Resolving the hist Function Mismatch: A Guide to Achieving Identical Results in MATLAB and Numpy

Discover how to align the results of MATLAB's `hist` function with that of Numpy's histogram through effective binning strategies. Learn the solution step-by-step!
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Resolving the hist Function Mismatch: A Guide to Achieving Identical Results in MATLAB and Numpy

When transitioning from MATLAB to Numpy, many data scientists and developers encounter challenges due to differences in library implementations. One commonly discussed issue involves the hist function in MATLAB compared to its counterpart in Numpy. The problem arises when you find that Numpy's histogram returns one less bin than MATLAB's histogram. Let's break down this issue and explore how to achieve identical results between the two languages.

Understanding the Problem

Consider the following example in MATLAB:

[[See Video to Reveal this Text or Code Snippet]]

This code will produce the following outputs:

[[See Video to Reveal this Text or Code Snippet]]

Now, let's see how the equivalent code looks in Numpy:

[[See Video to Reveal this Text or Code Snippet]]

The output from the Numpy code is:

[[See Video to Reveal this Text or Code Snippet]]

Notably, the results of the two languages' histograms do not match. Let's explore why that is and how we can fix it.

Why the Differences?

How Numpy Works

In Numpy, the bins created are determined by the edges of the intervals defined by np.unique(x). The way Numpy generates these bins is crucial for understanding the discrepancy:

First bin: [1, 2) → counts 2

Second bin: [2, 3) → counts 4

Third bin: [3, 4] → counts 6 (includes 4)

How MATLAB Works

Conversely, MATLAB's hist() function bins the data slightly differently, which leads to the additional bin it generates:

First bin: [1, 2) → counts 2

Second bin: [2, 3) → counts 4

Third bin: [3, 4) → counts 4 (does not include 4, next is added)

Fourth bin: [4, ∞) → counts 2

This difference in how bins are considered results in MATLAB returning one more value than Numpy.

Achieving Identical Results

To get results in Numpy that are identical to MATLAB's, you can utilize the histogram() function in MATLAB instead of hist(). This function provides more control over bin sizes and counts.

Example Using MATLAB's histogram()

Here's how you might use it:

[[See Video to Reveal this Text or Code Snippet]]

The output h.Values will yield identical representation comparable to Numpy's even with changing bin sizes.

Conclusion

By understanding how the two libraries create bins and how they manage intervals, you can effectively align the outputs of MATLAB and Numpy's histogram functions. Simply adjusting your approach in MATLAB and understanding Numpy's implicit behavior can solve this discrepancy.

For anyone transitioning from MATLAB to Numpy or needing to utilize both, knowledge of these differences is incredibly beneficial. I hope this helps you achieve the results you require!