5 Best Ways to Find Contiguous True Values in a Boolean Range in Python

πŸ’‘ Problem Formulation: Python developers often encounter the need to identify contiguous ranges of True values within a boolean array. This operation is essential, for instance, when processing time-series data points that meet certain criteria. Suppose we have an input [True, True, False, True, True, True, False, True], we seek to extract the ranges of indices where the values are continuously True, such as [(0, 1), (3, 5), (7, 7)].

Method 1: Using itertools.groupby()

The itertools.groupby() function is a powerful tool that groups items in an iterable if they are identical and occur in a row. For boolean ranges, we can use groupby to cluster contiguous True values and then extract the indices of these groups.

Here’s an example:

from itertools import groupby

def contiguous_true_ranges(data):
    ranges = []
    start = 0
    for value, group in groupby(data):
        length = sum(1 for _ in group)
        if value:
            ranges.append((start, start + length - 1))
        start += length
    return ranges

result = contiguous_true_ranges([True, True, False, True, True, True, False, True])
print(result)

Output:

[(0, 1), (3, 5), (7, 7)]

This code snippet utilizes groupby() to combine adjacent True values and then calculates the starting and ending indices of these groups. By iterating through the input data, it builds a list of tuple ranges that encapsulate the contiguous runs of True values.

Method 2: Using NumPy

NumPy provides vectorized operations that can be useful for finding contiguous regions in an efficient manner. By leveraging logical operations and functions such as np.where() and np.diff(), we can find the beginning and end of True regions fast.

Here’s an example:

import numpy as np

def contiguous_true_ranges_numpy(data):
    data = np.array(data)
    edges = np.diff(data.astype(int))
    starts = np.where(edges == 1)[0] + 1
    ends = np.where(edges == -1)[0]
    if data[0]:
        starts = np.insert(starts, 0, 0)
    if data[-1]:
        ends = np.append(ends, len(data) - 1)
    return list(zip(starts, ends))

result = contiguous_true_ranges_numpy([True, True, False, True, True, True, False, True])
print(result)

Output:

[(0, 1), (3, 5), (7, 7)]

Here, the boolean array is converted to an integer array so that differences between subsequent elements can be computed using np.diff(). Entries of 1 signal the start of a True sequence, while -1 marks the end. We adjust for edge cases at the beginning and end of the array, and then pair the start and end indices.

Method 3: Looping Manually

For those who prefer a more basic Python approach without third-party libraries, we can manually loop through the boolean array to track the start and end of contiguous ranges. This approach provides more control over the process and avoids additional dependencies.

Here’s an example:

def contiguous_true_ranges_loop(data):
    ranges = []
    start = None
    for i, value in enumerate(data):
        if value and start is None:
            start = i
        elif not value and start is not None:
            ranges.append((start, i - 1))
            start = None
    if start is not None:
        ranges.append((start, len(data) - 1))
    return ranges

result = contiguous_true_ranges_loop([True, True, False, True, True, True, False, True])
print(result)

Output:

[(0, 1), (3, 5), (7, 7)]

This method involves iterating through each element of the list, tracking when a series of True values starts and ends. It appends a range to the output list each time it finds a transition from True to False, and handles any remaining True values at the end of the list.

Method 4: Using Pandas

Pandas is a data manipulation library that can greatly simplify certain operations, including identifying contiguous true ranges. Specifically, the pandas.Series object, along with boolean indexing and the cumsum() trick, can be used to find and extract ranges conveniently.

Here’s an example:

import pandas as pd

def contiguous_true_ranges_pandas(data):
    s = pd.Series(data)
    s1 = s != s.shift()
    starts = s & s1
    ends = s & (~s).shift(-1, fill_value=True)
    return list(zip(starts[starts].index, ends[ends].index))

result = contiguous_true_ranges_pandas([True, True, False, True, True, True, False, True])
print(result)

Output:

[(0, 1), (3, 5), (7, 7)]

This code uses Pandas Series methods to identify starts and ends of true blocks. s.shift() shifts the series so that we can compare with the previous element, identifying changes. The starts and ends of true ranges are computed using boolean indexing, and finally, zip() combines the starting and ending indices to form the ranges.

Bonus One-Liner Method 5: List Comprehension with zip

A Pythonic way to address this problem is to leverage list comprehension along with the zip function, combining previous techniques in a concise expression.

Here’s an example:

def contiguous_true_ranges_zip(data):
    diff = [i for i, value in enumerate(data + [False]) if value != (data + [True])[i+1]]
    return list(zip(diff[::2], diff[1::2]))

result = contiguous_true_ranges_zip([True, True, False, True, True, True, False, True])
print(result)

Output:

[(0, 1), (3, 5), (7, 7)]

The list comprehension builds a list of indices where the boolean value differs from the next one. By appending a False at the end of our data, we ensure that the last True in the original data gets its range closed. zip pairs every two elements in this list (start and end indices) to get the contiguous ranges.

Summary/Discussion

  • Method 1: itertools.groupby(). The groupby method is elegant and part of the standard library, avoiding extra dependencies. However, performance may lag behind vectorized approaches for large datasets.
  • Method 2: NumPy. NumPy is fast and efficient for operations on large arrays due to its vectorized computation, but it requires installation of the external NumPy library.
  • Method 3: Looping Manually. Manual looping is simple and doesn’t rely on external libraries, making it universally applicable. This method might be slower compared to other techniques for large data sets.
  • Method 4: Pandas. Using Pandas can be incredibly fast and concise, with a syntax that can be more readable for those familiar with the library. However, it introduces a heavy dependency that’s not needed for simpler tasks.
  • Method 5: List Comprehension with zip. This one-liner approach is Pythonic and succinct. Yet, it may be less readable for less experienced Python programmers, and thus less maintainable.