What are NumPy Warnings? How to Ignore Them?
NumPy warnings are used to alert the developer to circumstances that are not always exceptions. A NumPy warning usually happens when some programming element, such as a term, function, or class, has become outdated. In general, a warning is different from an error in a program. If an error occurs, the Python application quits instantly. A warning, on the other hand, is not necessary. It displays a notice, but the software continues to operate. We may use the numpy.seterr() method to silence all NumPy floating-point warnings.
It was a fairly straightforward solution to the problem. As most of you are aware, NumPy may raise a floating-point warning at any moment. While these warnings might be beneficial at times, most of us probably simply want to dodge seeing all those red warning blocks appear. Fortunately, there is a fantastic NumPy function that helps us to avoid these sorts of floating-point warnings! The numpy.seterr() method is exactly what we need. Let's look at the numpy.seterr() function in more detail now.
numpy.seterr() Function
So far, we've seen that the numpy.seterr() function can be used to resolve Runtime floating-point Warnings. Let us begin by understanding its syntax.
Syntax:
Let's have a look at all of the different parameters we can pass to numpy. seterr() function
Parameters:
| Srno. | Parameter Name | Parameter Description |
|---|---|---|
| 1 | all | It is an optional parameter used to suppress all forms of floating-point errors at the same time. |
| 2 | divide | It's an optional parameter that's used to suppress division by zero mistakes. |
| 3 | over | An optional input for handling floating-point overflow errors. |
| 4 | under | An optional input for dealing with floating-point underflow errors. |
| 5 | invalid | An optional input that is used to address incorrect floating-point operation failures. |
It should be noted that all of the parameters can only take one of the following values: 'ignore', 'warn', 'raise', 'call', 'print', and 'log'. As you can see, a parameter can accept a value from numerous values as input; let's look at what they all represent.
- ignore When an exception occurs, we use ignore to take no action.
- warn: The warn input to the argument is used to display a RuntimeWarning.
- raise: The raise input to the parameter is used to throw a FloatingPointError.
- call: call is used to invoke a function that has been specified using the seterrcall function.
- print: To print a warning straight to stdout, use print.
- log: Finally, a log is used to log errors into a Log object defined by seterrcall.
We will use "numpy.seterr(all='ignore')" to suppress all floating-point warnings. Let me illustrate this with an example.
Case 1
When we do not use numpy.seterr(all='ignore'), a warning appears.
Code:
Output:
Case 2
All warnings are suppressed in the scenario when "numpy.seterr(all='ignore')" is used.
Code:
Output:
Explanation:
In the last example, we first loaded our NumPy library, which is commonly used for array manipulation and mathematical computations. After that, we simply generated data. A warning was produced in Case 1 because we did not use the numpy.seterr() method, however, all warnings were ignored in Case 2 because we used the "numpy.seterr()" function with all arguments set to "ignore".
I hope you understand the basic concept of the numpy.seterr() method, which is used to eliminate all floating-point warnings in NumPy.
Conclusion
In this blog, we learned that
- A NumPy warning occurs when a programming element, such as a keyword, function, or class, has become outdated.
- To suppress all floating point warnings, we may use the numpy.seterr() method.