# 5 Best Ways to Check if an Array Contains Contiguous Integers with Duplicates Allowed in Python

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π‘ Problem Formulation: In Python, you may encounter a situation where you need to determine if a list or array contains a sequence of contiguous integers, even if some values are repeated. This challenge can arise in various data analysis tasks, where the continuity of data points is essential. For instance, you have an input like `[3, 4, 5, 5, 6]` and you want to confirm that these integers form a contiguous sequence, disregarding the duplicates. The desired output for this input would be `True`.

## Method 1: Using Set and Range Comparison

This method involves converting the array to a set to remove duplicates, then comparing it with a set constructed from the range of its minimum to maximum values. It’s straightforward and efficient for datasets where removing duplicates does not lead to a loss of essential information.

Here’s an example:

```def is_contiguous(arr):
unique_elements = set(arr)
return unique_elements == set(range(min(arr), max(arr) + 1))

# Test the function
print(is_contiguous([3, 4, 5, 5, 6]))```

Output:

`True`

This snippet defines the function `is_contiguous` that takes an array as its parameter, removes duplicates by converting it to a set, and checks if this set equals the set of all numbers in the contiguous range defined by the smallest and largest numbers in the array. If equal, it returns `True`, indicating the array is contiguous.

## Method 2: Sorting and Iterating

Another approach is to sort the array and iterate through it to check for continuity. This method handles duplicates naturally and maintains the original array’s integrity.

Here’s an example:

```def is_contiguous(arr):
if not arr: return False
sort_arr = sorted(arr)
for i in range(1, len(sort_arr)):
if sort_arr[i] - sort_arr[i - 1] > 1:
return False
return True

# Test the function
print(is_contiguous([3, 4, 5, 5, 6]))```

Output:

`True`

In this code, the function `is_contiguous` sorts the input array and then checks whether each subsequent element is within one value of the previous. If any gap is larger than one, it returns `False`; otherwise, it confirms the sequence is contiguous with `True`.

## Method 3: Using Counter for Frequency Analysis

This method uses the Counter class from the collections module to analyze the frequency of each element in the array, allowing for a quick check of contiguous integers irrespective of duplicates.

Here’s an example:

```from collections import Counter

def is_contiguous(arr):
element_counter = Counter(arr)
for i in range(min(arr), max(arr)):
if i not in element_counter:
return False
return True

# Test the function
print(is_contiguous([3, 4, 5, 5, 6]))```

Output:

`True`

This example demonstrates using the Counter class to create a frequency map of the array. The `is_contiguous` function then checks for the presence of every integer in the range between the smallest and largest elements. If an integer is missing, it returns `False`; otherwise, it confirms the sequence is contiguous.

## Method 4: Leveraging Arithmetic Progression Properties

By checking if the difference between the maximum and minimum values equals the size of the set minus one, you can infer that the array forms an arithmetic progression and thus is contiguous.

Here’s an example:

```def is_contiguous(arr):
if len(arr) < 2: return False

unique_elements = set(arr)
return max(arr) - min(arr) == len(unique_elements) - 1

# Test the function
print(is_contiguous([3, 4, 5, 5, 6]))```

Output:

`True`

The function `is_contiguous` applies a mathematical property of arithmetic progressions: a sequence with no gaps will have a length that is one more than the difference between its maximum and minimum values. After the array is converted to a set to handle duplicates, the code checks this property to determine if the sequence is contiguous.

## Bonus One-Liner Method 5: Using Python’s All with List Comprehension

Using a combination of `all` built-in function and list comprehension, you can check for continuity in a single line of code.

Here’s an example:

```is_contiguous = lambda arr: all(i in arr for i in range(min(arr), max(arr) + 1))

# Test the function
print(is_contiguous([3, 4, 5, 5, 6]))```

Output:

`True`

This one-liner uses a lambda function alongside `all` and a generator expression to check if all elements in the contiguous range from the minimum to maximum values are present in the original array. It’s a concise method but may be less efficient with large arrays due to the full scan of the original array for each element in the range.

## Summary/Discussion

• Method 1: Set and Range Comparison. This method is simple and efficient. However, it might not be suitable for problems where the indices of duplicates are significant.
• Method 2: Sorting and Iterating. This method keeps index information intact and is efficient. However, its efficiency decreases with larger arrays due to the sorting step.
• Method 3: Counter for Frequency Analysis. Efficient for arrays with a lot of duplicates. It might not be efficient if there’s a large range of numbers with few elements.
• Method 4: Arithmetic Progression Properties. Fast and requires less computation. It assumes that the input array has at least two unique values, not ideal for single-element arrays.
• Method 5: Python’s All with List Comprehension. Highly concise and expressive but may not perform well on large arrays. It provides elegant syntax but lacks computational optimization.