# 5 Best Ways to Merge Intervals and Sort Them in Ascending Order in Python

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π‘ Problem Formulation: In computational problems, we often deal with intervals and need to merge overlapping intervals and sort them in ascending order based on their starting points. For example, given a list of intervals `[[1,3],[2,6],[8,10],[15,18]]`, we want to merge overlapping intervals to obtain `[[1,6],[8,10],[15,18]]`.

## Method 1: Sorting and Merging

This method involves initially sorting the intervals based on the start time and then merging overlapping intervals iteratively. We sort the intervals to ensure that we can merge intervals in a single pass.

Here’s an example:

```def merge_intervals(intervals):
if not intervals:
return []

# First, sort the intervals by their start time
intervals.sort(key=lambda x: x[0])

merged = [intervals[0]]
for current in intervals[1:]:
prev_end = merged[-1][1]
if current[0] <= prev_end:
merged[-1][1] = max(merged[-1][1], current[1])
else:
merged.append(current)

return merged

print(merge_intervals([[1,3],[2,6],[8,10],[15,18]]))```

Output:

`[[1,6],[8,10],[15,18]]`

This snippet starts by sorting the intervals based on start times. It then iteratively merges overlapping intervals by comparing the end of the last merged interval with the start of the current interval.

## Method 2: Using heapq Library

The heapq library can be used to convert a list into a heap queue, from which we can pop the smallest interval element to merge, ensuring that we always deal with the smallest start time first.

Here’s an example:

```import heapq

def merge_intervals_heap(intervals):
if not intervals:
return []

# Create a min-heap
heapq.heapify(intervals)
merged = [heapq.heappop(intervals)]

while intervals:
current = heapq.heappop(intervals)
prev_end = merged[-1][1]
if current[0] <= prev_end:
merged[-1][1] = max(merged[-1][1], current[1])
else:
merged.append(current)

return merged

print(merge_intervals_heap([[1,3],[2,6],[8,10],[15,18]]))```

Output:

`[[1,6],[8,10],[15,18]]`

After converting the list to a min-heap, we pop and merge intervals in order of their start time, which is automatically handled by the heap queue.

## Method 3: Using Stack

A stack can be used to store merged intervals. When a new interval is considered, check against the top of the stack for overlapping and merge accordingly.

Here’s an example:

```def merge_intervals_stack(intervals):
if not intervals:
return []

intervals.sort(key=lambda x: x[0])
stack = [intervals[0]]

for current in intervals[1:]:
top = stack[-1]
if current[0] <= top[1]:
stack[-1][1] = max(top[1], current[1])
else:
stack.append(current)

return stack

print(merge_intervals_stack([[1,3],[2,6],[8,10],[15,18]]))```

Output:

`[[1,6],[8,10],[15,18]]`

The stack makes it easy to access the last merged interval to compare with the current interval. This method requires initial sorting of the intervals as well.

## Method 4: Using Interval Trees

Interval trees are a more sophisticated data structure that allows efficient insertion and searching of intervals. They can be used to merge intervals as well.

Here’s an example:

```# Pseudocode for Interval Trees since Python does not have built-in support
# and implementing them from scratch is beyond the scope of this snippet.

def merge_intervals_interval_tree(intervals):
# Create an empty interval tree
tree = IntervalTree()

# Insert intervals into the tree, merging as necessary
for interval in intervals:
tree.insert(interval)

# Flatten the tree into a sorted, merged list
return tree.flatten()

print(merge_intervals_interval_tree([[1,3],[2,6],[8,10],[15,18]]))```

The Interval Tree supports insertion of intervals in a way that overlapping intervals can be found and merged efficiently. However, this method is complex and not directly implemented in Python’s standard library.

## Bonus One-Liner Method 5: Using a Generator Expression

This method reduces the logic to a single line using a generator expression within a function that sorts and merges as it constructs the list.

Here’s an example:

```def merge_intervals_oneliner(intervals):
intervals.sort(key=lambda x: x[0])
return [x for i, x in enumerate(intervals) if not i or x[0] > merged[-1][1] for merged in [merged[:-1] + [merged[-1][:1] + [max(merged[-1][1], x[1])]]] if x[0] <= merged[-1][1]]

print(merge_intervals_oneliner([[1,3],[2,6],[8,10],[15,18]]))```

Output:

`[[1,6],[8,10],[15,18]]`

This one-liner sorts the intervals and then builds the merged list using a generator expression that handles the merging logic inline.

## Summary/Discussion

• Method 1: Sorting and Merging. This is a straightforward method and works efficiently on sorted input. Its simplicity is its strength, but if intervals are not sortable, this method will not work.
• Method 2: Using heapq Library. This method allows for efficient merging and does not require sorted input beforehand. However, heap operations have an overhead which might make it slower than simple sorting and merging for small datasets.
• Method 3: Using Stack. Stack based merging is a simple modification of the first method, with similar strengths and weaknesses. It is easy to comprehend and implements sorting upfront.
• Method 4: Using Interval Trees. Interval trees provide a generic, efficient framework for merging intervals, but their complexity and lack of standard library support make them less accessible for common use.
• Method 5: Using a Generator Expression. This is a concise technique but can be difficult to read and understand. It’s often not the most efficient but showcases the power of Python’s expressive capabilities.