Leetcode – Pascal’s Triangle in Python

Spread the love


Pascal’s Triangle is one of the classic problems in mathematics and computer science, and it appears in various applications ranging from algebra to combinatorics. The Pascal’s Triangle problem on LeetCode requires generating the first n rows of Pascal’s Triangle. This article will guide you through understanding the problem, the mathematics behind Pascal’s Triangle, and different approaches to solving this problem in Python.

Understanding the Problem

Pascal’s Triangle is a triangular array of numbers in which each number is the sum of the two numbers directly above it. The triangle looks like this:

   1 1
  1 2 1
 1 3 3 1
1 4 6 4 1

The task in the LeetCode problem is to generate the first n rows of this triangle.

Mathematical Background

Pascal’s Triangle has a deep mathematical background. Each row represents the coefficients in the binomial expansion of (a + b)^n. Furthermore, the value at the k-th position of the n-th row (0-indexed) can be calculated directly using the binomial coefficient formula:

Value = n! / (k! * (n-k)!)

Approach 1: Dynamic Approach

We can solve this problem iteratively by understanding the structure of Pascal’s Triangle. Each number is the sum of the two numbers directly above it. The first and last numbers in each row are always 1.

Let’s look at the Python code for this approach:

def generate_pascals_triangle(num_rows):
    triangle = [[1]*i for i in range(1, num_rows + 1)]
    for i in range(2, num_rows):
        for j in range(1, i):
            triangle[i][j] = triangle[i-1][j-1] + triangle[i-1][j]
    return triangle

This approach has a time complexity of O(n^2) where n is the number of rows, as it must iterate through each element in the triangle.

Approach 2: Using Binomial Coefficient

As we saw earlier, each entry in Pascal’s Triangle can be calculated directly using the binomial coefficient. This approach can be computationally expensive for larger values of n but it’s interesting mathematically.

Here is the Python code using this approach:

def generate_pascals_triangle(num_rows):
    def factorial(n):
        return 1 if n == 0 else n * factorial(n-1)
    def binomial_coefficient(n, k):
        return factorial(n) / (factorial(k) * factorial(n - k))
    triangle = [[int(binomial_coefficient(n, k)) for k in range(n+1)] for n in range(num_rows)]
    return triangle

Approach 3: Optimized Binomial Coefficient

We can optimize the binomial coefficient approach by calculating it using iterative multiplications, which can be more efficient.

def generate_pascals_triangle(num_rows):
    triangle = []
    for i in range(num_rows):
        row = [1]
        value = 1
        for j in range(1, i + 1):
            value *= (i - j + 1)
            value //= j
    return triangle

Tips and Insights

  1. Understanding the mathematical properties of Pascal’s Triangle can sometimes provide insights into more efficient solutions.
  2. While the dynamic approach is intuitive and efficient, sometimes alternative approaches like calculating binomial coefficients can be handy for specific variations of the problem.
  3. Watch out for potential integer overflows in languages that are not as forgiving as Python. In such cases, using a different algorithm or applying optimizations is crucial.


The Pascal’s Triangle problem is an interesting blend of mathematics and programming. Understanding the structure of Pascal’s Triangle and knowing how to leverage it is key to solving this problem efficiently. In this article, we explored different methods for solving this problem, each with its own advantages. When working on problems like these, it’s essential to think critically about the underlying patterns and mathematics, as they often lead to the most efficient solutions.

Leave a Reply