fix: issue#915, Error for large integers in Series #1233
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Sohaib90
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@alexbarros you might want to look into this as well if you get the chance :) |
Sohaib90
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| bins = np.histogram_bin_edges(finite_values, bins=bins_arg) | ||
| stats[name] = np.histogram(finite_values, bins=bins, weights=weights) |
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interesting solution, but still seems to behave a bit weirdly with big numbers
> import numpy as np
> arr = np.array([716277643516076032 + i for i in range(100)])
> bins = np.histogram_bin_edges(arr, bins=5)
> np.histogram(arr, bins=bins)
(array([ 0, 0, 65, 0, 35]),
array([7.16277644e+17, 7.16277644e+17, 7.16277644e+17, 7.16277644e+17,
7.16277644e+17, 7.16277644e+17]))There was a problem hiding this comment.
True, it is still not evenly distributed like it should be for smaller numbers. What do you propose here? Leaving np.histogram_bin_edges raises an error for larger numbers. Is it better to raise error than have weird behavior?
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Thinking from a user's perspective, for me is better to have an error being raised than an incorrect plot. If I know that there was a problem with the large integers I can preprocess that column and run again, but an incorrect result may lead me to an incorrect interpretation of my data distribution.
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Yeah, that is what I was thinking as well. I think I should make the changes so that it leads to raising an error rather than making an incorrect plot, right?
Also there is a check Codacy Static Code Analysis that is failing. I think that is a new one
Codecov ReportBase: 90.47% // Head: 90.49% // Increases project coverage by
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This patch fixes issue #915: Error for series with large integers.
The issue is caused when using the
numpy.histogramfunction with large integers which causes unevenly spaced bin edges.here:
https://github.com/ydataai/pandas-profiling/blob/5b1abac48ed9ed5a9e7e662be30c913acc3e7a5b/src/pandas_profiling/model/summary_algorithms.py#L39
and here:
https://github.com/ydataai/pandas-profiling/blob/5b1abac48ed9ed5a9e7e662be30c913acc3e7a5b/src/pandas_profiling/model/summary_algorithms.py#L52
This can cause the resulting histogram to be distorted or misleading, as the bin sizes may not be uniform.
To resolve this issue, I used the
numpy.histogram_bin_edgesfunction to compute the bin edges for the data before passing them to thenumpy.histogramfunction. This function allows to specify the number of bins and the range of the data, and will compute the bin edges in a way that ensures they are evenly spaced. This fix does not raise an error as reported in the bug report and successfully generates a report. I have also included atest_issue915.pyfor testing the generation of the report.