Negative indexing in NumPy - Mini Project: Build & Apply

Negative Indexing with NumPy Arrays

📖 Scenario: You are working with daily temperature data for a week. You want to access specific days using negative indexing, which counts from the end of the data.

🎯 Goal: Learn how to use negative indexing on a NumPy array to access elements from the end.

📋 What You'll Learn

Create a NumPy array with exact temperature values

Create a variable to hold the negative index

Use negative indexing to get the temperature for that day

Print the temperature value

💡 Why This Matters

🌍 Real World

Negative indexing helps quickly access recent data points, like the last few days of temperature, sales, or stock prices, without counting the total length.

💼 Career

Data scientists often use negative indexing to efficiently retrieve recent records from datasets, making data analysis faster and code cleaner.

1

Create a NumPy array with temperatures

Import NumPy as np and create a NumPy array called temps with these exact values: 23, 25, 22, 20, 19, 21, 24.

NumPy

# Import numpy as np and create the array temps
# Your code here

Hint

Use np.array([...]) to create the array with the exact values.

2

Create a variable for negative index

Create a variable called neg_index and set it to -2 to represent the second last day.

NumPy

import numpy as np

temps = np.array([23, 25, 22, 20, 19, 21, 24])

# Create neg_index variable and set it to -2
# Your code here

Hint

Just assign -2 to the variable neg_index.

3

Use negative indexing to get temperature

Create a variable called temp_neg and assign it the value from temps at the negative index neg_index.

NumPy

import numpy as np

temps = np.array([23, 25, 22, 20, 19, 21, 24])

neg_index = -2

# Use negative indexing to get the temperature
# Your code here

Hint

Use temps[neg_index] to get the value at the negative index.

4

Print the temperature from negative index

Print the value of temp_neg to display the temperature for the second last day.

NumPy

import numpy as np

temps = np.array([23, 25, 22, 20, 19, 21, 24])

neg_index = -2

temp_neg = temps[neg_index]

# Print the temperature value
# Your code here

Hint

Use print(temp_neg) to show the temperature.

Practice

1. What does negative indexing in a numpy array allow you to do?

easy

A. Sort the array in descending order

B. Access elements starting from the end of the array

C. Change the size of the array

D. Access elements only at the beginning of the array

Solution

1. Step 1: Understand negative indexing concept

   Negative indexing means counting elements from the end of the array, starting at -1 for the last element.

2. Step 2: Compare options with concept

   Only Access elements starting from the end of the array correctly describes accessing elements from the end using negative indexes.

3. Final Answer:

   Access elements starting from the end of the array -> Option B

4. Quick Check:

   Negative indexing = Access from end [OK]

Hint: Negative index -1 means last element [OK]

Common Mistakes:

• Thinking negative index changes array size
• Confusing negative indexing with sorting
• Assuming negative index accesses start only at beginning

2. Which of the following is the correct syntax to get the last element of a numpy array arr using negative indexing?

easy

A. arr[0]

B. arr[1]

C. arr[-0]

D. arr[-1]

Solution

1. Step 1: Recall negative index for last element

   In numpy, -1 is used to access the last element of an array.

2. Step 2: Check each option

   arr[-1] uses arr[-1] which correctly accesses the last element. Others do not.

3. Final Answer:

   arr[-1] -> Option D

4. Quick Check:

   Last element = arr[-1] [OK]

Hint: Use arr[-1] for last element [OK]

Common Mistakes:

• Using arr[-0] which is same as arr[0]
• Using positive index 1 which is second element
• Confusing zero index with negative zero

3. Given the numpy array arr = np.array([10, 20, 30, 40, 50]), what is the output of arr[-3]?

medium

A. 20

B. 40

C. 30

D. 50

Solution

1. Step 1: Understand negative index -3

   Negative index -3 means counting from the end: last is -1 (50), second last -2 (40), third last -3 (30).

2. Step 2: Identify element at index -3

   The element at arr[-3] is 30.

3. Final Answer:

   30 -> Option C

4. Quick Check:

   arr[-3] = 30 [OK]

Hint: Count backward: -1 last, -2 second last, -3 third last [OK]

Common Mistakes:

• Counting negative index from start instead of end
• Mixing up index -3 with positive index 3
• Off-by-one errors in counting

4. Consider the code snippet:

import numpy as np
arr = np.array([5, 10, 15, 20])
print(arr[-5])

What is the error in this code?

medium

A. IndexError: index -5 is out of bounds for axis 0 with size 4

B. SyntaxError due to negative index

C. Prints 5

D. Prints 20

Solution

1. Step 1: Check array size and index used

   Array has 4 elements, valid negative indexes are -1 to -4.

2. Step 2: Understand index -5 usage

   Index -5 is outside valid range, causing IndexError.

3. Final Answer:

   IndexError: index -5 is out of bounds for axis 0 with size 4 -> Option A

4. Quick Check:

   Negative index out of range = IndexError [OK]

Hint: Negative index must be >= -array length [OK]

Common Mistakes:

• Assuming negative index wraps around infinitely
• Thinking negative index causes syntax error
• Expecting last element instead of error

5. You have a 2D numpy array arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]). What does arr[-1, -2] return?

hard

A. 8

B. 7

C. 6

D. 9

Solution

1. Step 1: Understand negative indexing in 2D arrays

   For rows, -1 means last row: [7, 8, 9]. For columns, -2 means second last column.

2. Step 2: Identify element at arr[-1, -2]

   Last row is [7, 8, 9], second last column is element 8.

3. Final Answer:

   8 -> Option A

4. Quick Check:

   arr[-1, -2] = 8 [OK]

Hint: Negative indexes count from end in each dimension [OK]

Common Mistakes:

• Mixing row and column indexes
• Using positive indexes instead of negative
• Confusing which axis negative index applies to