**π‘ 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.