**ArithmeticError** is the base class for all arithmetic-related errors. To solve **ArtithmeticError** in **Python**, use the **try-except** clause properly. **Try** and **except** statement is used to handle exceptions without terminating the program.

The **ArithmeticError** consists of three exceptions.

- ZeroDivisionError
- FloatingPointError
- OverFlowError

These three types of errors can be caught by a single exception called the **ArithmeticError** exception.

**ZeroDivisionError in Python**

The ZeroDivisionError is a type of exception raised when a number is divided by 0. In this type of error, the denominator should not be 0. If the value of the denominator is changed and other than 0, then the code works correctly. This is not a critical error. It is just an indication that a number is divided by 0.

**Example**

```
a = 5
b = 0
try:
c = a/b
print(c)
except ArithmeticError as e:
print("Arithmetic Error is thrown")
print(f"{e}, {e.__class_}")
```

**Output**

```
Arithmetic Error is thrown
Traceback (most recent call last):
File "/Users/krunallathiya/Desktop/Code/R/data.py", line 4, in <module>
c = a/b
ZeroDivisionError: division by zero
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/krunallathiya/Desktop/Code/R/data.py", line 8, in <module>
print(f"{e}, {e.__class_}")
AttributeError: 'ZeroDivisionError' object has no attribute '__class_'
```

The above code throws **ZeroDivisionError: ****division by error, <class ‘ZeroDivisionError’>.** When a number is divided by zero, this error is raised. This can be solved by replacing the denominator number.

You can directly use **ZeroDivisionError** in the except class. However, if you don’t know what error occurs, we can generally use **ArithmeticError**. Now, let’s see how to handle **ZeroDivisionError**.

```
a = 5
b = 0
try:
c = a/b
print(c)
except ZeroDivisionError:
print("ZeroDivisionError is occurred. Please change the denominator value")
```

**Output**

`ZeroDivisionError is occurred. Please change the denominator value`

**OverFlowError in Python**

The **OverFlowError** **exception** in **Python** is thrown when a number exceeds its limit. Every number data type has some starting and ending limit, and if a number exceeds this limit, the **OverFlowError** exception is raised.

**Example**

```
import math
print("Simple program for showing overflow error")
print("\n")
print("The exponential value is")
print(math.exp(1000))
```

**Output**

```
Simple program for showing overflow error
The exponential value is
Traceback (most recent call last):
File "/Users/krunallathiya/Desktop/Code/R/data.py", line 6, in <module>
print(math.exp(1000))
OverflowError: math range error
```

You can see that it throws an **OverflowError: math range error.**

In the above program, we can see that we are importing a math module and using it to calculate exponential values such as **exp(1000),** which means **e^x** here **x** **value** is **1000** and **e** value is **2.7** where when are trying to calculate this. As a result, it is double, and it cannot print the result.

Therefore, it gives an **OverflowError: math range error** as seen in the above program, which says it is out of range because the **x** value is **1000,** which when results give the value out of range or double to store the value and print it.

To solve the **OverflowError: math range** error in Python, use the **try-except** clause, and if it raises an exception, then handle it using **except** statement.

```
import math
try:
result = math.exp(1000)
except OverflowError:
result = float('inf')
print(result)
```

**Output**

`inf`

You can see that **math.exp(1000)** returns **Infinity, **and we handle it properly, then it won’t crash; otherwise, it returns the **OverflowError,** a type of **ArithmeticError**.

**FloatingPointError in Python**

This type of error is raised when a number has large decimal digits. A well-known example is when we divide 10 by 3. The decimal places go on like 3.333… hence this error can be rounded to the nearest number.

You can also trigger a **FloatingPointError** within **numpy** by setting the appropriate **numpy.errstate** flag.

```
import numpy as np
with np.errstate(invalid='raise'):
np.sqrt(-1)
```

**Output**

`FloatingPointError: invalid value encountered in sqrt`

You can see that it throws a **FloatingPointError: invalid value encountered in sqrt.**

To solve **FloatingPointError** in **Python**, use the **try-except **statement.

```
import numpy as np
try:
with np.errstate(invalid='raise'):
np.sqrt(-1)
except FloatingPointError:
print("Caught FloatingPointError")
```

**Output**

`Caught FloatingPointError`

This is one of the simplest ways to handle the **FloatingPointError** **exception**.

**Conclusion**

We saw all three ArithmeticError exceptions and how to handle them properly in Python.

- ZeroDivisionError
- OverflowError
- FloatingPointError

That’s it for this tutorial.

**See also**

TypeError: list indices must be integers or slices, not str

TypeError: Only size-1 arrays can be converted to Python scalars

Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language. Krunal has experience with various programming languages and technologies, including PHP, Python, and JavaScript. He is comfortable working in front-end and back-end development.