📘 Introduction to NumPy

What is NumPy?

NumPy (Numerical Python) is a powerful Python library used for numerical computations. It provides support for arrays, matrices, and many mathematical functions.

Why use NumPy?

• Faster than Python lists
• Supports multi-dimensional arrays
• Optimized mathematical functions
• Useful for data science, ML, and scientific computing

Installation

pip install numpy

Importing NumPy

import numpy as np

Real-Life Example:

You can analyze thousands of sales records, do calculations, and generate statistics quickly using NumPy arrays.

🔹 Creating Arrays

arr1 = np.array([1, 2, 3])          # 1D array
arr2 = np.array([[1, 2], [3, 4]])   # 2D array

Array Attributes

print(arr2.shape)   # (2, 2)
print(arr2.ndim)    # 2
print(arr2.dtype)   # int32 or int64

Real-Life Example:

Use 2D arrays to represent Excel-like tables (e.g., student marks, sales data).

🔹 Indexing and Slicing

arr = np.array([10, 20, 30, 40])
print(arr[1:3])  # Output: [20 30]

arr2 = np.array([[1, 2], [3, 4]])
print(arr2[1, 0])  # Output: 3

Real-Life Example:

Get a student’s marks from a 2D array of student results.

🔹 Array Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)  # [5 7 9]
print(a * b)  # [4 10 18]

Broadcasting

a = np.array([1, 2, 3])
print(a + 10)  # [11 12 13]

Real-Life Example:

Apply discount or tax to a list of prices using broadcasting.

🔹 Array Functions

arr = np.array([1, 2, 3, 4])
print(np.sum(arr))       # 10
print(np.mean(arr))      # 2.5
print(np.max(arr))       # 4

Real-Life Example:

Calculate total or average marks of students.

🔹 Reshaping and Flattening

arr = np.array([[1, 2], [3, 4]])
print(arr.reshape(4, 1))
print(arr.flatten())

Real-Life Example:

Convert a 2D image matrix into a 1D array for machine learning input.

🔹 Stacking and Splitting

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

print(np.vstack((a, b)))
print(np.hstack((a, b)))

🔹 Random Numbers

np.random.seed(0)
print(np.random.randint(1, 10, size=(2, 3)))

Real-Life Example:

Create random roll numbers or question orders for an online quiz.

🔹 File I/O in NumPy

np.savetxt('data.csv', arr, delimiter=',')
arr_loaded = np.loadtxt('data.csv', delimiter=',')

Real-Life Example:

Save and load data like marks, sales, or sensor values using CSV files.

📚 Interview Q&A with Real-Life Examples

1. What is NumPy? Why is it used?

Answer: A powerful library for numeric computations in Python. Used in data science, ML, and engineering.
Example: Analyze thousands of rows of Excel data in seconds.

2. Difference between Python list and NumPy array?

3. What is broadcasting?

Answer: It allows different-shaped arrays to work together in operations.
Example: Add 10% tax to each product price: arr + 10

4. How to create arrays?

Answer: Using np.array, np.zeros, np.ones, np.arange, etc.
Example: Initialize 0 attendance for all students.

5. What is reshape and flatten?

Answer: reshape() changes shape, flatten() converts to 1D.
Example: Convert a 2D image to 1D for model input.

6. Mathematical operations in NumPy?

Answer: Use +, -, *, / between arrays or scalars.
Example: Calculate final bill: price - discount

7. How to calculate statistics?

Answer: Use np.mean, np.median, np.std, etc.
Example: Find average marks of a class.

8. Generating random numbers?

Answer: Use np.random.randint, np.random.rand, etc.
Example: Generate random test scores or sample data.

9. What is axis in NumPy?

Answer: Tells NumPy to operate along rows or columns.
Example: Sum all subjects per student: axis=1

10. File handling in NumPy?

Answer: np.savetxt, np.loadtxt for CSV operations.
Example: Save survey results into a CSV file.

Sure! Here’s a step-by-step NumPy guide with more examples and outputs, explained in simple English, including real-life usage.

🧮 NumPy Step-by-Step with Examples and Outputs

✅ Step 1: Importing NumPy

import numpy as np

✅ Why? This lets us use all the NumPy functions.

