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Calling qcut with too many duplicates now gives an informative error #9030

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Calling qcut with too many duplicates now gives an informative error #9030

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Calling qcut with too many duplicates now gives an informative error …
edjoesu Dec 6, 2014
b176ac9
updated tests and docs
edjoesu Dec 7, 2014
0e17789
fixed small formatting mistake
edjoesu Dec 7, 2014
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.15.2.txt
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Expand Up @@ -164,7 +164,7 @@ Bug Fixes
- Bug in ``merge`` where ``how='left'`` and ``sort=False`` would not preserve left frame order (:issue:`7331`)
- Fix: The font size was only set on x axis if vertical or the y axis if horizontal. (:issue:`8765`)
- Fixed division by 0 when reading big csv files in python 3 (:issue:`8621`)

- Fixed an unclear error message in ``qcut`` when repeated values result in duplicate bin edges.
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pls move this to 0.16.0




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2 changes: 1 addition & 1 deletion pandas/tools/tests/test_tile.py
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Expand Up @@ -153,7 +153,7 @@ def test_qcut_specify_quantiles(self):
self.assertTrue(factor.equals(expected))

def test_qcut_all_bins_same(self):
assertRaisesRegexp(ValueError, "edges.*unique", qcut, [0,0,0,0,0,0,0,0,0,0], 3)
assertRaisesRegexp(ValueError, "quantiles.*repeated", qcut, [0,0,0,0,0,0,0,0,0,0], 3)

def test_cut_out_of_bounds(self):
arr = np.random.randn(100)
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4 changes: 4 additions & 0 deletions pandas/tools/tile.py
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Expand Up @@ -165,6 +165,10 @@ def qcut(x, q, labels=None, retbins=False, precision=3):
else:
quantiles = q
bins = algos.quantile(x, quantiles)
if len(algos.unique(bins)) < len(bins):
bins_sorted = np.sort(bins, axis=None)
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you can do this as a set operation (set(bins)-set(unique(bins)) once you know that you have too many bins

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That doesn't work. Say bins = [0,0,0,1,1,1]. We want [0,1]. (set(bins)-set(unique(bins)) gives set([]).

Something like it should, but I can't think of it at the moment.

bins_dup = algos.unique(bins_sorted[bins_sorted[1:] == bins_sorted[:-1]])
raise ValueError('One or more quantiles consists entirely of a repeated value: %s' % repr(bins_dup))
return _bins_to_cuts(x, bins, labels=labels, retbins=retbins,precision=precision,
include_lowest=True)

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