NumPy Project – End-to-End Numerical Analysis

In this final lesson, you will build a complete NumPy-based project that applies everything you learned so far. The goal is to simulate a real-world numerical workflow using only NumPy.

This project mirrors how NumPy is used in analytics, engineering, and machine learning pipelines before higher-level libraries are applied.

Project Scenario

You are given numerical data representing daily sales (in units) for a product across multiple stores. Your task is to analyze, transform, and summarize the data.

The dataset contains:

• Rows → Days
• Columns → Stores

Step 1: Create the Dataset

import numpy as np

sales = np.array([
    [120, 135, 150],
    [130, 140, 160],
    [125, 138, 155],
    [140, 150, 170],
    [145, 160, 180]
])

print(sales)

Each column represents a store, and each row represents a day of sales.

Step 2: Basic Statistics

Calculate total, average, and maximum sales.

total_sales = sales.sum()
avg_sales = sales.mean()
max_sales = sales.max()

print(total_sales, avg_sales, max_sales)

These statistics provide a quick overview of business performance.

Step 3: Store-Wise Analysis

Analyze sales per store.

store_totals = sales.sum(axis=0)
store_averages = sales.mean(axis=0)

print(store_totals)
print(store_averages)

This shows which store performs best over time.

Step 4: Day-Wise Trends

Analyze daily performance across all stores.

daily_totals = sales.sum(axis=1)
print(daily_totals)

This helps identify high-performing or low-performing days.

Step 5: Normalizing the Data

Normalize the sales data to compare trends fairly.

mean = sales.mean(axis=0)
std = sales.std(axis=0)

normalized_sales = (sales - mean) / std
print(normalized_sales)

Normalization is essential in machine learning and analytics workflows.

Step 6: Performance Comparison Using Vectorization

Apply a growth factor to simulate a 10% increase in sales.

projected_sales = sales * 1.10
print(projected_sales)

Vectorized operations allow fast transformations without loops.

Step 7: Identifying Best Performing Store

Find the store with the highest total sales.

best_store = np.argmax(store_totals)
print("Best performing store index:", best_store)

This technique is commonly used in ranking and optimization problems.

Project Summary

In this project, you:

• Created and structured numerical data
• Performed aggregation and statistics
• Used axis-based operations
• Applied normalization
• Used vectorized computation

These steps represent a complete NumPy workflow used in real applications.

Where NumPy Fits Next

After NumPy, most workflows move to:

• Pandas for labeled data analysis
• Matplotlib for visualization
• Scikit-learn for machine learning

A strong NumPy foundation makes all of these easier to master.

Course Completion

You have successfully completed the NumPy course. You now understand numerical computing at a professional level.