✅ Step 2: Creating Arrays

➤ 1D Array

a = np.array([1, 2, 3])
print(a)
# Output: [1 2 3]

➤ 2D Array

b = np.array([[1, 2], [3, 4]])
print(b)
# Output:
# [[1 2]
#  [3 4]]

➤ 3D Array

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

🎯 Real-Life Use: Store pixel values for an image (3D – width, height, channels).

✅ Step 3: Array Properties

print(b.shape)     # (2, 2)
print(b.ndim)      # 2
print(b.size)      # 4
print(b.dtype)     # int64

🎯 Real-Life Use: Know the shape of Excel-like data before applying operations.

✅ Step 4: Indexing and Slicing

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

➤ 2D Indexing

arr2 = np.array([[1, 2], [3, 4]])
print(arr2[1, 0])
# Output: 3

🎯 Real-Life Use: Access student marks from rows and subjects from columns.

✅ Step 5: Array Operations

a = np.array([10, 20, 30])
b = np.array([1, 2, 3])
print(a + b)     # [11 22 33]
print(a * b)     # [10 40 90]

🎯 Real-Life Use: Calculate bill amount = price * quantity

✅ Step 6: Broadcasting

a = np.array([1, 2, 3])
print(a + 5)
# Output: [6 7 8]

🎯 Real-Life Use: Apply flat discount or tax to all items at once.

✅ Step 7: Useful NumPy Functions

arr = np.array([10, 20, 30])
print(np.sum(arr))     # 60
print(np.mean(arr))    # 20.0
print(np.min(arr))     # 10
print(np.max(arr))     # 30
print(np.std(arr))     # 8.16...

🎯 Real-Life Use: Calculate total, average, and spread of exam scores.

✅ Step 8: Reshape and Flatten

arr = np.array([[1, 2, 3], [4, 5, 6]])
reshaped = arr.reshape(3, 2)
print(reshaped)
# Output:
# [[1 2]
#  [3 4]
#  [5 6]]

flat = arr.flatten()
print(flat)
# Output: [1 2 3 4 5 6]

🎯 Real-Life Use: Prepare data for machine learning models (flat format).

✅ Step 9: Stack and Split Arrays

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

print(np.hstack((a, b)))
# Output:
# [[1 2 5 6]
#  [3 4 7 8]]

print(np.vstack((a, b)))
# Output:
# [[1 2]
#  [3 4]
#  [5 6]
#  [7 8]]

🎯 Real-Life Use: Combine tables or reports horizontally/vertically.

✅ Step 10: Random Numbers

np.random.seed(0)
print(np.random.randint(1, 10, (2, 3)))
# Output:
# [[5 6 1]
#  [4 4 8]]

🎯 Real-Life Use: Create random test data or shuffle questions.

✅ Step 11: Conditional Selection

arr = np.array([10, 20, 30, 40])
print(arr[arr > 20])
# Output: [30 40]

🎯 Real-Life Use: Filter students who scored above 20 marks.

✅ Step 12: Save & Load Files

arr = np.array([[1, 2], [3, 4]])
np.savetxt("data.csv", arr, delimiter=",")
loaded = np.loadtxt("data.csv", delimiter=",")
print(loaded)

🎯 Real-Life Use: Save and reload reports, marks, or sales data.

✅ Step 13: Axis in Functions

arr = np.array([[1, 2], [3, 4]])
print(np.sum(arr, axis=0))  # Column sum: [4 6]
print(np.sum(arr, axis=1))  # Row sum: [3 7]

🎯 Real-Life Use: Get total sales per product or per day.

✅ Step 14: Special Arrays

print(np.zeros((2, 3)))   # Array with all zeros
print(np.ones((2, 3)))    # Array with all ones
print(np.eye(3))          # Identity matrix

🎯 Real-Life Use: Initialize tables, filters, or neural network layers.

✅ Step 15: Linspace & Arange

print(np.linspace(1, 5, 5))  # [1. 2. 3. 4. 5.]
print(np.arange(1, 10, 2))   # [1 3 5 7 9]

🎯 Real-Life Use: Generate time steps or price intervals for charts.

🔍 Final Thoughts

NumPy is essential for data analysis, machine learning, and scientific work in Python. Mastering it gives you speed, accuracy, and power to work with big data.

If you’re a beginner, practice real-life examples like:

• Student marks
• Sales data
• Weather sensor logs

Keep experimenting with arrays, slicing, operations, and reshaping to become a NumPy pro!