Understanding Interval Index Closure in Pandas: Left, Right, Both or Neither?

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💡 Problem Formulation: When working with interval data in Pandas, it’s important to know if an interval is closed on the left side, the right side, both sides or neither. This is necessary for accurate data analysis and manipulation. For instance, given an IntervalIndex, you might need to confirm its closure to correctly interpret range queries or to align it with other interval data. The desired output is a clear indication of the closure of intervals within an IntervalIndex object.

Method 1: Inspecting the closed Attribute

The closed attribute of a Pandas IntervalIndex object provides direct information about how the intervals are closed. It returns a string that can be ‘left’, ‘right’, ‘both’, or ‘neither’ indicating the closure of the intervals within the IntervalIndex.

Here’s an example:

import pandas as pd

# Create an IntervalIndex
intervals = pd.IntervalIndex.from_tuples([(0, 1), (2, 3)], closed='left')
print(intervals.closed)

Output:

'left'

This code snippet creates an IntervalIndex with intervals closed on the left and prints the closure type. By inspecting the closed attribute of the IntervalIndex, we can determine that the intervals are left-sided.

Method 2: Checking Interval Objects Individually

Intervals within an IntervalIndex can be inspected individually using a loop to determine their closure. Each interval has a closed attribute similar to an IntervalIndex. This method provides confirmation of closure for each interval.

Here’s an example:

for interval in intervals:
    print(interval, interval.closed)

Output:

(0, 1] 'left'
(2, 3] 'left'

This code iterates over each interval in the IntervalIndex and prints the interval along with its closure type. Each interval confirms to be ‘left’ closed.

Method 3: Using the Interval.overlaps Method Sparingly

Although not a direct check, you can use the overlaps method on individual intervals to infer about closure. If an interval does not overlap with a point just outside its boundary, this can hint at it being open on that side.

Here’s an example:

point_right_of_interval = 1
print(intervals[0].overlaps(pd.Interval(point_right_of_interval, point_right_of_interval)))

Output:

False

Since the interval (0, 1] does not overlap with a point interval at 1, we infer that it is not closed on the right side. However, this method requires careful consideration of edge cases and is less direct.

Method 4: Using the Interval.closed_left and Interval.closed_right Properties

Each Interval object has closed_left and closed_right properties that return boolean values, indicating if an interval is closed on the left or right side respectively.

Here’s an example:

for interval in intervals:
    print(interval, interval.closed_left, interval.closed_right)

Output:

(0, 1] True False
(2, 3] True False

Each pair of boolean values confirms that the intervals are closed on the left but not on the right, as True indicates closure while False indicates openness.

Bonus One-Liner Method 5: List Comprehension with closed Attribute

For a concise and quick check of all the intervals’ closure, a one-liner using list comprehension along with the closed attribute can be employed.

Here’s an example:

closure_types = [interval.closed for interval in intervals]
print(closure_types)

Output:

['left', 'left']

This one-liner generates a list of closure types for all intervals in the IntervalIndex, confirming each as ‘left’ closed.

Summary/Discussion

  • Method 1: Inspecting closed Attribute. Straightforward and efficient. It gives an overview of the closure for the entire IntervalIndex but does not check individual intervals.
  • Method 2: Checking Individually. Accurate and detailed. It can be cumbersome if you only need the general closure type for the IntervalIndex.
  • Method 3: Using overlaps. Indirect and requires careful analysis. It’s more useful for in-depth interval analysis than for a quick closure check.
  • Method 4: closed_left and closed_right Properties. Direct and informative at the individual interval level. Similar to Method 2 but offers boolean values for clarity.
  • Bonus Method 5: List Comprehension. Quick and compact. It’s a convenient way to output all closures in a single list but lacks the context of individual intervals.