ArithmeticError - Python

Arithmetic errors in Python are common and occur when mathematical operations fail to execute properly. These errors can be caused by various issues, such as invalid input, division by zero, or exceeding number limits. Understanding the causes and how to handle these errors is important for writing robust Python programs.

a = 10
b = 0
res = a / b  # Raises ZeroDivisionError

Output

Hangup (SIGHUP)
Traceback (most recent call last):
  File "/home/guest/sandbox/Solution.py", line 3, in <module>
    res = a / b  # Raises ZeroDivisionError
          ~~^~~
ZeroDivisionError: division by zero

In this article, we'll explore common types of arithmetic errors, their causes and how to handle them.

Types of Airthmetic Errors in Python

• OverflowError
• ValueError
• TypeError
• FloatingPoint Error

OverflowError

An OverflowError occurs when a number exceeds the maximum limit that Python's data types can represent, typically in cases of large numbers, recursion or exponentiation. For Example:

a = 10 ** 10000  # Results in OverflowError in some systems
print(a)

Output

Hangup (SIGHUP)
Traceback (most recent call last):
  File "/home/guest/sandbox/Solution.py", line 2, in <module>
    print(a)
    ~~~~~^^^
ValueError: Exceeds the limit (4300 digits) for integer string conversion; use sys.set_int_max_str_digits() to increase the limit

To handle an OverflowError, break calculations into smaller parts or use data types like Python's decimal module for high-precision arithmetic.

from decimal import Decimal
a = Decimal(10) ** 10000  # Handling large numbers using Decimal
print(a)

1.000000000000000000000000000E+10000

ValueError

ValueError occurs when an invalid operation is performed on incompatible data types, such as adding a string to an integer.

a = "10"
b = 5
res = a + b  # Raises ValueError because num1 is a string

Output

Hangup (SIGHUP)
Traceback (most recent call last):
  File "/home/guest/sandbox/Solution.py", line 3, in <module>
    res = a + b  # Raises ValueError because num1 is a string
          ~~^~~
TypeError: can only concatenate str (not "int") to str

To handle a ValueError, ensure values are of the correct data type before performing operations, and use exception handling to catch and manage errors gracefully.

a = "10"
b = 5

try:
    res = int(a) + b  # Convert num1 to an integer before addition
    print(res)
except ValueError:
    print("Invalid input")

15

TypeError

TypeError occurs when performing arithmetic operations on incompatible data types, such as adding a list and a number.

a = [1, 2, 3]
b = 4
res = a + b  # Raises TypeError because a list and integer can't be added

Output

Hangup (SIGHUP)
Traceback (most recent call last):
  File "/home/guest/sandbox/Solution.py", line 3, in <module>
    res = a + b  # Raises TypeError because a list and integer can't be added
          ~~^~~
TypeError: can only concatenate list (not "int") to list

To handle a TypeError, ensure operands are of the correct type before operations, and use type checking or try-except blocks to manage errors.

a = [1, 2, 3]
b = 4

if isinstance(a, int) and isinstance(b, int):
    result = a + b
else:
    print("Both operands must be integers.")

Both operands must be integers.

FloatingPoint Error

FloatingPointError occurs when floating-point arithmetic results in precision issues leading to unexpected outcomes, especially in operations like comparisons.

a = 0.1 + 0.2
b = 0.3
print(a == b)  # False due to floating-point precision error

False

To handle a FloatingPointError, use the math.isclose() function to check for approximate equality of floating-point numbers.

a = 0.1 + 0.2
b = 0.3

import math
if math.isclose(a, b):
    print("The numbers are approximately equal.")
else:
    print("The numbers are not equal.")

The numbers are approximately equal.