Module 6 Lesson 3: NumPy Math and Stats

In the last lesson, we learned how to create arrays and perform simple math like addition. In this lesson, we’ll explore NumPy's built-in statistical functions and how to "filter" your data using conditional logic.

Lesson Overview

In this lesson, we will cover:

• Statistical Functions: Mean, Median, Sum, and Std.
• Axe and Dimensions: Calculating stats across rows or columns.
• Boolean Masking: Filtering data without using if-statements.
• Slicing Arrays: Getting exactly the data you need.

---

1. Statistical Powerhouse

NumPy can calculate statistics across an entire array instantly.

import numpy as np

scores = np.array([85, 90, 78, 92, 88])

print(f"Average: {np.mean(scores)}")
print(f"Highest: {np.max(scores)}")
print(f"Spread (Std Dev): {np.std(scores):.2f}")

---

2. Working with Axes

When you have a 2D array (a table), you can choose to calculate stats for the whole thing, for each row, or for each column.

• axis=0: Columns (Vertical).
• axis=1: Rows (Horizontal).

grades = np.array([
    [80, 85], # Student 1
    [90, 95]  # Student 2
])

print(np.mean(grades, axis=1)) # Output: [82.5, 92.5] (Averages for each student)

---

3. Filtering with Boolean Masks

This is the most "magical" part of NumPy. You can filter data using a logical condition.

ages = np.array([12, 25, 18, 40, 30])

# Create a "mask" (True/False list)
adult_mask = ages >= 18 

# Use the mask to get just the data you want
adults = ages[adult_mask]
print(adults) # Output: [25 18 40 30]

---

4. Slicing and Dicing

Slicing works just like Python lists, but you can do it on multiple dimensions.

arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4]) # Output: [20 30 40] (items at index 1, 2, 3)

---

Practice Exercise: The Rainfall Analyzer

1. Create a 2D NumPy array representing rainfall for 2 cities over 7 days (a 2x7 matrix).
2. Calculate the Total Rainfall for each city (hint: use axis=1).
3. Calculate the Max Rainfall recorded across the entire region.
4. Use a Boolean Mask to find and print only the days where rainfall was greater than 5.0.

---

Quick Knowledge Check

1. What does np.std() calculate?
2. What is the difference between axis=0 and axis=1 in a 2D array?
3. What is a Boolean Mask?
4. How would you find the median value of an array named data?

---

Key Takeaways

• NumPy handles complex stats with built-in, optimized functions.
• Axis parameters allow you to control the direction of your calculations.
• Boolean masking is a fast, expressive way to filter data.
• Mastering these tools is essential for modern data analysis.

---

What’s Next?

NumPy is for numbers. But what if your data has words, dates, and missing values? In Lesson 4, we’ll meet Pandas—the library that brings spreadsheet-style power to Python!