Numpy basic functions II – Python

Basic Operations in NumPy (NumPy Basic Functions II)

While Python provides primitive operations (+, -, *, /, etc.), we use NumPy operations because they offer better speed and efficiency.

Since NumPy is designed for handling large datasets, Python’s built-in operations can be slow when dealing with lists because they require iterations for element-wise computations.

However, NumPy supports vectorized operations, which allow array calculations to be performed all at once, without loops. This makes NumPy operations significantly faster and more memory-efficient.

Advantages of NumPy Operations

• Fast – Built on C, making it highly optimized for speed.
• Supports Vector Operations – Performs array-wide operations without the need for iteration.
• Memory Efficient – Uses significantly less memory compared to Python lists.
• Rich Mathematical & Statistical Functions – Easily compute averages, standard deviations, sums, and more.

In short, if you’re working with data analysis or machine learning, NumPy is an essential tool for handling large-scale computations efficiently.

Numpy basic functions II Code

import numpy as np

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

result = np.add(a, b)
print(result) # [5 7 9]

result = np.subtract(a, b)
print(result) # [-3 -3 -3]

result = np.multiply(a, b)
print(result) # [ 4 10 18]

result = np.divide(a, b)
print(result) # [0.25 0.4  0.5 ]

m = np.mean(a)
print(m) # 2.0

med = np.median(a)
print(med) # 2.0

s = np.std(a)
print(s) # 0.816496580927726

s = np.sum(a)
print(s) # 6

min_val = np.min(a)
print(min_val) # 1

max_val = np.max(a)
print(max_val) # 3

Output

[5 7 9]
[-3 -3 -3]
[ 4 10 18]
[0.25 0.4  0.5 ]
2.0
2.0
0.816496580927726
6
1
3

NumPy and Its Connection to Discrete Mathematics

NumPy is a powerful library for handling numbers and arrays, while discrete mathematics focuses on integers, combinations, logical operations, graphs, and more. The two are closely related and widely used in fields like network analysis, recommender systems, and route optimization.

1. Sequence and Matrix Operations

• Discrete math often involves sequences and matrices, and NumPy efficiently handles vector and matrix operations.
• Examples include Fibonacci sequences, binomial coefficients, and matrix computations like multiplication and transposition.

2. Combinations and Permutations

• Counting possible cases using combinations and permutations is crucial in discrete math.
• NumPy provides optimized computations for these, making it useful for data sampling, probability calculations, and more.

3. Logical Operations and Sets

• Discrete math covers logic gates, Boolean evaluations, and set operations (intersection, union, difference).
• NumPy offers efficient array-based logical and set operations, applied in machine learning, data filtering, and image processing.

4. Graph Theory and Adjacency Matrices

• Problems like networks, social graphs, and shortest paths are fundamental in discrete math.
• NumPy enables easy graph representation using adjacency matrices, simplifying complex graph computations.

NumPy’s capabilities make it an essential tool for applying discrete mathematics in computational fields.