NumPy: 4 ways to disable future/depreciated warnings

When working with NumPy, it’s common to encounter ‘FutureWarning’ or ‘DeprecationWarning’ notifications. These warnings are designed to inform users about changes in the future versions of NumPy that might affect the current codebase. While these warnings are useful for developers to future-proof their code, there may be scenarios where you would want to temporarily suppress these warnings during development or production. This tutorial will guide you through several methods to disable future and depreciation warnings in NumPy, complete with code examples.

Understanding NumPy Warnings

Before diving into how to suppress these warnings, let’s briefly understand what they signify:

• FutureWarning: Indicates that a particular feature or aspect of NumPy is going to change in a future version, which might affect your code if it relies on the current behavior.
• DeprecationWarning: Alerts you that a specific feature or function is deprecated and might be removed in future releases.

Method 1: Using the warnings Module

Python’s built-in warnings module is the most straightforward way to suppress warnings. You can target just the NumPy warnings or all warnings in your session.

import numpy as np
import warnings

# Suppress all future warnings
warnings.filterwarnings('ignore', category=FutureWarning)

# Suppress DeprecationWarnings
warnings.filterwarnings('ignore', category=DeprecationWarning)

Method 2: Setting a Global Filter

If you wish to suppress all warnings universally, including those not generated by NumPy, you can use a more global approach.

import warnings
warnings.filterwarnings('ignore')

Method 3: Using contextlib for Temporary Suppression

Sometimes, you may want to suppress warnings only temporarily, particularly when running specific blocks of code. The contextlib module can be helpful in these scenarios to create a context where warnings are suppressed.

from contextlib import suppress
import warnings

with suppress(warnings.WarningMessage):
    # Code block where you want to suppress the warnings
    pass

Method 4: NumPy-specific Warning Controls

In addition to the above, NumPy offers its method for controlling warnings through its seterr function. This can be particularly useful for managing warnings related to floating point errors.

import numpy as np

# Ignore all NumPy warnings (Useful for floating point warnings)
np.seterr(all='ignore')

Conclusion

Managing warnings efficiently can make your development process smoother and your code cleaner. Whether you choose to suppress warnings globally or locally, remember that these warnings are there for a reason. They serve as a gentle reminder that parts of your code may need updating in the near future. Hence, while it’s okay to suppress them temporarily, always plan to review and address these warnings appropriately. Through this tutorial, you’ve learned four methods to disable future and depreciation warnings in NumPy, enabling you to focus on what’s important – writing effective and efficient code.