Using the "and" Boolean Operator in Python

Using Python’s and Operator With Boolean Expressions

You’ll typically use logical operators to build compound Boolean expressions, which are combinations of variables and values that produce a Boolean value as a result. In other words, Boolean expressions return True or False.

Comparisons and equality tests are common examples of this type of expression:

>>> 5 == 3 + 2
True
>>> 5 > 3
True
>>> 5 < 3
False
>>> 5 != 3
True

>>> [5, 3] == [5, 3]
True

>>> "hi" == "hello"
False

All these expressions return True or False, which means they’re Boolean expressions. You can combine them using the and keyword to create compound expressions that test two—or more–subexpressions at a time:

>>> 5 > 3 and 5 == 3 + 2
True

>>> 5 < 3 and 5 == 5
False

>>> 5 == 5 and 5 != 5
False

>>> 5 < 3 and 5 != 5
False

Here, when you combine two True expressions, you get True as a result. Any other combination returns False. From these examples, you can conclude that the syntax for creating compound Boolean expressions with the and operator is the following:

expression1 and expression2

If both subexpressions expression1 and expression2 evaluate to True, then the compound expression is True. If at least one subexpression evaluates to False, then the result is False.

There’s no limit to the number of and operators you can use when you’re building a compound expression. This means that you can combine more than two subexpressions in a single expression using several and operators:

>>> 5 > 3 and 5 == 3 + 2 and 5 != 3
True

>>> 5 < 3 and 5 == 3 and 5 != 3
False

Again, if all the subexpressions evaluate to True, then you get True. Otherwise, you get False. Especially as expressions get longer, you should keep in mind that Python evaluates the expressions sequentially from left to right.

Short-Circuiting the Evaluation

Python’s logical operators, such as and and or, use something called short-circuit evaluation, or lazy evaluation. In other words, Python evaluates the operand on the right only when it needs to.

To determine the final result of an and expression, Python starts by evaluating the left operand. If it’s false, then the whole expression is false. In this situation, there’s no need to evaluate the operand on the right. Python already knows the final result.

Having a false left operand automatically makes the whole expression false. It would be a waste of CPU time to evaluate the remaining operand. Python prevents this by short-circuiting the evaluation.

In contrast, the and operator evaluates the operand on the right only if the first operand is true. In this case, the final result depends on the right operand’s truth value. If it’s true, then the whole expression is true. Otherwise, the expression is false.

To demonstrate the short-circuiting feature, take a look at the following examples:

>>> def true_func():
...     print("Running true_func()")
...     return True
...

>>> def false_func():
...     print("Running false_func()")
...     return False
...

>>> true_func() and false_func()  # Case 1
Running true_func()
Running false_func()
False

>>> false_func() and true_func()  # Case 2
Running false_func()
False

>>> false_func() and false_func()  # Case 3
Running false_func()
False

>>> true_func() and true_func()  # Case 4
Running true_func()
Running true_func()
True

Here’s how this code works:

• Case 1: Python evaluates true_func(), which returns True. To determine the final result, Python evaluates false_func() and gets False. You can confirm this by seeing both functions’ output.
• Case 2: Python evaluates false_func(), which returns False. Python already knows that the final result is False, so it doesn’t evaluate true_func().
• Case 3: Python runs false_func() and gets False as a result. It doesn’t need to evaluate the repeated function a second time.
• Case 4: Python evaluates true_func() and gets True as a result. It then evaluates the function again. Since both operands evaluate to True, the final result is True.

Python processes Boolean expressions from left to right. It stops when it no longer needs to evaluate any further operands or subexpressions to determine the final outcome. To sum up this concept, you should remember that if the left operand in an and expression is false, then the right operand won’t be evaluated.

Short-circuit evaluation can have a significant impact on your code’s performance. To take advantage of that, consider the following tips when you’re building and expressions:

• Place time-consuming expressions on the right of the and keyword. This way, the costly expression won’t run if the short-circuit rule takes effect.
• Place the expression that is more likely to be false on the left of the and keyword. This way, it’s more likely that Python can determine if the whole expression is false by evaluating the left operand only.

Sometimes you may want to avoid lazy evaluation in a specific Boolean expression. You can do so by using the bitwise operators (&, |, ~). These operators also work in Boolean expressions, but they evaluate the operands eagerly:

>>> def true_func():
...     print("Running true_func()")
...     return True
...

>>> def false_func():
...     print("Running false_func()")
...     return False
...

