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.