_datetime()`, arithmetic
KPIs	Calculate summary metrics	`.sum()`, `.mean()`, `len()`
Breakdowns	Group by category	`.groupby().agg()`
Rankings	Find top performers	`.idxmax()`
Growth	Track trends over time	`.pct_change()`
Reporting	Present results clearly	f-string formatting

This is the exact workflow data analysts follow in real companies. The dataset gets bigger and the analysis gets more sophisticated, but the pattern stays the same: load, prepare, analyze, report.

Step 1: Create the Sales Dataset

Step 2: Calculate Key Performance Indicators (KPIs)

Step 3: Break Down Performance by Product and Region

Step 4: Find Top Performers

Step 5: Calculate Month-over-Month Growth

Step 6: Create a Summary Report Function

Step 7: Generate a Formatted Report

What You Built

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