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Reproducible Example

import numpy as np import pandas as pd number = 102 example = pd.cut(x=pd.Series(range(100)), bins=pd.Series(range(number))) print(example)

Issue Description

The above code works as expected when number is <102, but fails when number >=102. This only occurs on a 32 bit machine. When this is run on a 64 bit machine, it does not fail.

The traceback when this fails is:
Traceback (most recent call last):

File "C:\Users\careyc\OneDrive - Harman Technology Ltd\Python\densitometer\cutbugtest.py", line 10, in
example = pd.cut(x=pd.Series(range(100)), bins=pd.Series(range(102)))

File "C:\Users\careyc\Anaconda3\lib\site-packages\pandas\core\reshape\tile.py", line 287, in cut
fac, bins = _bins_to_cuts(

File "C:\Users\careyc\Anaconda3\lib\site-packages\pandas\core\reshape\tile.py", line 450, in _bins_to_cuts
labels = Categorical(

File "C:\Users\careyc\Anaconda3\lib\site-packages\pandas\core\arrays\categorical.py", line 456, in init
codes = _get_codes_for_values(values, dtype.categories)

File "C:\Users\careyc\Anaconda3\lib\site-packages\pandas\core\arrays\categorical.py", line 2666, in _get_codes_for_values
return coerce_indexer_dtype(categories.get_indexer_for(values), categories)

File "C:\Users\careyc\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 5274, in get_indexer_for
if self._index_as_unique:

File "pandas_libs\properties.pyx", line 37, in pandas._libs.properties.CachedProperty.get

File "C:\Users\careyc\Anaconda3\lib\site-packages\pandas\core\indexes\interval.py", line 727, in _index_as_unique
return not self.is_overlapping and self._engine._na_count < 2

File "C:\Users\careyc\Anaconda3\lib\site-packages\pandas\core\indexes\interval.py", line 454, in is_overlapping
return self._engine.is_overlapping

File "pandas_libs\properties.pyx", line 37, in pandas._libs.properties.CachedProperty.get

File "C:\Users\careyc\Anaconda3\lib\site-packages\pandas\core\indexes\interval.py", line 325, in _engine
return IntervalTree(left, right, closed=self.closed)

File "pandas_libs\intervaltree.pxi", line 77, in pandas._libs.interval.IntervalTree.init

File "pandas_libs\intervaltree.pxi", line 1025, in pandas._libs.interval.Int64ClosedRightIntervalNode.init

File "pandas_libs\intervaltree.pxi", line 1069, in pandas._libs.interval.Int64ClosedRightIntervalNode.new_child_node

File "pandas_libs\intervaltree.pxi", line 196, in pandas._libs.interval.take

TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'

Expected Behavior

Would expect the output to be:
0 NaN
1 (0.0, 1.0]
2 (1.0, 2.0]
3 (2.0, 3.0]
4 (3.0, 4.0]
.....
95 (94.0, 95.0]
96 (95.0, 96.0]
97 (96.0, 97.0]
98 (97.0, 98.0]
99 (98.0, 99.0]
100 (99.0, 100.0]
Length: 101, dtype: category
Categories (101, interval[int64, right]): [(0, 1] < (1, 2] < (2, 3] < (3, 4] ... (96, 97] <
(97, 98] < (98, 99] < (99, 100] < (100, 101]]

Installed Versions