#2.2 🧮 Numpy Cook Book

This blog covers essential NumPy concepts with simple code examples. Each demo has syntax highlighting and a copy button — perfect for learning or sharing!

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1. Importing NumPy

import numpy as np

2. Creating Arrays

# From list
a = np.array([1, 2, 3])
print(a)

# From tuple
b = np.array((4, 5, 6))
print(b)

3. Array Dimensions

a = np.array([[1, 2], [3, 4]])
print(a.ndim)   # 2
print(a.shape)  # (2,2)
print(a.size)   # 4

4. Array Data Types

a = np.array([1, 2, 3], dtype=np.float64)
print(a.dtype)

5. Array Indexing

a = np.array([10, 20, 30, 40, 50])
print(a[1])   # 20
print(a[-1])  # 50

6. Slicing Arrays

print(a[1:4])  # [20 30 40]
print(a[:3])   # [10 20 30]
print(a[::2])  # [10 30 50]

7. Array Operations

b = np.array([4, 5, 6])
print(a + b)        # [14 25 36]
print(a * b)        # [40 100 180]
print(np.dot(a, b)) # Dot product

8. Universal Functions (ufuncs)

a = np.array([1, 4, 9])
print(np.sqrt(a))  # [1. 2. 3.]
print(np.exp(a))   # e^a

9. Reshaping Arrays

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

10. Stacking Arrays

a = np.array([1, 2])
b = np.array([3, 4])
c = np.vstack((a, b))
print(c)

11. Splitting Arrays

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

12. Random Numbers

a = np.random.rand(3, 2)
print(a)

13. Array Broadcasting

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

14. Saving and Loading Arrays

a = np.array([1, 2, 3])
np.save('my_array.npy', a)
b = np.load('my_array.npy')
print(b)