Detect and Remove repeated patterns in a list – Python

In Python, identifying and removing repeated patterns in a list can be a common task in data processing, signal analysis, or general algorithm development.

This article explores various techniques to detect and eliminate such repetitive sequences using Python. We’ll cover approaches from simple iteration to more advanced methods using libraries like NumPy, ensuring you can choose the best approach for your specific use case. You’ll learn how to use techniques to detect repeated patterns and how to efficiently remove them from your list.

Let’s start with a simple example:

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

 [1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5]
 

Method 1: Iterative Pattern Detection and Removal

This method involves iterating through the list and comparing sub-sequences to identify repeating patterns. Once a pattern is found, it is removed from the list.

 def remove_repeated_pattern_iterative(data):
     n = len(data)
     for pattern_length in range(1, n // 2 + 1):  # Check patterns up to half the list length
         pattern = data[:pattern_length]
         num_repeats = 0
         for i in range(0, n - pattern_length + 1, pattern_length):
             if data[i:i + pattern_length] == pattern:
                 num_repeats += 1
             else:
                 break

         if num_repeats > 1:
             # Remove the repeated pattern
             new_data = data[num_repeats * pattern_length:]
             return new_data
     return data  # No repeating pattern found

 data = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5]
 result = remove_repeated_pattern_iterative(data)
 print(result)
 

 [4, 5]
 

Explanation: The code iterates through possible pattern lengths, checking for repetition at each length. If a pattern repeats more than once consecutively, the code assumes the repetition continues until a mismatch is found, and then removes the repeating portion. The n // 2 + 1 ensures we only check for patterns up to half the length of the list since a pattern longer than half the list cannot repeat. The num_repeats variable tracks how many times the pattern is repeated consecutively. If num_repeats is greater than 1, it means a repeating pattern is found, and the list is sliced to remove the repetitions.

Method 2: Using NumPy for Pattern Detection

NumPy’s array operations can be used to efficiently compare sequences and detect patterns. This method involves converting the list to a NumPy array and using array slicing for comparison.

 import numpy as np

 def remove_repeated_pattern_numpy(data):
     arr = np.array(data)
     n = len(arr)
     for pattern_length in range(1, n // 2 + 1):
         pattern = arr[:pattern_length]
         num_repeats = 0
         for i in range(0, n - pattern_length + 1, pattern_length):
             if np.array_equal(arr[i:i + pattern_length], pattern):
                 num_repeats += 1
             else:
                 break

         if num_repeats > 1:
             new_arr = arr[num_repeats * pattern_length:]
             return new_arr.tolist()
     return data

 data = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5]
 result = remove_repeated_pattern_numpy(data)
 print(result)
 

 [4, 5]
 

Explanation: This code converts the input list to a NumPy array. It then iterates through potential pattern lengths similar to Method 1. np.array_equal is used for efficient array comparison. If a repeating pattern is detected, NumPy array slicing is used to remove the pattern, and the resulting array is converted back to a list.

Method 3: Regular Expressions for Pattern Identification (String Conversion)

If the list elements can be converted to a string, regular expressions can be used to identify and remove repeating patterns. This method is particularly useful when dealing with more complex or variable patterns.

 import re

 def remove_repeated_pattern_regex(data):
     s = ''.join(map(str, data)) # Convert list to string
     pattern = r'(.+?)\1+'  # Regex to find repeating patterns
     match = re.search(pattern, s)
     if match:
         repeated_pattern = match.group(1)
         new_s = re.sub(rf"({re.escape(repeated_pattern)})+", "", s) # Remove all repetitions of pattern
         return [int(x) for x in new_s] # Convert string back to list of integers if applicable.
     else:
         return data

 data = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5]
 result = remove_repeated_pattern_regex(data)
 print(result)
 

 [4, 5]
 

Explanation: This method first converts the list to a string. It uses a regular expression (.+?)\1+ to find the shortest repeating pattern. re.escape ensures that any special characters in the identified pattern are treated literally. The identified pattern and its repetitions are removed using re.sub. Finally, the resulting string is converted back into a list of integers (if applicable). The ? in the regex makes the match non-greedy, finding the *shortest* repeating pattern.

Method 4: Using a Dictionary to Track Pattern Frequencies

This approach utilizes a dictionary to keep track of the frequency of different patterns within the list. It can be useful for identifying the most frequent pattern and removing it or its repetitions.

 def remove_most_frequent_pattern(data):
     pattern_counts = {}
     n = len(data)

     for pattern_length in range(1, n // 2 + 1):
         for i in range(n - pattern_length + 1):
             pattern = tuple(data[i:i + pattern_length])  # Convert to tuple for dictionary key
             if pattern in pattern_counts:
                 pattern_counts[pattern] += 1
             else:
                 pattern_counts[pattern] = 1

     if pattern_counts:
         most_frequent_pattern = max(pattern_counts, key=pattern_counts.get)
         most_frequent_pattern_length = len(most_frequent_pattern)
         new_data = []
         i = 0
         while i < n:
             if tuple(data[i:i+most_frequent_pattern_length]) == most_frequent_pattern:
                 i += most_frequent_pattern_length # Skip the pattern
             else:
                 new_data.append(data[i])
                 i += 1
         return new_data
     else:
         return data

 data = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5]
 result = remove_most_frequent_pattern(data)
 print(result)
 

 [4, 5]
 

Explanation: This method counts the occurrences of each possible pattern (up to half the length of the list) using a dictionary. The tuple() conversion is crucial because lists are not hashable and cannot be dictionary keys. After counting, it identifies the most frequent pattern. Finally, it iterates through the original list and constructs a new list, skipping instances of the most frequent pattern.

Method 5: Using List Comprehension for Concise Removal

List comprehension offers a concise way to remove specific patterns from the list based on certain conditions. This method is best used when the pattern to remove is known beforehand.

 def remove_specific_pattern(data, pattern_to_remove):
     pattern_length = len(pattern_to_remove)
     new_data = [data[i] for i in range(len(data)) if data[i:i + pattern_length] != pattern_to_remove]
     return new_data

 data = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5]
 pattern_to_remove = [1, 2, 3]
 result = remove_specific_pattern(data, pattern_to_remove)
 print(result)
 

 [4, 5]
 

Explanation: This function removes all occurrences of a *specific* pattern provided as input. List comprehension efficiently builds a new list, including only elements that are not part of the specified pattern sequence. Because the original list may contain overlapping sequences, this approach fails to remove the whole pattern.