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.

Practice

1. What is the main purpose of using groupby() in sales data analysis?

easy

A. To sort sales data alphabetically

B. To delete duplicate sales records

C. To visualize sales trends with charts

D. To summarize sales data by categories like product or date

Solution

1. Step 1: Understand the role of groupby()

   The groupby() function groups data based on one or more columns, such as product or date.

2. Step 2: Identify its use in sales data

   Grouping allows us to summarize sales, like total sales per product or per month.

3. Final Answer:

   To summarize sales data by categories like product or date -> Option D

4. Quick Check:

   groupby() groups data for summary [OK]

Hint: Remember: groupby groups data to summarize by categories [OK]

Common Mistakes:

• Thinking groupby deletes data
• Confusing groupby with sorting
• Assuming groupby creates charts

2. Which of the following is the correct syntax to calculate total sales per product using pandas?

easy

A. df['sales'].groupby('product').sum()

B. df.groupby('product')['sales'].sum()

C. df.sum('sales').groupby('product')

D. df.groupby('sales')['product'].sum()

Solution

1. Step 1: Understand pandas groupby syntax

   The correct syntax groups by 'product' column, then sums the 'sales' column.

2. Step 2: Check each option

   df.groupby('product')['sales'].sum() correctly uses df.groupby('product')['sales'].sum(). Others misuse order or columns.

3. Final Answer:

   df.groupby('product')['sales'].sum() -> Option B

4. Quick Check:

   Group by product, then sum sales [OK]

Hint: Group first, then select column and sum [OK]

Common Mistakes:

• Calling sum() before groupby()
• Grouping by sales instead of product
• Selecting columns after sum()

3. Given the DataFrame df below, what is the output of df.groupby('product')['sales'].sum()?

  product  sales
0   A       100
1   B       200
2   A       150
3   B       100
4   C       300

medium

A. product\nA 250\nB 300\nC 300\nName: sales, dtype: int64

B. product\nA 100\nB 200\nC 300\nName: sales, dtype: int64

C. product\nA 150\nB 100\nC 300\nName: sales, dtype: int64

D. product\nA 350\nB 300\nC 300\nName: sales, dtype: int64

Solution

1. Step 1: Group sales by product

   Sum sales for each product: A = 100+150=250, B = 200+100=300, C = 300.

2. Step 2: Write the output as pandas Series

   The result shows sums indexed by product with name 'sales'.

3. Final Answer:

   product\nA 250\nB 300\nC 300\nName: sales, dtype: int64 -> Option A

4. Quick Check:

   Sum sales per product = 250, 300, 300 [OK]

Hint: Add sales values grouped by product [OK]

Common Mistakes:

• Adding sales incorrectly
• Confusing product labels
• Ignoring grouping before sum

4. The following code is intended to find the average sales per month, but it raises an error. What is the error?

df.groupby('month').mean('sales')

medium

A. mean() does not take any arguments; column selection must be before mean()

B. groupby() requires a list, not a string

C. mean() should be replaced with sum()

D. The DataFrame must be sorted before groupby()

Solution

1. Step 1: Check mean() function usage

   The mean() method does not accept column names as arguments.

2. Step 2: Correct usage for column selection

   Select the 'sales' column before calling mean(): df.groupby('month')['sales'].mean().

3. Final Answer:

   mean() does not take any arguments; column selection must be before mean() -> Option A

4. Quick Check:

   mean() no args; select column first [OK]

Hint: Select column before aggregation function [OK]

Common Mistakes:

• Passing column name inside mean()
• Using groupby with wrong argument type
• Thinking sorting is required before groupby

5. You have sales data with columns product, month, and sales. How do you find the top-selling product for each month?

hard

A. Use df.groupby('month')['sales'].max() without product info

B. Use df.groupby('product')['sales'].max() and sort by month

C. Use df.groupby(['month', 'product'])['sales'].sum().unstack().idxmax(axis=1)

D. Use df.sort_values('sales').groupby('month').first() without aggregation

Solution

1. Step 1: Group sales by month and product

   Sum sales for each product in each month using groupby(['month', 'product'])['sales'].sum().

2. Step 2: Reshape and find top product per month

   Use unstack() to make products columns, then idxmax(axis=1) to find product with max sales per month.

3. Final Answer:

   Use df.groupby(['month', 'product'])['sales'].sum().unstack().idxmax(axis=1) -> Option C

4. Quick Check:

   Group by month and product, then find max product [OK]

Hint: Group by month and product, then find max sales product [OK]

Common Mistakes:

• Ignoring product when finding max sales
• Using max() without grouping by product
• Sorting without aggregation