💡 Problem Formulation: In Python, you might often need to convert an array of boolean values to an array of integers for operations that require numeric binary data, such as bitwise computations or data storage optimization. This article will show how to transform a boolean array like [True, False, True] into an integer array like [1, 0, 1] using different methods.
Method 1: Using a List Comprehension
An effective and pythonic way to convert a boolean array into an integer one is via list comprehension. This method iterates over each element in the boolean array, casting it as an int directly in the new list being created.
Here’s an example:
bool_array = [True, False, True, False] int_array = [int(val) for val in bool_array]
Output:
[1, 0, 1, 0]
This list comprehension iterates over each boolean element in bool_array and converts it to an integer using the int() function, resulting in int_array.
Method 2: Using the map() Function
The map() function can also be used to convert each boolean in an array to an integer. It applies the int() function to every item of the iterable, returning a map object that can be easily transformed into a list.
Here’s an example:
bool_array = [True, False, True, False] int_array = list(map(int, bool_array))
Output:
[1, 0, 1, 0]
The map(int, bool_array) applies the int() function to each item in the bool_array. The resulting map object is then converted into a list to get int_array.
Method 3: Using NumPy
For those working with scientific computing in Python, NumPy offers a straightforward way to convert boolean arrays into integer arrays by leveraging its array type conversion methods.
Here’s an example:
import numpy as np bool_array = np.array([True, False, True, False]) int_array = bool_array.astype(int)
Output:
[1 0 1 0]
Using NumPy’s astype(int) method on a NumPy boolean array will create a new array where all boolean values are cast to integers.
Method 4: Using a Custom Function
If precise control over the conversion logic is needed, or additional operations must be performed during conversion, a custom function can be defined to explicitly handle the conversion of each boolean to an integer.
Here’s an example:
def bool_to_int(bool_array):
return [1 if value else 0 for value in bool_array]
bool_array = [True, False, True, False]
int_array = bool_to_int(bool_array)
Output:
[1, 0, 1, 0]
The custom function bool_to_int() uses a list comprehension with a conditional expression to convert each boolean in the provided bool_array into int_array.
Bonus One-Liner Method 5: Using the * operator
A compact, yet less immediately readable method to achieve the bool to int conversion is by using the unpacking operator * within the zip function. This leverages the truth that Booleans in Python are subclasses of integers.
Here’s an example:
bool_array = [True, False, True, False] int_array = [*map(int, bool_array)]
Output:
[1, 0, 1, 0]
This one-liner uses map() to apply the int() function to each boolean value, with the unpacking operator * being used to convert the map object to a list in one step.
Summary/Discussion
Method 1: List Comprehension. Simple and concise. Ideal for small arrays. May not be the best option for very large arrays due to potential performance issues.
Method 2: map() Function. Clean syntax and efficient for large datasets. It’s more Pythonic, but some may find it less readable than list comprehension.
Method 3: NumPy Conversion. Highly efficient for large numerical data. Requires NumPy, which is not a part of standard Python library.
Method 4: Custom Function. Offers flexibility and readability. Beneficial when additional logic is required during conversion.
Bonus Method 5: Unpacking Operator. A concise one-liner. May sacrifice readability but useful for writing shorter code.