Sales data analysis pattern in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ data_output

intermediate

Calculate total sales per product

You have a sales dataset with columns: product, units_sold, and unit_price. What is the total sales amount per product?

Data Analysis Python

import pandas as pd

data = pd.DataFrame({
    'product': ['A', 'B', 'A', 'C', 'B'],
    'units_sold': [10, 5, 7, 3, 8],
    'unit_price': [2.5, 3.0, 2.5, 4.0, 3.0]
})

# Calculate total sales per product
sales = data.copy()
sales['total'] = sales['units_sold'] * sales['unit_price']
result = sales.groupby('product')['total'].sum().sort_index()

A {'A': 42.5, 'B': 39.0, 'C': 12.0}

B {'A': 17.5, 'B': 13.0, 'C': 12.0}

C {'A': 42.5, 'B': 26.0, 'C': 12.0}

D {'A': 42.5, 'B': 39.0, 'C': 15.0}

💡 Hint

Multiply units sold by unit price for each row, then sum totals by product.

We multiply units_sold by unit_price for each sale, then group by product and sum these totals. This gives total sales per product.

🧠 Conceptual

intermediate

Understanding sales data time grouping

You want to analyze monthly sales totals from daily sales data. Which step is essential before grouping by month?

A Convert the sales amount to integers

B Convert the date column to datetime type

C Remove duplicate sales records

D Sort the data by product name

💡 Hint

Grouping by month requires working with date information properly.

To group by month, the date column must be in datetime format so you can extract month and year easily.

🔧 Debug

advanced

Fix the error in sales aggregation code

What error does this code raise?

import pandas as pd
data = pd.DataFrame({'product': ['A', 'B'], 'units_sold': [5, 3]})
total_sales = data.groupby('product')['units_sold' * 2].sum()

Data Analysis Python

import pandas as pd
data = pd.DataFrame({'product': ['A', 'B'], 'units_sold': [5, 3]})
total_sales = data.groupby('product')['units_sold' * 2].sum()

A KeyError: 'units_sold * 2'

B TypeError: unsupported operand type(s) for *: 'str' and 'int'

C SyntaxError: invalid syntax

D ValueError: No numeric types to aggregate

💡 Hint

Look carefully at the expression inside the brackets.

The expression 'units_sold' * 2 evaluates to 'units_soldunits_sold' due to string repetition, which is not a column name, causing a KeyError.

❓ visualization

advanced

Identify the correct sales trend plot

You have monthly sales totals for three products. Which plot code correctly shows a line chart with months on the x-axis and sales on the y-axis for each product?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

sales = pd.DataFrame({
    'month': ['Jan', 'Feb', 'Mar'],
    'A': [100, 120, 130],
    'B': [90, 110, 115],
    'C': [80, 85, 90]
})

A

plt.plot(sales['month'], sales['A'], sales['B'], sales['C'])
plt.show()

B

sales.plot(y='month', x=['A', 'B', 'C'], kind='line')
plt.show()

C

sales.plot(x='month', y='A', kind='bar')
plt.show()

D

sales.plot(x='month', y=['A', 'B', 'C'], kind='line')
plt.show()

💡 Hint

Check which argument sets the x-axis and which sets the y-axis in plot().

Option D correctly sets x='month' and y=['A','B','C'] to plot lines for each product over months.

🚀 Application

expert

Find the product with highest average daily sales

Given daily sales data with columns date, product, and units_sold, which code finds the product with the highest average daily units sold?

Data Analysis Python

import pandas as pd

data = pd.DataFrame({
    'date': ['2024-01-01', '2024-01-01', '2024-01-02', '2024-01-02', '2024-01-03'],
    'product': ['A', 'B', 'A', 'B', 'A'],
    'units_sold': [10, 5, 15, 7, 20]
})
data['date'] = pd.to_datetime(data['date'])

A

avg_sales = data.groupby('date')['units_sold'].mean()
result = avg_sales.idxmax()

B

avg_sales = data.groupby('product')['units_sold'].mean()
result = avg_sales.idxmax()

C

avg_sales = data.groupby(['product', 'date'])['units_sold'].sum().groupby('product').mean()
result = avg_sales.idxmax()

D

avg_sales = data.groupby('product')['units_sold'].sum()
result = avg_sales.idxmax()

💡 Hint

Calculate daily totals per product first, then average those daily totals per product.

Option C first sums units sold per product per day, then averages these daily sums per product to find the highest average daily sales.