5 Best Ways to Check Overlapping Intervals with Pandas in Python

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πŸ’‘ Problem Formulation: When working with interval data in Python using pandas, it is a common requirement to determine if two interval objects that share closed endpoints overlap. For example, given Interval(1, 3, ‘right’) and Interval(2, 4, ‘left’), a method is needed to ascertain whether these intervals overlap and by how much. The desired output in this case would be True, indicating overlap.

Method 1: Using Interval.overlaps()

This method involves utilizing the overlaps() function provided by the pandas Interval object. It takes another Interval object as an argument and returns True if the two intervals overlap, False otherwise. It is a straightforward approach specifically designed for comparing intervals.

Here’s an example:

from pandas import Interval

interval1 = Interval(1, 3, 'right')
interval2 = Interval(2, 4, 'left')

overlap = interval1.overlaps(interval2)

Output: True

This code snippet creates two Interval objects and uses the overlaps() method of one to check if it overlaps with the other. In this scenario, they do overlap.

Method 2: Manual Comparison

Manual comparison involves checking the start and end points of the intervals and determining overlap based on their values. This approach provides greater control over the comparison logic but requires more boilerplate code.

Here’s an example:

def intervals_overlap(interval1, interval2):
    return max(interval1.left, interval2.left) < min(interval1.right, interval2.right)
    
overlap = intervals_overlap(Interval(1, 3, 'right'), Interval(2, 4, 'left'))

Output: True

In this example, the function intervals_overlap() manually computes whether the intervals overlap by comparing start and end points. The intervals in the example do indeed overlap.

Method 3: IntervalIndex.contains()

The IntervalIndex.contains() method can be used to check if a point or interval is contained within any interval within an IntervalIndex object. This method provides a way to compare multiple intervals at once.

Here’s an example:

from pandas import Interval, IntervalIndex

interval1 = Interval(1, 3, 'right')
intervals = IntervalIndex([Interval(2, 4, 'left')])

overlap = intervals.contains(interval1.left)

Output: array([True])

This code snippet creates an Interval and an IntervalIndex. It checks whether the start of interval1 is contained within any interval in IntervalIndex, indicating an overlap.

Method 4: Using IntervalTree

Pandas’ IntervalTree data structure is designed to efficiently query intervals. You can use it to check if any intervals overlap with a given interval, especially useful for large sets of interval data.

Here’s an example:

from pandas import Interval
from pandas.core.indexes.interval import IntervalTree

intervals = IntervalTree([Interval(1, 3, 'right'), Interval(2, 4, 'left')])
overlap = intervals.overlaps(Interval(2, 4, 'left'))

Output: True

This snippet creates an IntervalTree containing two intervals and then uses the overlaps() method from IntervalTree to check if any interval in the tree overlaps with the given interval. The indicated interval does overlap with another in the tree.

Bonus One-Liner Method 5: Using Set Intersection

As a bonus one-liner method, you can convert intervals to sets of points and use set intersection to test for an overlap. Although this is not the most efficient method for interval comparisons, it can be useful for small or discrete intervals.

Here’s an example:

overlap = set(range(1, 3)).intersection(set(range(2, 4))) != set()

Output: True

This code converts the intervals into sets of integer points and checks for any intersection between these sets. Non-empty intersection implies an overlap, as shown in this example.

Summary/Discussion

  • Method 1: Interval.overlaps(). Straightforward and purpose-built. It may not offer additional control for custom logic.
  • Method 2: Manual Comparison. Offers flexibility and control. It requires more code and is error-prone.
  • Method 3: IntervalIndex.contains(). Good for checking multiple intervals. May be less intuitive for simple interval comparisons.
  • Method 4: Using IntervalTree. Efficient for large sets of data. Overkill for simple or one-off comparisons.
  • Method 5: Using Set Intersection. Quick and easy for discrete intervals. Not suitable for large ranges or non-integer intervals.