Creating an IntervalArray from Splits in Pandas & Checking Closed Intervals

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💡 Problem Formulation: Python’s Pandas library provides the powerful IntervalArray to handle intervals data efficiently. Developers often need to create an IntervalArray from an array of split values and verify whether the intervals are closed on the left, right, both, or neither. For example, given an array of splits [1, 3, 5, 7], we want to create intervals that might look like [(1, 3], (3, 5], (5, 7]], and then ascertain their ‘closed’ property.

Method 1: Using pandas.IntervalArray.from_breaks with Named Parameters

The pandas.IntervalArray.from_breaks method takes an array of split points and a ‘closed’ parameter to specify which side of the interval should be closed. This method directly maps the provided array to a list of intervals considering the given ‘closed’ option.

Here’s an example:

import pandas as pd

breaks = [1, 3, 5, 7]
intervals = pd.IntervalArray.from_breaks(breaks, closed='right')

print(intervals)

Output:

IntervalArray([(1, 3], (3, 5], (5, 7]], dtype='interval[int64, right]')

In this snippet, we created an IntervalArray which contains intervals that are closed on the right. The closed='right' parameter defines that each interval excludes the left endpoint and includes the right endpoint.

Method 2: Using pandas.cut for Binning Data into Intervals

pandas.cut function is often used for segmenting and sorting data into bins. When used with its ‘right’ parameter, it also creates an IntervalArray from the resulting categorical data with a specified closure.

Here’s an example:

import pandas as pd
import numpy as np

data = np.array([2, 4, 6])
bins = [1, 3, 5, 7]
intervals = pd.cut(data, bins, right=False)

print(intervals)

Output:

[(1, 3), [3, 5), [5, 7)]
Categories (3, interval[int64, left]): [(1, 3) < [3, 5) < [5, 7)]

This code uses pandas.cut to bin the data array into intervals defined by the breaks in bins. By setting right=False, we ensure that the intervals are closed on the left.

Method 3: Using List Comprehension and pandas.Interval Objects

List comprehension in Python can be harnessed alongside pandas.Interval objects to create an IntervalArray manually. Each pandas.Interval can be set with individual left and right closure.

Here’s an example:

import pandas as pd

breaks = [1, 3, 5, 7]
intervals = pd.IntervalArray([pd.Interval(left, right, closed='both') for left, right in zip(breaks[:-1], breaks[1:])])

print(intervals)

Output:

IntervalArray([(1, 3], [3, 5], [5, 7]], dtype='interval[int64, both]')

By using list comprehension and zipping together the breaks, we create a list of pandas.Interval objects and pass it to pandas.IntervalArray. Each interval is set as closed on both sides.

Method 4: Using Numpy’s array_split Method

Numpy’s array_split function is generally used to split an array into multiple sub-arrays, but we can also use it to define the bounds for intervals when combined with pandas.IntervalArray.

Here’s an example:

import pandas as pd
import numpy as np

data = np.arange(10)
splits = np.array_split(data, indices_or_sections=3)
bounds = [(x[0], x[-1] + 1) for x in splits if len(x) > 0]
intervals = pd.IntervalArray.from_tuples(bounds, closed='neither')

print(intervals)

Output:

IntervalArray([(0, 4), (4, 7), (7, 10)], dtype='interval[int64, neither]')

This approach utilizes Numpy’s array_split to create sub-arrays from which we derive our interval bounds. Intervals are then created as neither left- nor right-closed.

Bonus One-Liner Method 5: Using pd.IntervalIndex

For creating an Index of intervals which could easily be converted to an IntervalArray, we can use the convenience of pd.IntervalIndex.

Here’s an example:

import pandas as pd

breaks = [1, 3, 5, 7]
intervals = pd.IntervalIndex.from_breaks(breaks, closed='left').array

print(intervals)

Output:

IntervalArray([[1, 3), [3, 5), [5, 7)], dtype='interval[int64, left]')

This focuses on the pd.IntervalIndex which provides an array-like data structure. By calling the .array property, we can convert it to an IntervalArray closed on the left.

Summary/Discussion

  • Method 1: Using from_breaks. Direct and concise. Limited to a single ‘closed’ parameter for all intervals.
  • Method 2: Utilizing pandas.cut. Great for binning existing data into intervals. Requires actual data points rather than just the splits.
  • Method 3: List Comprehension with pandas.Interval. Offers flexibility for custom closures for each interval. Possibly verbose and less efficient.
  • Method 4: Numpy array_split Approach. Integrates Numpy’s splitting with Pandas’ intervals. Unconventional method that may be less readable.
  • Bonus Method 5: pd.IntervalIndex. Simplistic one-liner, with the utility of an index object. Converts to IntervalArray but starts as an IntervalIndex.