How to ignore all numpy suppress warnings?

A simple guide showcasing how to ignore all numpy suppress warnings.

When working with NumPy, we are likely to encounter certain warnings, especially while working on datasets with NaN (Not-a-Number) values. These warnings, although informative, can sometimes be a source of frustration, especially when you want to focus on your data analysis or scientific computations without interruption. Fortunately, NumPy provides a way to handle these warnings with care, allowing you to take control of your code's behavior.

Step By Step Guide to Ignore/Suppress NumPy Warnings

In this short guide, we'll explore how to effectively ignore NumPy warnings, giving you the freedom to run your code smoothly and without unnecessary distractions. Let's dive into the details of how to suppress NumPy warnings and make your workflow more seamless.

Learn to Build a Neural network from Scratch using NumPy 

How to Ignore NumPy Warnings with Example?

Now, let's delve into an example where we will step by step take a look an example of NumPy disable warnings in Python.

Step 1 - Import the NumPy library

import numpy as np

Let's pause and look at this import. Numpy library is generally used for working over arrays and performing mathematical operations.

Step 2 - Setup the Data with NaN values

data = np.random.random(1000).reshape(10, 10,10) * np.nan

In this step, we have simply setup a random data using NumPy's random number generation capabilities. We generate a 10x10x10 array filled with NaN (Not-a-Number) values. NaN is often used to represent missing or undefined data. 

Step 3 - Setup Warning Controller using NumPy Library

np.seterr(all="ignore")

Seterr function comes handy for control of warnings. Here all='ignore' helps in ignoring any type of warnings we might encounter.  This is particularly useful when you want to focus on the results of your computations without being interrupted by warning messages.

Step 4 - Calling Warning Statement

np.nanmedian(data, axis=[1, 2])

Now, let's perform an operation that would typically trigger a warning as we calculate the median of the data array along specified axes. However, because of seterr function, it's being ignored. We will only see the output of the above code with no warning in the final run. 

Step 5 - Let's look at our dataset now

Once we run the above code snippet, we will see:

array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])

Here, you can see that the calculation proceeded smoothly, providing you with the desired output while silently ignoring any warnings that might have otherwise appeared.

Explore NumPy with ProjectPro!

In this short guide, you learned how to leverage NumPy's warning control mechanisms to ensure a seamless and uninterrupted workflow, particularly when dealing with operations on data containing NaN values or other potential sources of warnings.  However, this is just the beginning of your journey in data science. To unlock the full potential of NumPy and master the intricacies of data analysis, consider delving deeper into its functionalities. ProjectPro offers a comprehensive platform where you can explore an array of solved projects in data science and big data. These projects provide invaluable hands-on experience, enabling you to sharpen your skills and become proficient in the fascinating world of data science.

 

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