5 Best Ways to Add One Polynomial to Another in Python

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πŸ’‘ Problem Formulation: Polynomials are algebraic expressions consisting of variables and coefficients. The task is to add two polynomials, each represented by a list of coefficients in Python. For example, p1 = [2, 1] representing 2x + 1 and p2 = [3, 0, 1] representing 3x^2 + 1 should be added to yield p3 = [3, 2, 2], representing 3x^2 + 2x + 2.

Method 1: Using Loops

This method involves iterating over the coefficients of each polynomial using traditional for-loops. It’s hands-on and allows for easy customization while being readable and straightforward. Suitable for beginners or when no external libraries are preferred.

Here’s an example:

def add_polynomials(p1, p2):
    # Ensure the first polynomial is the longer one
    if len(p1) < len(p2):
        p1, p2 = p2, p1
    result = p1.copy()
    # Add the second polynomial to the result
    for i in range(len(p2)):
        result[i] += p2[i]
    return result

# Example usage:
p1 = [2, 1]
p2 = [3, 0, 1]
print(add_polynomials(p1, p2))


[3, 2, 2]

This code snippet first ensures that the longer polynomial is used as the base for addition. It then iterates through the coefficients of the shorter polynomial and adds them to the corresponding coefficients of the longer polynomial. This way, it deals with polynomials of different degrees effectively.

Method 2: Using NumPy’s polyadd

The NumPy library provides a convenient function numpy.polyadd() to add polynomials represented by coefficient arrays. It automatically handles alignment of terms by degree, making it robust and simple for mathematical operations.

Here’s an example:

import numpy as np

p1 = np.array([2, 1])
p2 = np.array([3, 0, 1])
print(np.polyadd(p1, p2))


[3. 2. 2.]

This snippet uses the NumPy library to handle polynomial addition. The np.polyadd() function takes two NumPy array arguments and returns a new array representing the sum of the polynomials. The result includes floating-point numbers due to NumPy’s default data types.

Method 3: Using Operator Overloading

This method leverages Python’s magic methods to enable the direct addition of custom polynomial objects with the ‘+’ operator. Ideal for an object-oriented approach, it allows for more readable and intuitive code when working with polynomial operations.

Here’s an example:

class Polynomial:
    def __init__(self, coeffs):
        self.coeffs = coeffs

    def __add__(self, other):
        # Ensure self has the longer list of coeffs
        if len(self.coeffs) < len(other.coeffs):
            self.coeffs, other.coeffs = other.coeffs, self.coeffs
        result = self.coeffs.copy()
        for i in range(len(other.coeffs)):
            result[i] += other.coeffs[i]
        return Polynomial(result)
    def __repr__(self):
        return f'Polynomial({self.coeffs})'

# Example usage:
p1 = Polynomial([2, 1])
p2 = Polynomial([3, 0, 1])
print(p1 + p2)


Polynomial([3, 2, 2])

The code snippet defines a Polynomial class with an __add__ method that overloads the ‘+’ operator. This method is similar to Method 1 but allows users to add polynomial instances directly instead of working with coefficient lists. It thus combines both object-oriented programming and the customization of polynomial arithmetic.

Method 4: Using Recursion

Recursive addition involves defining a function that can add two polynomials by recursively adding their coefficients. This method showcases the elegance of recursive paradigms and works well for educational purposes or in cases where recursion is a preferred approach.

Here’s an example:

def add_polynomials_recursive(p1, p2, result=None, index=0):
    if result is None:
        result = [0] * max(len(p1), len(p2))
    if index < len(p1):
        result[index] += p1[index]
    if index < len(p2):
        result[index] += p2[index]
    if index == max(len(p1), len(p2)) - 1:
        return result
        return add_polynomials_recursive(p1, p2, result, index + 1)
# Example usage:
p1 = [2, 1]
p2 = [3, 0, 1]
print(add_polynomials_recursive(p1, p2))


[3, 2, 2]

The recursion is initiated with two lists and an optional result list. At each recursive call, coefficients of the same degree are added together, and the recursion continues until all coefficients are processed. This method can elegantly handle polynomials of different lengths without explicit looping constructs.

Bonus One-Liner Method 5: Using List Comprehension

This concise method uses list comprehension combined with the zip function to add polynomials. It’s Pythonic and leverages the expressiveness of Python’s list comprehension syntax to achieve the result in a single line.

Here’s an example:

p1 = [2, 1]
p2 = [3, 0, 1]

# Ensure that p1 is the longer list
if len(p1) < len(p2):
    p1, p2 = p2, p1
# Add using list comprehension
result = [sum(x) for x in zip(p1, p2)] + p1[len(p2):] 


[3, 2, 2]

This one-liner uses a list comprehension to zip the two lists together, summing elements pairwise. If the first polynomial is longer, its remaining coefficients are concatenated. This method is elegant and compact, but readability might be a concern for those unfamiliar with list comprehensions.


  • Method 1: Using Loops. Strengths: Intuitive and versatile. Weaknesses: Can be inefficient with long polynomials.
  • Method 2: Using NumPy’s polyadd. Strengths: Convenient and robust. Weaknesses: Requires an external library.
  • Method 3: Operator Overloading. Strengths: Intuitive use with custom objects. Weaknesses: More complex implementation.
  • Method 4: Using Recursion. Strengths: Illustrates recursion beautifully. Weaknesses: Less intuitive and possible stack overflow with large polynomials.
  • Method 5: Using List Comprehension. Strengths: Pythonic and concise. Weaknesses: May sacrifice readability for brevity.