NumPy

On This Page

• 13.1  Why NumPy?
• 13.2  Creating Arrays
• 13.3  Indexing
• 13.4  Slicing
• 13.5  Broadcasting
• 13.6  Mathematical Operations
• 13.7  Aggregation Functions
• 13.8  Reshaping Arrays
• 13.9  Practical Exercises
• 13.10  Module Quiz (MCQs)
• 13.11  Module Assignment
• 13.12  Interview Questions — Module 13
• 13.13  Student Notes Page

Module 13: NumPy

13.1  Why NumPy?

NumPy (Numerical Python) is the foundational library for numerical computing in Python. It introduces the array — a data structure that is far faster and more memory-efficient than a regular Python list for numeric operations, and is the backbone that Pandas itself is built on.

pip install numpy

import numpy as np

13.2  Creating Arrays

import numpy as np

marks = np.array([78, 85, 92, 66, 88])
print(marks)
print(type(marks))

matrix = np.array([[1,2,3],[4,5,6]])   # 2D array
print(matrix)

zeros = np.zeros(5)         # array of 5 zeros
ones = np.ones((2,3))       # 2x3 array of ones
seq = np.arange(0, 10, 2)   # like range(), but returns an array

13.3  Indexing

marks = np.array([78, 85, 92, 66, 88])
print(marks[0])       # first element
print(marks[-1])      # last element

matrix = np.array([[1,2,3],[4,5,6]])
print(matrix[0, 2])   # row 0, column 2 -> 3
print(matrix[1, :])   # entire second row

13.4  Slicing

marks = np.array([78, 85, 92, 66, 88, 95])
print(marks[1:4])     # index 1 to 3
print(marks[:3])      # first 3 elements
print(marks[::2])     # every 2nd element

13.5  Broadcasting

Broadcasting lets NumPy apply an operation to every element of an array automatically, without writing a loop — this is one of NumPy's most powerful features.

marks = np.array([70, 80, 90])
bonus_marks = marks + 5      # adds 5 to EVERY element automatically
print(bonus_marks)

scaled = marks * 1.1          # 10% boost to every element
print(scaled)

13.6  Mathematical Operations

a = np.array([10, 20, 30])
b = np.array([1, 2, 3])

print(a + b)     # [11 22 33]
print(a - b)     # [9 18 27]
print(a * b)     # [10 40 90]
print(a / b)     # [10. 10. 10.]

13.7  Aggregation Functions

marks = np.array([78, 85, 92, 66, 88])
print("Mean:", np.mean(marks))
print("Max:", np.max(marks))
print("Std Dev:", round(np.std(marks), 2))

13.8  Reshaping Arrays

reshape() lets you change an array's dimensions without changing its data — very useful when preparing data for analysis or ML models.

data = np.arange(1, 13)         # 1D array: 1 to 12
reshaped = data.reshape(3, 4)   # 3 rows, 4 columns
print(reshaped)

13.9  Practical Exercises

Basic (5 Questions)

1. Create a NumPy array of 6 exam marks and print its type.

2. Create a 2x3 array of zeros and a 3x2 array of ones.

3. Print the first and last element of an array using indexing.

4. Slice an array to get only the middle 3 elements.

5. Use np.arange() to create an array of even numbers from 0 to 20.

Intermediate (5 Questions)

1. Use broadcasting to add 10 bonus marks to an array of student scores.

2. Calculate the mean, max, min, and standard deviation of a marks array.

3. Create a 2D array (3x3) and access a specific row and a specific element.

4. Reshape a 1D array of 9 elements into a 3x3 grid.

5. Multiply two arrays element-wise and print the result.

Advanced (5 Questions)

1. Build a 'Grade Booster' that adds different bonus points to different subjects using broadcasting on a 2D array.

2. Given a 2D array of student marks (rows = students, columns = subjects), calculate each student's average using aggregation along an axis.

3. Reshape a dataset of 24 sales figures into a 4x6 grid representing 4 weeks x 6 days, then find the weekly totals.

4. Compare the execution speed of summing 1 million numbers using a Python loop vs. np.sum() (using the time module).

5. Build a simple 'Data Normalizer' that scales an array of marks to a 0-1 range using broadcasting: (x - min) / (max - min).

13.10  Module Quiz (MCQs)

13.11  Module Assignment

Assignment: Student Marks Analyzer with NumPy

• Create a 2D NumPy array with 5 students × 4 subjects of marks (your own sample data).
• Calculate each student's average marks using aggregation along the correct axis.
• Calculate each subject's class average using aggregation along the other axis.
• Use broadcasting to add 2 grace marks to every score, then recalculate the overall class average.

Expected Output: A script that computes per-student and per-subject averages from a 2D marks array, correctly applying grace marks using broadcasting.

13.12  Interview Questions — Module 13

• Q: Why is NumPy faster than regular Python lists for numeric work?
  A: NumPy arrays store data in contiguous memory blocks with a fixed data type and use vectorized, compiled C operations internally, avoiding the overhead of Python-level loops.
• Q: What is broadcasting in NumPy?
  A: Broadcasting is NumPy's ability to apply an operation across an entire array (or between arrays of different shapes) without explicitly writing a loop.
• Q: What is the difference between a Python list and a NumPy array?
  A: A NumPy array requires all elements to be the same data type and supports fast vectorized math, while a Python list can mix types but is slower for numeric operations.
• Q: What does reshape() do, and what condition must be met?
  A: reshape() changes an array's dimensions without changing its data, but the total number of elements must match exactly between the original and new shape.

13.13  Student Notes Page

Use this space to write down key points, doubts, and your own examples from today's session.