Array Indexing and Slicing

Basic Indexing

Access elements by position, starting from 0.

code.py

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
print("First element:", arr[0])
print("Third element:", arr[2])
print("Last element:", arr[-1])

Output:

First element: 10
Third element: 30
Last element: 50

Negative indexing:

-1 is last element
-2 is second from end
-3 is third from end

Modifying Elements

code.py

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
arr[0] = 99
arr[-1] = 88
print(arr)

Output: [99 2 3 4 88]

Basic Slicing

Extract portions of array.

Syntax: [start:stop:step]

code.py

import numpy as np

arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

print("First 5:", arr[:5])
print("From index 3:", arr[3:])
print("Middle:", arr[3:7])
print("Every other:", arr[::2])
print("Reverse:", arr[::-1])

Output:

First 5: [0 1 2 3 4]
From index 3: [3 4 5 6 7 8 9]
Middle: [3 4 5 6]
Every other: [0 2 4 6 8]
Reverse: [9 8 7 6 5 4 3 2 1 0]

2D Array Indexing

Access rows and columns.

code.py

import numpy as np

matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print("Element at (0,0):", matrix[0, 0])
print("Element at (1,2):", matrix[1, 2])
print("Element at (2,1):", matrix[2, 1])

Output:

Element at (0,0): 1
Element at (1,2): 6
Element at (2,1): 8

Format: matrix[row, column]

Accessing Rows and Columns

code.py

import numpy as np

matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print("First row:", matrix[0])
print("Second row:", matrix[1])
print("First column:", matrix[:, 0])
print("Second column:", matrix[:, 1])

Output:

First row: [1 2 3]
Second row: [4 5 6]
First column: [1 4 7]
Second column: [2 5 8]

What : means:

matrix[0] - first row, all columns
matrix[:, 0] - all rows, first column

2D Slicing

Extract sub-matrices.

code.py

import numpy as np

matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

print("First 2 rows, first 2 columns:")
print(matrix[:2, :2])

print("All rows, columns 1-3:")
print(matrix[:, 1:3])

print("Rows 1-2, columns 2-3:")
print(matrix[1:3, 2:4])

Output:

[[1 2]
 [5 6]]

[[2 3]
 [6 7]
 [10 11]]

[[7 8]
 [11 12]]

Boolean Indexing

Filter elements using conditions.

code.py

import numpy as np

scores = np.array([78, 85, 92, 68, 95, 72])

high_scores = scores[scores > 80]
print("Scores above 80:", high_scores)

passing = scores[scores >= 70]
print("Passing scores:", passing)

Output:

Scores above 80: [85 92 95]
Passing scores: [78 85 92 95 72]

How it works:

scores > 80 creates boolean array [False, True, True, False, True, False]
Boolean array filters original array

Multiple Conditions

Combine conditions with & (and) and | (or).

code.py

import numpy as np

prices = np.array([45, 32, 67, 28, 51, 39])

affordable = prices[(prices >= 30) & (prices <= 50)]
print("Prices 30-50:", affordable)

extreme = prices[(prices < 30) | (prices > 60)]
print("Very cheap or expensive:", extreme)

Output:

Prices 30-50: [45 32 39]
Very cheap or expensive: [67 28]

Important: Use & for and, | for or, not "and"/"or".

Fancy Indexing

Select specific indices.

code.py

import numpy as np

arr = np.array([10, 20, 30, 40, 50, 60])
indices = [0, 2, 4]
selected = arr[indices]
print(selected)

Output: [10 30 50]

Multiple elements at once:

code.py

arr = np.array([100, 200, 300, 400, 500])
selected = arr[[0, 3, 4]]
print(selected)

Output: [100 400 500]

Modifying with Boolean Indexing

code.py

import numpy as np

scores = np.array([78, 65, 92, 58, 88])

scores[scores < 70] = 70
print("After curve:", scores)

Output: [78 70 92 70 88]

What this does: All scores below 70 become 70 (grade curve).

Practice Example

The scenario: Analyze and process sales data.

code.py

import numpy as np

sales = np.array([[150, 200, 180], [220, 190, 210], [170, 240, 200]])

print("Sales Data (Days × Products)")
print(sales)
print()

print("Day 1 sales:", sales[0])
print("Product 1 sales:", sales[:, 0])
print()

total_per_day = sales.sum(axis=1)
print("Total per day:", total_per_day)

total_per_product = sales.sum(axis=0)
print("Total per product:", total_per_product)
print()

high_sales = sales[sales > 200]
print("High sales (>200):", high_sales)
print("Count of high sales:", len(high_sales))
print()

best_day_idx = total_per_day.argmax()
print("Best day:", best_day_idx + 1)
print("Best day total:", total_per_day[best_day_idx])
print()

sales[sales < 180] = 180
print("After minimum adjustment:")
print(sales)

What this does:

Shows sales data (3 days, 3 products)
Accesses specific days and products
Calculates daily and product totals
Filters high-performing sales
Finds best sales day
Adjusts low sales to minimum

Where Function

Find positions where condition is true.

code.py

import numpy as np

arr = np.array([1, 5, 3, 8, 2, 9])
positions = np.where(arr > 4)
print("Positions where > 4:", positions)
print("Values:", arr[positions])

Output:

Positions where > 4: (array([1, 3, 5]),)
Values: [5 8 9]

Conditional Assignment

code.py

import numpy as np

scores = np.array([85, 92, 78, 95, 88])
grades = np.where(scores >= 90, "A", "B")
print(grades)

Output: ['B' 'A' 'B' 'A' 'B']

What this does: If score >= 90, assign "A", else assign "B".

Key Points to Remember

Indexing starts at 0. Use negative indices to count from end (-1 is last).

Slicing syntax: [start:stop:step]. stop is excluded, step is optional.

For 2D arrays: array[row, column]. Use : to select all rows or columns.

Boolean indexing filters with conditions: array[array > 5]. Use & for and, | for or.

Fancy indexing selects specific indices: array[[0, 2, 4]].

Common Mistakes

Mistake 1: Wrong 2D syntax

code.py

matrix[0][1]  # Works but slow
matrix[0, 1]  # Better, NumPy way

Mistake 2: Using "and" instead of &

code.py

arr[(arr > 5) and (arr < 10)]  # Error!
arr[(arr > 5) & (arr < 10)]  # Correct

Mistake 3: Forgetting parentheses

code.py

arr[arr > 5 & arr < 10]  # Wrong!
arr[(arr > 5) & (arr < 10)]  # Correct

Mistake 4: Slice without copy

code.py

subset = arr[0:3]  # This is a view
subset[0] = 99  # Changes original arr too!
subset = arr[0:3].copy()  # Independent copy

What's Next?

You now know how to access and extract array data. Next, you'll learn about array operations - mathematical operations, element-wise operations, and array arithmetic.