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

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π‘ 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.