NumPy with Pandas integration - Mini Project: Build & Apply

NumPy with Pandas integration

📖 Scenario: You work in a small store and want to analyze sales data. You have sales numbers for different products stored in a NumPy array. You want to use Pandas to organize this data with product names and then calculate some statistics.

🎯 Goal: Create a Pandas DataFrame from a NumPy array of sales data with product names as the index. Then calculate the average sales using NumPy functions integrated with Pandas.

📋 What You'll Learn

Create a NumPy array with exact sales data

Create a Pandas DataFrame using the NumPy array and product names

Use a NumPy function to calculate the average sales from the DataFrame

Print the average sales value

💡 Why This Matters

🌍 Real World

Stores and businesses often collect sales data as numbers. Using NumPy and Pandas together helps organize and analyze this data quickly.

💼 Career

Data analysts and scientists use NumPy and Pandas daily to clean, organize, and analyze data for reports and decision making.

1

Create the sales data as a NumPy array

Create a NumPy array called sales_data with these exact values: 100, 150, 200, 130, 170.

NumPy

# Create the NumPy array sales_data with the given values
# Your code here

Need a hint?

Use np.array and pass the list of numbers inside square brackets.

2

Create a Pandas DataFrame with product names

Create a list called products with these exact names: 'Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'. Then create a Pandas DataFrame called df using sales_data as the data and products as the index.

NumPy

import pandas as pd

# Create the list products with the given names
# Create the DataFrame df using sales_data and products as index
# Your code here

Need a hint?

Use pd.DataFrame with sales_data as data, products as index, and set column name to 'Sales'.

3

Calculate the average sales using NumPy

Use the NumPy function np.mean() on the 'Sales' column of the DataFrame df to calculate the average sales. Store the result in a variable called average_sales.

NumPy

# Calculate average_sales using np.mean on df['Sales']
# Your code here

Need a hint?

Use np.mean() and pass df['Sales'] as the argument.

4

Print the average sales

Print the value of average_sales using a print() statement.

NumPy

# Print the average_sales value
# Your code here

Need a hint?

Use print(average_sales) to display the average sales.