💡 Problem Formulation: Converting Python boolean arrays into integer representations is a common task in data processing and bit manipulation tasks. For example, given an input array of boolean values such as [True, False, True], we want to transform it into its integer equivalent, with a desired output of 5 (the binary number 101 corresponds to integer 5).
Method 1: Using int() and join()
This method involves converting the boolean array into a string of ‘0’s and ‘1’s and then converting the string to an integer with the int() function. It is straightforward and clear in its intentions, making it easy to understand and debug.
Here’s an example:
bool_array = [True, False, True]
str_array = ['1' if b else '0' for b in bool_array]
int_representation = int(''.join(str_array), 2)
print(int_representation)Output: 5
This code snippet creates a new list str_array with ‘1’s for True and ‘0’s for False, then joins them into a string which represents a binary number. Finally, it uses int() with base 2 to convert the string into its integer equivalent.
Method 2: Using numpy.packbits()
NumPy’s packbits function is a powerful tool for quickly converting boolean arrays to their byte representation and then to their integer equivalent. This approach is optimized and perfect for large arrays or performance-critical code.
Here’s an example:
import numpy as np bool_array = np.array([True, False, True], dtype=np.bool) packed_byte_array = np.packbits(bool_array.astype(np.uint8)) int_representation = int.from_bytes(packed_byte_array, byteorder='big') print(int_representation)
Output: 5
The code first converts the boolean array into the np.uint8 type required by np.packbits(). The packed bytes are then converted into an integer using int.from_bytes().
Method 3: Using Bitwise Operations
Bitwise operations can manipulate individual bits to construct the integer representation of the boolean array. By shifting and OR-ing bits in place, this method effectively builds the integer from its binary form.
Here’s an example:
bool_array = [True, False, True]
int_representation = 0
for bit in bool_array:
int_representation = (int_representation << 1) | bit
print(int_representation)Output: 5
This code iterates through the boolean array, shifting the current value of int_representation to the left by one bit, and then applying the bitwise OR operation with the boolean value treated as 1 or 0.
Method 4: Using functools.reduce()
The functools.reduce function can be used to apply a rolling computation to the boolean array for converting it to an integer. This method treats the array conversion as a reduction problem.
Here’s an example:
from functools import reduce bool_array = [True, False, True] int_representation = reduce(lambda acc, x: (acc << 1) | x, bool_array) print(int_representation)
Output: 5
This code uses reduce() to apply a lambda function that shifts the accumulated value and performs a bitwise OR with the current boolean value. It accumulates the integer representation in a left-to-right traversal of the boolean array.
Bonus One-Liner Method 5: Using sum() and enumerate()
The sum function can iterate over the boolean array, utilizing enumerate to handle the exponentiation for bitwise left-shifts effectively. This one-liner method is concise and accurate for small arrays.
Here’s an example:
bool_array = [True, False, True] int_representation = sum(b << i for i, b in enumerate(reversed(bool_array))) print(int_representation)
Output: 5
The code uses a generator expression with sum, iterating over the boolean array in reversed order. For each boolean, it shifts a 1 left by the boolean’s index, resulting in the correct place-value for binary digits.
Summary/Discussion
- Method 1: int() and join(): Straightforward implementation. Slow for large arrays because of the string manipulation overhead.
- Method 2: numpy.packbits(): Fast and efficient for large arrays, requires NumPy installation, can be obscure for readers unfamiliar with NumPy.
- Method 3: Bitwise Operations: Intuitive for those familiar with bitwise operations, relatively fast, may be less concise than other methods.
- Method 4: functools.reduce(): Functional programming approach, concise, slight overhead because of lambda function.
- Method 5: sum() and enumerate(): Elegant one-liner, good for small arrays, not as efficient as NumPy for larger arrays.