🎭 Boolean Indexing

Boolean indexing filters data using True/False conditions. Create logical masks to find values that meet specific criteria.

import numpy as np

# Boolean indexing overview
scores = np.array([85, 92, 78, 96, 89, 74, 88, 93])
names = np.array(['Alice', 'Bob', 'Carol', 'David', 
                  'Eve', 'Frank', 'Grace', 'Henry'])

# Create boolean mask
high_scores = scores > 90
print(f"High scores mask: {high_scores}")
print(f"High performers: {names[high_scores]}")
print(f"Their scores: {scores[high_scores]}")

🔍 Creating Boolean Masks

Boolean masks are arrays of True/False values that act as filters.

Basic Comparisons

import numpy as np

temperatures = np.array([22, 25, 18, 30, 27, 15, 32, 24])

# Create different masks
hot_days = temperatures > 25
cold_days = temperatures < 20
perfect_days = temperatures == 25

print(f"Temperatures: {temperatures}")
print(f"Hot days (>25°C): {hot_days}")
print(f"Number of hot days: {hot_days.sum()}")

String Comparisons

import numpy as np

fruits = np.array(['apple', 'banana', 'cherry', 'date'])
colors = np.array(['red', 'yellow', 'red', 'brown'])

# String conditions
red_fruits = colors == 'red'
has_e = np.char.find(fruits, 'e') >= 0

print(f"Red fruits: {fruits[red_fruits]}")
print(f"Contains 'e': {fruits[has_e]}")

🔗 Combining Conditions

Use logical operators to combine multiple conditions.

AND Conditions

import numpy as np

scores = np.array([85, 92, 78, 96, 89, 74, 88, 93])
ages = np.array([20, 22, 19, 21, 23, 20, 22, 21])
names = np.array(['Alice', 'Bob', 'Carol', 'David', 
                  'Eve', 'Frank', 'Grace', 'Henry'])

# Multiple conditions with AND
high_achievers = (scores > 85) & (ages < 22)
print(f"High achievers under 22: {names[high_achievers]}")

# Range conditions
good_range = (scores >= 80) & (scores <= 90)
print(f"Scores 80-90: {names[good_range]}")

OR Conditions

import numpy as np

scores = np.array([85, 92, 78, 96, 89, 74, 88, 93])
names = np.array(['Alice', 'Bob', 'Carol', 'David', 
                  'Eve', 'Frank', 'Grace', 'Henry'])

# Either condition true
extreme_scores = (scores > 95) | (scores < 75)
print(f"Very high or low scores: {names[extreme_scores]}")

# Multiple OR conditions
need_help = (scores < 80) | (scores == 78)
print(f"Students needing help: {names[need_help]}")

NOT Conditions

import numpy as np

scores = np.array([85, 92, 78, 96, 89, 74, 88, 93])
names = np.array(['Alice', 'Bob', 'Carol', 'David', 
                  'Eve', 'Frank', 'Grace', 'Henry'])

# NOT operator
not_excellent = ~(scores > 90)
print(f"Not excellent (≤90): {names[not_excellent]}")

# Combining NOT with other conditions
decent_not_great = (scores >= 80) & ~(scores > 90)
print(f"Decent but not great: {names[decent_not_great]}")

🎯 2D Boolean Indexing

Apply boolean conditions to multi-dimensional arrays.

Row-wise Filtering

import numpy as np

# Student data: [Math, Science, English]
grades = np.array([[85, 92, 78],   # Alice
                   [79, 85, 91],   # Bob
                   [94, 89, 96],   # Carol
                   [72, 78, 74]])  # David

students = np.array(['Alice', 'Bob', 'Carol', 'David'])

# Filter based on Math scores
good_math = grades[:, 0] > 80
print(f"Good at Math: {students[good_math]}")

# Filter based on average
averages = grades.mean(axis=1)
high_average = averages > 85
print(f"High average: {students[high_average]}")

Element-wise Filtering

import numpy as np

data = np.array([[10, 25, 30],
                 [15, 5, 35],
                 [20, 45, 10]])

# Find all values > 20
high_values = data > 20
print(f"Original: \n{data}")
print(f"Values > 20: {data[high_values]}")

# Replace values conditionally
data_copy = data.copy()
data_copy[data_copy < 15] = 0  # Set low values to 0
print(f"After replacing <15 with 0: \n{data_copy}")

🧮 Using np.where()

np.where() provides conditional selection with custom values.

Basic np.where()

import numpy as np

scores = np.array([85, 92, 78, 96, 89, 74, 88, 93])

# Convert to pass/fail
pass_fail = np.where(scores >= 80, 'Pass', 'Fail')
print(f"Scores: {scores}")
print(f"Result: {pass_fail}")

# Convert to letter grades
grades = np.where(scores >= 90, 'A',
          np.where(scores >= 80, 'B', 'C'))
print(f"Letter grades: {grades}")

Advanced np.where()

import numpy as np

temperatures = np.array([22, 35, 18, 40, 27, 15, 32])

# Multiple conditions
weather = np.where(temperatures > 30, 'Hot',
          np.where(temperatures < 20, 'Cold', 'Nice'))

print(f"Temperatures: {temperatures}")
print(f"Weather: {weather}")

# Mathematical operations
adjusted = np.where(temperatures > 30, 
                   temperatures - 5,  # Cool down hot days
                   temperatures + 2)  # Warm up other days
print(f"Adjusted temps: {adjusted}")

🧠 Practice Exercise

import numpy as np

# Sales data: [Q1, Q2, Q3, Q4]
sales = np.array([[120, 135, 145, 160],  # Store A
                  [98, 112, 125, 140],   # Store B
                  [156, 167, 175, 185],  # Store C
                  [89, 95, 105, 125]])   # Store D

stores = np.array(['Store A', 'Store B', 'Store C', 'Store D'])

# Find stores with strong Q4
strong_q4 = sales[:, 3] > 150
print(f"Strong Q4 (>150): {stores[strong_q4]}")

# Find consistent performers (all quarters > 100)
consistent = (sales > 100).all(axis=1)
print(f"Consistent performers: {stores[consistent]}")

# Find growth pattern (Q4 > Q1)
growing = sales[:, 3] > sales[:, 0]
growth_amount = sales[:, 3] - sales[:, 0]
print(f"Growing stores: {stores[growing]}")
print(f"Growth amounts: {growth_amount[growing]}")

🎯 Key Takeaways

🚀 What's Next?

You've mastered array indexing! Next, learn array manipulation techniques.

Continue to: Array Manipulation

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🎭 Boolean Indexing

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