5 Best Ways to Remove Rows with Duplicate Elements in a Python Matrix

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💡 Problem Formulation: In Python programming, it’s not uncommon to encounter a scenario where you need to cleanse a matrix (a list of lists) by removing rows that contain duplicate elements. This task might be necessary in data pre-processing to ensure the uniqueness of data points. For example, given a matrix [[1, 2], [3, 3], [4, 5], [1, 2]], we want the output to be [[1, 2], [4, 5]] as the other rows contain duplicate elements or are duplicates themselves.

Method 1: Using a for Loop with set()

This method involves iterating over each row in the matrix and adding it to a new list if its set representation (which removes duplicates) has the same length as the row. This effectively filters out rows with duplicate elements.

Here’s an example:

def remove_duplicate_rows(matrix):
    unique_rows = []
    for row in matrix:
        if len(row) == len(set(row)):
            unique_rows.append(row)
    return unique_rows

matrix = [[1, 2], [3, 3], [4, 5], [1, 2]]
print(remove_duplicate_rows(matrix))

Output: [[1, 2], [4, 5]]

This snippet defines a function remove_duplicate_rows that checks each row with the set() function to ensure all elements are unique before appending it to the result list, yielding the desired result.

Method 2: Using List Comprehension

List comprehension is a concise way to achieve the same result. The condition inside list comprehension ensures only unique rows are included in the final list.

Here’s an example:

matrix = [[1, 2], [3, 3], [4, 5], [1, 2]]
result = [row for row in matrix if len(row) == len(set(row))]
print(result)

Output: [[1, 2], [4, 5]]

This code uses list comprehension to create a filtered list that only includes rows where the number of unique elements (using set(row)) matches the length of the row, indicating no duplicates.

Method 3: Using itertools.groupby()

The itertools.groupby() method can be used to group rows and then filter out groups that contain duplicates by converting each row to a tuple and sorting the matrix.

Here’s an example:

from itertools import groupby

def remove_duplicate_rows(matrix):
    matrix.sort()  # Sort for grouping
    return [list(group) for key, group in groupby(matrix) if len(set(key)) == len(key)]

matrix = [[1, 2], [3, 3], [4, 5], [1, 2]]
print(remove_duplicate_rows(matrix))

Output: [[1, 2], [4, 5]]

This function first sorts the matrix to ensure that groupby() can group identical rows. Then it filters these groups based on the uniqueness criteria using a set to provide the desired result.

Method 4: Using numpy library

For those using the numpy library, it offers a more specialized and efficient way to address matrix operations. Here numpy is used to filter rows based on the presence of duplicates.

Here’s an example:

import numpy as np

def remove_duplicate_rows(matrix):
    return matrix[np.all(np.diff(np.sort(matrix, axis=1), axis=1) != 0, axis=1)]

matrix = np.array([[1, 2], [3, 3], [4, 5], [1, 2]])
print(remove_duplicate_rows(matrix))

Output: [[1 2] [4 5]]

Within this snippet, the remove_duplicate_rows function sorts each row and compares adjacent elements with np.diff, excluding those with any zero differences, which would indicate duplicates.

Bonus One-Liner Method 5: Using set() with map()

A one-liner bonus method using a combination of map(), set(), and filtering to deliver an elegant solution.

Here’s an example:

matrix = [[1, 2], [3, 3], [4, 5], [1, 2]]
unique_rows = filter(lambda row: len(row) == len(set(row)), matrix)
print(list(unique_rows))

Output: [[1, 2], [4, 5]]

This functional programming approach uses filter() along with a lambda function that checks for duplicate elements within rows. list() is then applied to convert the filter object to a list.

Summary/Discussion

  • Method 1: Using a for loop with set(). Strengths: straightforward and easy to read. Weaknesses: relatively slower with larger data sets due to explicit looping.
  • Method 2: Using List Comprehension. Strengths: concise and Pythonic. Weaknesses: readability may suffer for those new to Python.
  • Method 3: Using itertools.groupby(). Strengths: efficient with sorted data. Weaknesses: requires sorting, which adds complexity.
  • Method 4: Using numpy library. Strengths: highly efficient for large matrices. Weaknesses: requires numpy, not part of Python’s standard library.
  • Bonus One-Liner Method 5: Using set() with map(). Strengths: very concise. Weaknesses: may obfuscate what’s happening for some users.