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Correct type inference for UInt64Index during access #29420

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Merged
merged 13 commits into from
Nov 27, 2019
Merged

Correct type inference for UInt64Index during access #29420

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606b71d
Adds test case and fix suggestion
oguzhanogreden Aug 31, 2019
7b0eef4
Uses skipna=False default for libs.lib.infer_dtype()
oguzhanogreden Nov 5, 2019
2b537c6
Reverts last commit due to deprecation warning - Uses skipna=False de…
oguzhanogreden Nov 5, 2019
82cdc80
Add test case for #28279
oguzhanogreden Nov 5, 2019
d1818dd
Passes black and flake8
oguzhanogreden Nov 5, 2019
3e370b2
Removes unnecessary test
oguzhanogreden Nov 6, 2019
1722116
Rename test
oguzhanogreden Nov 6, 2019
500e73c
Clarifies logic & tests explicitly
oguzhanogreden Nov 10, 2019
813ed20
Formatter...
oguzhanogreden Nov 10, 2019
78db1e8
Replay changes from #29529 & replay whatsnew in master
oguzhanogreden Nov 18, 2019
a5d20d9
Fixed test and added whatsnew entry
oguzhanogreden Nov 18, 2019
03b91b1
Merge branch 'master' into indexing-inference-pr
oguzhanogreden Nov 18, 2019
aac01ea
Addressed review
oguzhanogreden Nov 23, 2019
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17 changes: 10 additions & 7 deletions pandas/core/indexes/numeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@

import numpy as np

from pandas._libs import index as libindex
from pandas._libs import index as libindex, lib
from pandas.util._decorators import Appender, cache_readonly

from pandas.core.dtypes.cast import astype_nansafe
Expand Down Expand Up @@ -302,12 +302,15 @@ def _convert_scalar_indexer(self, key, kind=None):

@Appender(_index_shared_docs["_convert_arr_indexer"])
def _convert_arr_indexer(self, keyarr):
# Cast the indexer to uint64 if possible so
# that the values returned from indexing are
# also uint64.
keyarr = com.asarray_tuplesafe(keyarr)
if is_integer_dtype(keyarr):
return com.asarray_tuplesafe(keyarr, dtype=np.uint64)
# Cast the indexer to uint64 if possible so that the values returned
# from indexing are also uint64.
if is_integer_dtype(keyarr) or (
lib.infer_dtype(keyarr, skipna=False) == "integer"
):
keyarr = com.asarray_tuplesafe(keyarr, dtype=np.uint64)
else:
keyarr = com.asarray_tuplesafe(keyarr)

return keyarr

@Appender(_index_shared_docs["_convert_index_indexer"])
Expand Down
36 changes: 36 additions & 0 deletions pandas/tests/indexes/test_numeric.py
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Expand Up @@ -1187,3 +1187,39 @@ def test_range_float_union_dtype():

result = other.union(index)
tm.assert_index_equal(result, expected)


def test_uint64_keys_in_list():
# https://github.com/pandas-dev/pandas/issues/28023
bug = pd.Series(
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Could both these test functions be combined into 1 func? Seem very similar

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They are indeed similar. I'll wait for a few more comments to decide on this.

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yea pls do we prefer parametized tests as much as possible

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Revisiting the issues, I realized one of the tests would be redundant and removed that.

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Revisiting the issues, I realized one of the tests would be redundant and removed that.

How come? - it seems that test case was exactly covering issue #28023

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@alimcmaster1 alimcmaster1 Nov 6, 2019 •
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I would just use https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.testing.assert_index_equal.html

Hope that helps

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@oguzhanogreden oguzhanogreden Nov 7, 2019 •
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#28023 reports a KeyError. If https://github.com/pandas-dev/pandas/pull/29420/files#diff-7c97f2c17a9193409668a6f698905c73R1206 holds, KeyError won't be raised, I think

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About the test function... Now that I'm rooting for a single test, I'll leave it as isinstance since I think the intention of test is clearer this way.

[0, 1, 2, 3, 4],
index=[
7606741985629028552,
17876870360202815256,
13106359306506049338,
8991270399732411471,
8991270399732411471,
],
)

tm.assert_equal(
bug.loc[[7606741985629028552, 17876870360202815256]], bug.iloc[[0, 1]]
)


def test_uint_index_not_converted_to_float64():
# https://github.com/pandas-dev/pandas/issues/28279
bug = pd.Series(
[0, 1, 2, 3, 4],
index=[
7606741985629028552,
17876870360202815256,
13106359306506049338,
8991270399732411471,
8991270399732411472,
],
)

assert isinstance(
bug.loc[[7606741985629028552, 17876870360202815256]].index, UInt64Index
)
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Can you explicitly construct the expected index and use tm.assert_index_equal to verify they're the same:

result = s.loc[[7606741985629028552, 17876870360202815256]].index
expected = UInt64Index([7606741985629028552, 17876870360202815256])
tm.assert_index_equal(result, expected)

I'd rather not do a simple isinstance check here because it doesn't guard against potential precision loss with the values in the index, e.g. if someone makes a change where there's an intermediate coercion to Float64Index:

In [2]: idx = pd.UInt64Index([2**53, 2**53 + 1])

In [3]: idx
Out[3]: UInt64Index([9007199254740992, 9007199254740993], dtype='uint64')

In [4]: pd.UInt64Index(pd.Float64Index(idx))
Out[4]: UInt64Index([9007199254740992, 9007199254740992], dtype='uint64')