[26660] compare int with double by subtracting during describe percen… #26768
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## master #26768 +/- ##
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- Coverage 91.71% 91.71% -0.01%
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Files 178 178
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- Hits 46552 46548 -4
- Misses 4203 4207 +4
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Codecov Report
@@ Coverage Diff @@
## master #26768 +/- ##
==========================================
- Coverage 91.71% 91.71% -0.01%
==========================================
Files 178 178
Lines 50771 50776 +5
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+ Hits 46567 46570 +3
- Misses 4204 4206 +2
Continue to review full report at Codecov.
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…mining if indexes are integers
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pandas/io/formats/format.py
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| @@ -1246,7 +1246,7 @@ def format_percentiles(percentiles): | |||
| raise ValueError("percentiles should all be in the interval [0,1]") | |||
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| percentiles = 100 * percentiles | |||
| int_idx = (percentiles.astype(int) == percentiles) | |||
| int_idx = (abs(percentiles.astype(int) - percentiles) < 1e-4) | |||
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What's the reasoning for using 1e-4? May make more sense just to leverage np.allclose
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Good insight - it makes code more readable. Instead of np.allclose, I've opted out for np.isclose, which returns array of booleans. Reasoning for that is that further down in the function that mask is used when determining how to format integers (where mask is True) and decimal numbers (where mask is False).
Another approach would be that if at least one percentile is decimal, all percentiles should be represented as decimals. This is currently not the case, for instance if percentiles to describe are: [0, 0.5, 0.334] the method will return ["0%", "50%", "33.4%"]. This approach would yield: ["0.0%", "50.0%", "33.4%"].
I wanted to change as little as possible, so I returned mask of booleans from this expression, but if it fits your business logic to change to second approach, I can modify the code to that behavior also.
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Nice. This is fine as is - really just wanted to circumvent hard coding thresholds like before. Thanks for reviewing in detail!
| @@ -2740,6 +2740,14 @@ def test_format_percentiles(): | |||
| fmt.format_percentiles([0.1, 0.5, 'a']) | |||
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can you add an additional test that uses .describe, like you did in the OP: #26660 (comment), add in pandas/tests/frame/test_analytics
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I've added the test you mentioned. Please comment if it is not sufficient.
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thanks @jkovacevic |
git diff upstream/master -u -- "*.py" | flake8 --diffI am not sure whether I had put my issue in whatsnew entry in appropriate place.