How to Fix Python OverflowError: Python Int Too Large to Convert to C Long

1. the OverflowError: Python int too large to convert to C long in Python
2. Fix the OverflowError: Python int too large to convert to C long in Python

This tutorial will demonstrate the OverflowError: python int too large to convert to c long error in Python.

the OverflowError: Python int too large to convert to C long in Python

An OverflowError is raised in Python when an arithmetic result exceeds the given limit of the data type. We encounter this specified error because we try to operate with an integer’s value more than the given range.

Code:

import numpy as np

arr = np.zeros((1, 2), dtype=int)
a = [6580225610007]
arr[0] = a

Output:

OverflowError: Python int too large to convert to C long

The above example creates an array of type int. We try to store a list containing an integer greater than the int type range.

The maximum size can also be checked using a constant from the sys library. This constant is available as sys.maxsize.

Fix the OverflowError: Python int too large to convert to C long in Python

To avoid this error, we must be mindful of the maximum range we can use with the int type. A fix exists, especially in this example regarding a numpy array.

The int type was equal to the long int type of the C language and was changed in Python 3, and the int type was converted to an arbitrary precision type.

However, the numpy library still uses it as declared in Python 2. The long int is usually platform-dependent, but for windows, it is always 32-bit.

To fix this error, we can use other types that are not platform-dependent. The most common alternative is the np.int64 type, and we specify this in the array with the dtype parameter.

Code:

import numpy as np

arr = np.zeros((1, 2), dtype=np.int64)
a = [6580225610007]
arr[0] = a
print(arr)

Output:

[[6580225610007 6580225610007]]

The above example shows that the error is resolved using the alternative type.

Another way can be to use a try and except block. These two blocks are used to work around exceptions in Python.

The code in the except block is executed if an exception is thrown in the try block.

Code:

import numpy as np

arr = np.zeros((1, 2), dtype=int)
a = [6580225610007]
try:
    arr[0] = a
except:
    print("Error in code")

Output:

Error in code

The above example avoids the error using the try and except blocks. Remember that this is not a fix to the given error but a method to work around it.