5 Best Ways to Check If a Pattern of Length m Is Repeated k or More Times in Python

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πŸ’‘ Problem Formulation: The challenge is to determine whether a string contains a specific pattern of a given length m that is repeated at least k times consecutively. For example, if the input string is “abcabcabc” and we want to find whether the pattern of length m=3 (which is “abc”) is repeated k=3 or more times, our function should return True as the output because “abc” is repeated three times.

Method 1: Using Loop and Slicing

This method employs a basic for-loop in conjunction with string slicing. We iterate through the string, slice segments of length m, and count consecutive occurrences of the pattern. If the count reaches k, we return True.

Here’s an example:

def has_pattern(s, m, k):
    count = 0
    for i in range(len(s) - m + 1):
        if s[i:i + m] == s[i - m:i]:
            count += 1
        else:
            count = 1
        if count >= k:
            return True
    return False

print(has_pattern("abcabcabc", 3, 3)) 

The output of this code would be:

True

This snippet defines the function has_pattern that takes a string s, and two integers m and k to check for the pattern. It uses a sliding window approach, where a window of size m moves one character at a time, and if the current window matches the previous window, it increases the count. When the count is equal to or exceeds k, it returns True.

Method 2: Using Regular Expressions

This method employs Python’s re module, which is used for string searching and manipulation. Specifically, we construct a regular expression pattern that will match any character sequence followed by itself k-1 times.

Here’s an example:

import re

def has_pattern_regex(s, m, k):
    pattern = r"(.{" + str(m) + r"})\1{" + str(k - 1) + r",}"
    return re.search(pattern, s) is not None

print(has_pattern_regex("abcabcabc", 3, 3)) 

The output of this code would be:

True

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_regex dynamically constructs a regular expression pattern that checks for any sequence of m characters that repeats k or more times. The \1 refers back to the captured group, ensuring that the same pattern is matched in subsequent searches. We use re.search() to find the pattern within the string.

Method 3: Using itertools.groupby

The itertools.groupby function can be used to group consecutive items together. In this approach, we generate all subsequences of length m and use groupby to count consecutive occurrences of the same subsequence.

Here’s an example:

from itertools import groupby

def has_pattern_groupby(s, m, k):
    groups = [''.join(group) for _, group in groupby(s[i:i+m] for i in range(len(s)-m+1))]
    return any(groups.count(sub) >= k for sub in set(groups))

print(has_pattern_groupby("abcabcabc", 3, 3)) 

The output of this code would be:

True

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_regex dynamically constructs a regular expression pattern that checks for any sequence of m characters that repeats k or more times. The \1 refers back to the captured group, ensuring that the same pattern is matched in subsequent searches. We use re.search() to find the pattern within the string.

Method 3: Using itertools.groupby

The itertools.groupby function can be used to group consecutive items together. In this approach, we generate all subsequences of length m and use groupby to count consecutive occurrences of the same subsequence.

Here’s an example:

from itertools import groupby

def has_pattern_groupby(s, m, k):
    groups = [''.join(group) for _, group in groupby(s[i:i+m] for i in range(len(s)-m+1))]
    return any(groups.count(sub) >= k for sub in set(groups))

print(has_pattern_groupby("abcabcabc", 3, 3)) 

The output of this code would be:

True

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_regex dynamically constructs a regular expression pattern that checks for any sequence of m characters that repeats k or more times. The \1 refers back to the captured group, ensuring that the same pattern is matched in subsequent searches. We use re.search() to find the pattern within the string.

Method 3: Using itertools.groupby

The itertools.groupby function can be used to group consecutive items together. In this approach, we generate all subsequences of length m and use groupby to count consecutive occurrences of the same subsequence.

Here’s an example:

from itertools import groupby

def has_pattern_groupby(s, m, k):
    groups = [''.join(group) for _, group in groupby(s[i:i+m] for i in range(len(s)-m+1))]
    return any(groups.count(sub) >= k for sub in set(groups))

print(has_pattern_groupby("abcabcabc", 3, 3)) 

The output of this code would be:

True

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_regex dynamically constructs a regular expression pattern that checks for any sequence of m characters that repeats k or more times. The \1 refers back to the captured group, ensuring that the same pattern is matched in subsequent searches. We use re.search() to find the pattern within the string.

Method 3: Using itertools.groupby

The itertools.groupby function can be used to group consecutive items together. In this approach, we generate all subsequences of length m and use groupby to count consecutive occurrences of the same subsequence.

Here’s an example:

from itertools import groupby

def has_pattern_groupby(s, m, k):
    groups = [''.join(group) for _, group in groupby(s[i:i+m] for i in range(len(s)-m+1))]
    return any(groups.count(sub) >= k for sub in set(groups))

print(has_pattern_groupby("abcabcabc", 3, 3)) 

The output of this code would be:

True

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_regex dynamically constructs a regular expression pattern that checks for any sequence of m characters that repeats k or more times. The \1 refers back to the captured group, ensuring that the same pattern is matched in subsequent searches. We use re.search() to find the pattern within the string.

