Fixing NumPy’s Warning: Casting Data Type from Float to Int

NumPy issues warnings when it automatically casts data types from float to integer, which can lead to data loss. This warning is intended to alert users of the non-intuitive and possibly unintended data type changes. See how to address these warnings and ensure that your data types are correct for your application needs.

See also  How to cast an array from one dtype to another using Numpy astype?

Understanding the Warning

Here is a common scenario where you might encounter this warning:

import numpy as np
np.array([1.7, 1.3, 1.9], dtype=np.int)

This code tries to create an integer array from a list of floats, which will trigger a warning because the decimal parts are truncated.

Strategies to Fix or Avoid the Warning

Explicit Conversion

If truncation is acceptable, explicitly convert floats to integers using methods that clearly define the behavior:

See also  How to generate distribution plot the easiest way in Python?

np.array([1.7, 1.3, 1.9], dtype=np.int).astype(np.int)

or

np.floor([1.7, 1.3, 1.9]).astype(np.int)

Using Correct Data Types

If your data should remain floats, ensure that you specify or maintain float data types:

np.array([1.7, 1.3, 1.9], dtype=np.float)

Suppressing the Warning

If you understand the implications and decide that the warning is not helpful, you can suppress it:

See also  How to resolve AttributeError: 'numpy.ndarray' object has no attribute 'function_name

import warnings
with warnings.catch_warnings():
   warnings.simplefilter("ignore")
   np.array([1.7, 1.3, 1.9], dtype=np.int)