Why is Numpy faster in Python?

NumPy is faster in Python because of the following reasons:

• Vectorization: NumPy is designed to perform vectorized operations, which means that it can perform operations on entire arrays without the need for explicit loops. This is possible because NumPy arrays are homogeneous, which means that all elements in an array have the same data type. Vectorized operations are much faster than explicit loops because they are implemented in C and executed at the machine level.

Example:

import numpy as np

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

# Vectorized addition
c = a + b

print(c)

Output:

[5 7 9]

• Memory efficiency: NumPy arrays are more memory-efficient than Python lists because they are stored in contiguous blocks of memory. This means that NumPy arrays can be accessed and manipulated more quickly than Python lists, which are scattered throughout memory.

Example:

import numpy as np

# Creating a Python list
a = [1, 2, 3, 4, 5]

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

# Printing the size of the Python list and NumPy array
print("Size of Python list:", a.__sizeof__())
print("Size of NumPy array:", b.itemsize * b.size)

Output:

Size of Python list: 64
Size of NumPy array: 20

• Broadcasting: NumPy allows for broadcasting, which means that operations can be performed on arrays of different shapes and sizes. This is possible because NumPy automatically aligns the dimensions of the arrays to perform the operation.

Example:

import numpy as np

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

# Creating a 1D array
b = np.array([1, 2, 3])

# Broadcasting the 1D array to the 2D array
c = a + b

print(c)

Output:

[[2 4 6]
 [5 7 9]]

• Optimized algorithms: NumPy uses optimized algorithms for mathematical operations, such as linear algebra, Fourier transforms, and random number generation. These algorithms are implemented in C and Fortran, which are faster than Python.

Example:

import numpy as np

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

# Calculating the determinant of the array
det = np.linalg.det(a)

print(det)

Output:

-2.0