>>> # Use logical and
>>> false_func() and true_func()
Running false_func()
False

>>> # Use bitwise and
>>> false_func() & true_func()
Running false_func()
Running true_func()
False

In the first expression, the and operator works lazily, as expected. It evaluates the first function, and since the result is false, it doesn’t evaluate the second function. In the second expression, however, the bitwise AND operator (&) calls both functions eagerly even though the first function returns False. Note that in both cases, the final result is False.

Even though this trick works, it’s generally discouraged. You should use bitwise operators to manipulate bits, and Boolean operators to work with Boolean values and expressions. For a deeper dive into bitwise operators, check out Bitwise Operators in Python.

Using Python’s and Operator With Common Objects

You can use the and operator to combine two Python objects in a single expression. In that situation, Python internally uses bool() to determine the truth value of the operands. As a result, you get a specific object rather than a Boolean value. You only get True or False if a given operand explicitly evaluates to True or False:

>>> 2 and 3
3

>>> 5 and 0.0
0.0

>>> [] and 3
[]

>>> 0 and {}
0

>>> False and ""
False

In these examples, the and expression returns the operand on the left if it evaluates to False. Otherwise, it returns the operand on the right. To produce these results, the and operator uses Python’s internal rules to determine an object’s truth value. The Python documentation states these rules like this:

By default, an object is considered true unless its class defines either a __bool__() method that returns False or a __len__() method that returns zero, when called with the object. Here are most of the built-in objects considered false:

• constants defined to be false: None and False.
• zero of any numeric type: 0, 0.0, 0j, Decimal(0), Fraction(0, 1)
• empty sequences and collections: '', (), [], {}, set(), range(0)

(Source)

With these rules in mind, look again at the code above. In the first example, the integer number 2 is true (nonzero), so and returns the right operand, 3. In the second example, 5 is true, so and returns the right operand even though it evaluates to False.

The next example uses an empty list ([]) as the left operand. Since empty lists evaluate to false, the and expression returns the empty list. The only case where you get True or False is the one where you use a Boolean object explicitly in the expression.

Here’s how you can summarize the behavior of the and operator when you use it with common Python objects instead of Boolean expressions. Note that Python uses the truth value of each object to determine the final result:

In general, if the operands in an and expression are objects instead of Boolean expressions, then the operator returns the object on the left if it evaluates to False. Otherwise, it returns the object on the right, even when it evaluates to False.

Mixing Boolean Expressions and Objects

You can also combine Boolean expressions and common Python objects within an and expression. In that situation, the and expression still returns the left operand if it’s false, or else it returns the right operand. The returned value could be True, False, or a regular object, depending on which part of the expression provides that result:

>>> 2 < 4 and 2
2
>>> 2 and 2 < 4
True

>>> 2 < 4 and []
[]
>>> [] and 2 < 4
[]

>>> 5 > 10 and {}
False
>>> {} and 5 > 10
{}

>>> 5 > 10 and 4
False
>>> 4 and 5 > 10
False

These examples use a combination of Boolean expressions and common objects. In each pair of examples, you see that you can get either a non-Boolean object or a Boolean value, True or False. The result will depend on which part of the expression provides the final result.

Here’s a table that summarizes the behavior of the and operator when you combine Boolean expressions and common Python objects:

To find out what’s returned, Python evaluates the Boolean expression on the left to get its Boolean value (True or False). Then Python uses its internal rules to determine the truth value of the object on the right.

As an exercise to test your understanding, you could try to rewrite this table by swapping the order of the operands in the third column to object and expression. Try to predict what will be returned in each row.

Combining Python Logical Operators

As you’ve seen earlier in this tutorial, Python provides two additional logical operators: the or operator and the not operator. You can use them along with the and operator to create more complex compound expressions. If you want to make accurate and clear expressions with multiple logical operators, then you need to consider the precedence of each operator. In other words, you need to consider the order in which Python executes them.

Python’s logical operators have low precedence when compared with other operators. However, sometimes it’s healthy to use a pair of parentheses (()) to ensure a consistent and readable result:

>>> 5 or 2 and 2 > 1
5

>>> (5 or 3) and 2 > 1
True

These examples combine the or operator and the and operator in a compound expression. Just like the and operator, the or operator uses short-circuit evaluation. Unlike and, however, the or operator stops once it finds a true operand. You can see this in the first example. Since 5 is true, the or subexpression immediately returns 5 without evaluating the rest of the expression.

