Sales data analysis pattern in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the sales data from a CSV file into a DataFrame.

Data Analysis Python

import pandas as pd
sales_data = pd.read_csv([1])

Drag options to blanks, or click blank then click option'

A'sales.csv'

Bsales.csv

Cdata.csv

D"sales_data"

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the file name.

Using a variable name instead of a string.

The file name must be a string in quotes. 'sales.csv' is the correct file name as a string.

2fill in blank

medium

Complete the code to calculate the total sales by summing the 'Amount' column.

Data Analysis Python

total_sales = sales_data[[1]].sum()

Drag options to blanks, or click blank then click option'

A'Total'

B'Price'

C'Amount'

D'Sales'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name.

Forgetting quotes around the column name.

The sales amounts are stored in the 'Amount' column, so we sum that column.

3fill in blank

hard

Fix the error in the code to group sales by 'Region' and calculate the mean sales amount.

Data Analysis Python

mean_sales_by_region = sales_data.groupby([1])['Amount'].mean()

Drag options to blanks, or click blank then click option'

A'Region'

BRegion

Cregion

D"region"

Attempts:

3 left

💡 Hint

Common Mistakes

Using the column name without quotes.

Using incorrect capitalization.

The groupby method requires the column name as a string in quotes. 'Region' is correct.

4fill in blank

hard

Fill both blanks to create a dictionary of total sales for products with sales greater than 1000.

Data Analysis Python

high_sales = {product: sales_data[sales_data['Product'] == product][[1]].sum() for product in sales_data['Product'].unique() if sales_data[sales_data['Product'] == product][[2]].sum() > 1000}

Drag options to blanks, or click blank then click option'

A'Amount'

B'Sales'

C'Price'

D'Quantity'

Attempts:

3 left

💡 Hint

Common Mistakes

Using different column names for the sums.

Using columns that do not represent sales amount.

We sum the 'Amount' column to get total sales and filter products with total sales over 1000.

5fill in blank

hard

Fill all three blanks to create a dictionary of average sales per region for products with average sales above 500.

Data Analysis Python

avg_sales = {region: sales_data[sales_data['Region'] == region][[1]].mean() for region in sales_data['Region'].unique() if sales_data[sales_data['Region'] == region][[2]].mean() > [3]

Drag options to blanks, or click blank then click option'

A'Amount'

C500

D1000

Attempts:

3 left

💡 Hint

Common Mistakes

Using different columns for mean calculation and filtering.

Using the wrong threshold value.

We calculate the mean of 'Amount' for each region and filter those with average sales above 500.

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