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Thinking Like a Programmer•Interactive notebook lesson•Free

numpy complete tutorial

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NumPy Complete Tutorial (Beginner → Advanced)

This notebook provides a structured and correct tutorial covering NumPy from basics to advanced usage.

Sections include:

Installation & Setup
NumPy Arrays vs Python Lists
Array Creation
Array Attributes
Indexing & Slicing
Boolean Indexing
Fancy Indexing
Broadcasting
Vectorization
Aggregations
Linear Algebra
Random Numbers
Statistics
Reshaping
Stacking & Splitting
File IO
Structured Arrays
Performance Optimization
Memory Layout
Practical Examples

Codepython


import numpy as np
print("NumPy version:", np.__version__)

NumPy Arrays vs Python Lists

Explanation and example.

Code


import numpy as np

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

print("Python list:",python_list)
print("NumPy array:",numpy_array)

print("List * 2:",python_list*2)
print("Array * 2:",numpy_array*2)

Codepython


import numpy as np

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

print(a)
print(b)

result=a+b
print(result)

Output

[1 2 3]
[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]
[[2. 3. 4.]
 [2. 3. 4.]
 [2. 3. 4.]]

Codepython


import numpy as np

x=np.arange(5)

print(np.sin(x))
print(np.exp(x))
print(np.sqrt(x))

Output

[ 0.          0.84147098  0.90929743  0.14112001 -0.7568025 ]
[ 1.          2.71828183  7.3890561  20.08553692 54.59815003]
[0.         1.         1.41421356 1.73205081 2.        ]

Codepython


import numpy as np

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

print("Matrix multiplication")
print(A@B)

print("Determinant")
print(np.linalg.det(A))

print("Inverse")
print(np.linalg.inv(A))

Codepython


import numpy as np
import time

x=np.arange(1000000)

start=time.time()
y=[i*i for i in x]
print("Python loop:",time.time()-start)

start=time.time()
y=x*x
print("NumPy vectorized:",time.time()-start)

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numpy complete tutorial

NumPy Complete Tutorial (Beginner → Advanced)

NumPy Arrays vs Python Lists

Exercise

Array Creation

Exercise

Array Attributes

Exercise

Indexing and Slicing

Exercise

Boolean Indexing

Exercise

Fancy Indexing

Exercise

Reshaping Arrays

Exercise

Broadcasting

Exercise

Vectorized Operations

Exercise

Aggregations

Exercise

Linear Algebra

Exercise

Random Numbers

Exercise

Statistics

Exercise

Stacking Arrays

Exercise

Splitting Arrays

Exercise

Saving and Loading Files

Exercise

Structured Arrays

Exercise

Performance: Vectorization vs Loops

Exercise

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