✅ NumPy in Python – From Beginner to Hard Level (With Programs)

 

BEGINNER LEVEL NumPy Projects

1. Introduction to NumPy

NumPy (Numerical Python) is a Python library used for:

• Fast numerical computation

• Working with arrays (faster than Python lists)

• Scientific computing

• Machine learning & data analysis

Installation

pip install numpy

Import

import numpy as np

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⭐ 2. NumPy Array Basics

2.1 Creating Arrays

From Python list

import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr)

2D Array

arr2 = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2)

Check Data Type

print(arr.dtype)

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2.2 Array Built-in Functions

Zeros, Ones, Full

np.zeros(5)
np.ones((2,3))
np.full((3,3), 7)

Arange & Linspace

np.arange(1, 10, 2)   # step size
np.linspace(1, 5, 10) # 10 equal points

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⭐ 3. Array Indexing & Slicing

Indexing

arr = np.array([10, 20, 30, 40])
print(arr[2])  # 30

Slicing

print(arr[1:3])
print(arr[:3])
print(arr[::-1])   # reverse

2D Array Indexing

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

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⭐ 4. Array Math Operations

Element-wise operations

arr = np.array([1, 2, 3])

print(arr + 5)
print(arr * 2)
print(arr ** 2)

Array to array

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

print(a + b)
print(a * b)

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⭐ 5. Aggregate Functions

arr = np.array([2, 5, 8, 10])

arr.sum()
arr.max()
arr.min()
arr.mean()
arr.std()    # standard deviation
arr.var()    # variance

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⭐ 6. Reshaping Arrays

Reshape

arr = np.arange(12)
new_arr = arr.reshape((3,4))
print(new_arr)

Flatten

arr.flatten()

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⭐ 7. Stacking Arrays

Vertical Stack

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

np.vstack((a, b))

Horizontal Stack

np.hstack((a, b))

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⭐ 8. NumPy Broadcasting

Broadcasting allows operations on arrays of different shapes.

Example

arr = np.array([1,2,3])
print(arr + 10)

2D + 1D example

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

b = np.array([10, 20, 30])
print(a + b)

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⭐ 9. Boolean Indexing

Filter

arr = np.array([10, 21, 32, 43, 54])
print(arr[arr > 30])

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⭐ 10. NumPy Random Module

Random number

np.random.rand(3)        # 1D
np.random.rand(3, 3)     # 2D

Random integers

np.random.randint(1, 10, 5)

Random normal distribution

np.random.randn(3, 3)

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⭐ 11. Advanced NumPy

11.1 Vectorization (Speed Improvement)

Without NumPy (slow)

lst = [1, 2, 3, 4]
result = [x * 2 for x in lst]

With NumPy (fast)

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

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11.2 Matrix Operations

Matrix multiplication

a = np.array([[1, 2],
              [3, 4]])

b = np.array([[5, 6],
              [7, 8]])

print(a.dot(b))

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11.3 Statistical Operations

Correlation

arr = np.array([1,2,3,4,5])
print(np.corrcoef(arr))

Percentile

np.percentile([10,20,30,40], 50)

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11.4 NumPy with Files

Save array to file

np.save("data.npy", arr)

Load array

arr = np.load("data.npy")

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⭐ 12. Hard Level – Practical Programs

12.1 Matrix Inverse, Determinant

arr = np.array([[2, 1],
                [5, 3]])

print(np.linalg.det(arr))
print(np.linalg.inv(arr))

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12.2 Eigenvalues & Eigenvectors

A = np.array([[4, 2],
              [1, 3]])

values, vectors = np.linalg.eig(A)

print(values)
print(vectors)

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12.3 Solve Linear Equation

Solve:
2x + 3y = 8
3x + 4y = 11

A = np.array([[2,3],[3,4]])
B = np.array([8,11])

solution = np.linalg.solve(A, B)
print(solution)

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12.4 Convolution (Image processing concept)

signal = np.array([1,2,3])
kernel = np.array([0,1])

print(np.convolve(signal, kernel))

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12.5 NumPy + Matplotlib Plot

(asked earlier also)

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.title("Sine Wave")
plt.show()