Method 3: Using itertools.groupby

The itertools.groupby function can be used to group consecutive items together. In this approach, we generate all subsequences of length m and use groupby to count consecutive occurrences of the same subsequence.

Here’s an example:

from itertools import groupby

def has_pattern_groupby(s, m, k):
    groups = [''.join(group) for _, group in groupby(s[i:i+m] for i in range(len(s)-m+1))]
    return any(groups.count(sub) >= k for sub in set(groups))

print(has_pattern_groupby("abcabcabc", 3, 3)) 

The output of this code would be:

True

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_regex dynamically constructs a regular expression pattern that checks for any sequence of m characters that repeats k or more times. The \1 refers back to the captured group, ensuring that the same pattern is matched in subsequent searches. We use re.search() to find the pattern within the string.

Method 3: Using itertools.groupby

The itertools.groupby function can be used to group consecutive items together. In this approach, we generate all subsequences of length m and use groupby to count consecutive occurrences of the same subsequence.

Here’s an example:

from itertools import groupby

def has_pattern_groupby(s, m, k):
    groups = [''.join(group) for _, group in groupby(s[i:i+m] for i in range(len(s)-m+1))]
    return any(groups.count(sub) >= k for sub in set(groups))

print(has_pattern_groupby("abcabcabc", 3, 3)) 

The output of this code would be:

True

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.

The function has_pattern_regex dynamically constructs a regular expression pattern that checks for any sequence of m characters that repeats k or more times. The \1 refers back to the captured group, ensuring that the same pattern is matched in subsequent searches. We use re.search() to find the pattern within the string.

Method 3: Using itertools.groupby

The itertools.groupby function can be used to group consecutive items together. In this approach, we generate all subsequences of length m and use groupby to count consecutive occurrences of the same subsequence.

Here’s an example:

from itertools import groupby

def has_pattern_groupby(s, m, k):
    groups = [''.join(group) for _, group in groupby(s[i:i+m] for i in range(len(s)-m+1))]
    return any(groups.count(sub) >= k for sub in set(groups))

print(has_pattern_groupby("abcabcabc", 3, 3)) 

The output of this code would be:

True

The function has_pattern_groupby constructs all possible substrings of length m and then groups identical substrings together. It checks if any of the substrings in the list appear k or more times using the count function.

Method 4: Using str.count

This solution centers around the use of Python’s built-in str.count() method, which returns the number of non-overlapping occurrences of a substring. We simply count the occurrences of the target pattern in the string.

Here’s an example:

def has_pattern_count(s, m, k):
    for i in range(len(s) - m + 1):
        if s.count(s[i:i + m]) >= k:
            return True
    return False

print(has_pattern_count("abcabcabc", 3, 3)) 

The output of this code would be:

True

In has_pattern_count, we sequentially check each subsequence of length m and use str.count() to verify if the subsequence appears at least k times within the string. If so, return True; otherwise, after the loop completes, return False.

Bonus One-Liner Method 5: Using all and list comprehension

A compact one-liner that leverages list comprehension and the all() function. This uses the idea that if a sequence of length m is repeated k or more times, then this must hold true for all characters in the pattern.

Here’s an example:

has_pattern_oneliner = lambda s, m, k: any(all(s[i:i + m] == s[j:j + m] for j in range(i, i + m * k, m)) for i in range(len(s) - m * k + 1))

print(has_pattern_oneliner("abcabcabc", 3, 3)) 

The output of this code would be:

True

This Python one-liner defines an anonymous function (lambda) that iterates over slices of the string and checks whether the slice repeats consecutively for k times. The all() function ensures all the checks must return True for the containing any() function to return True as a result.

Summary/Discussion

Each method for checking if a pattern repeats in a string has its own strengths and weaknesses:

  • Method 1: Using Loop and Slicing. Strengths: Easy to understand. Weaknesses: May not be as efficient as other methods for large strings due to potential for high time complexity.
  • Method 2: Using Regular Expressions. Strengths: Very powerful and concise. Weaknesses: Can be difficult to understand and possibly overkill for simple patterns.
  • Method 3: Using itertools.groupby. Strengths: Efficient for large strings. Weaknesses: Slightly complex and not as intuitive.
  • Method 4: Using str.count. Strengths: Very straightforward. Weaknesses: Can result in higher time complexity for long strings with many repeats.
  • Bonus One-Liner Method 5: Using all and list comprehension. Strengths: Extremely concise. Weaknesses: May be hard to read and understand at a glance.