In contrast, if you enclose the or subexpression in a pair of parentheses, then it works as a single true operand, and 2 > 1 gets evaluated as well. The final result is True.

The takeaway is that if you’re using multiple logical operators in a single expression, then you should consider using parentheses to make your intentions clear. This trick will also help you get the correct logical result.

if Statements

Boolean expressions are commonly known as conditions because they typically imply the need for meeting a given requirement. They’re pretty useful in the context of conditional statements. In Python, this type of statement starts with the if keyword and continues with a condition. A conditional statement can additionally include elif and else clauses.

Python conditional statements follow the logic of conditionals in English grammar. If the condition is true, then the if code block executes. Otherwise, the execution jumps to a different code block:

>>> a = -8

>>> if a < 0:
...     print("a is negative")
... elif a > 0:
...     print("a is positive")
... else:
...     print("a is equal to 0")
...
a is negative

Since a holds a negative number, the condition a < 0 is true. The if code block runs, and you get the message a is negative printed on your screen. If you change the value of a to a positive number, however, then the elif block runs and Python prints a is positive. Finally, if you set a to zero, then the else code block executes. Go ahead and play with a to see what happens!

Now say you want to make sure that two conditions are met—meaning that they’re both true—before running a certain piece of code. To try this out, suppose you need to get the age of a user running your script, process that information, and display to the user their current life stage.

Fire up your favorite code editor or IDE and create the following script:

# age.py

age = int(input("Enter your age: "))

if age >= 0 and age <= 9:
    print("You are a child!")
elif age > 9 and age <= 18:
    print("You are an adolescent!")
elif age > 18 and age <= 65:
    print("You are an adult!")
elif age > 65:
    print("Golden ages!")

Here, you get the user’s age using input() and then convert it to an integer number with int(). The if clause checks if age is greater than or equal to 0. In the same clause, it checks if age is less than or equal to 9. To do this, you build an and compound Boolean expression.

The three elif clauses check other intervals to determine the life stage associated with the user’s age.

If you run this script from your command line, then you get something like this:

$ python age.py
Enter your age: 25
You are an adult!

Depending on the age you enter at the command line, the script takes one course of action or another. In this specific example, you provide an age of 25 years and get the message You are an adult! printed to your screen.

while Loops

The while loop is the second construct that can use and expressions to control a program’s execution flow. By using the and operator in the while statement header, you can test several conditions and repeat the loop’s code block for as long as all conditions are met.

Say you’re prototyping a control system for a manufacturer. The system has a critical mechanism that should work with a pressure of 500 psi or lower. If the pressure goes over 500 psi while staying under 700 psi, then the system has to run a given series of standard safety actions. For pressures greater than 700 psi, there are a whole new set of safety actions that the system must run.

To approach this problem, you can use a while loop with an and expression. Here’s a script that simulates a possible solution:

 1# pressure.py
 2
 3from time import sleep
 4from random import randint
 5
 6def control_pressure():
 7    pressure = measure_pressure()
 8    while True:
 9        if pressure <= 500:
10            break
11
12        while pressure > 500 and pressure <= 700:
13            run_standard_safeties()
14            pressure = measure_pressure()
15
16        while pressure > 700:
17            run_critical_safeties()
18            pressure = measure_pressure()
19
20    print("Wow! The system is safe...")
21
22def measure_pressure():
23    pressure = randint(490, 800)
24    print(f"psi={pressure}", end="; ")
25    return pressure
26
27def run_standard_safeties():
28    print("Running standard safeties...")
29    sleep(0.2)
30
31def run_critical_safeties():
32    print("Running critical safeties...")
33    sleep(0.7)
34
35if __name__ == "__main__":
36    control_pressure()

Inside control_pressure(), you create an infinite while loop on line 8. If the system is stable and the pressure is below 500 psi, the conditional statement breaks out of the loop and the program finishes.

On line 12, the first nested while loop runs the standard safety actions while the system pressure stays between 500 psi and 700 psi. In each iteration, the loop gets a new pressure measurement to test the condition again in the next iteration. If the pressure grows beyond 700 psi, then the second loop on line 16 runs the critical safety actions.

def control_pressure():
    while True:
        pressure = measure_pressure()
        if pressure > 700:
            run_critical_safeties()
        elif 500 < pressure <= 700:
            run_standard_safeties()
        elif pressure <= 500:
            break
    print("Wow! The system is safe...")