+25
-21
lines changedFilter options
+25
-21
lines changed Original file line number Diff line number Diff line change
@@ -274,7 +274,7 @@ def box_expected(expected, box_cls, transpose: bool = True):
274 274
else:
275 275
expected = pd.array(expected, copy=False)
276 276
elif box_cls is Index:
277 -
expected = Index._with_infer(expected)
277 +
expected = Index(expected)
278 278
elif box_cls is Series:
279 279
expected = Series(expected)
280 280
elif box_cls is DataFrame:
@@ -894,7 +894,7 @@ def value_counts(
894 894 895 895
# For backwards compatibility, we let Index do its normal type
896 896
# inference, _except_ for if if infers from object to bool.
897 -
idx = Index._with_infer(keys)
897 +
idx = Index(keys)
898 898
if idx.dtype == bool and keys.dtype == object:
899 899
idx = idx.astype(object)
900 900
@@ -2678,6 +2678,7 @@ def fillna(self, value=None, downcast=None):
2678 2678
if downcast is None:
2679 2679
# no need to care metadata other than name
2680 2680
# because it can't have freq if it has NaTs
2681 +
# _with_infer needed for test_fillna_categorical
2681 2682
return Index._with_infer(result, name=self.name)
2682 2683
raise NotImplementedError(
2683 2684
f"{type(self).__name__}.fillna does not support 'downcast' "
@@ -4230,10 +4231,10 @@ def _reindex_non_unique(
4230 4231
new_indexer = np.arange(len(self.take(indexer)), dtype=np.intp)
4231 4232
new_indexer[~check] = -1
4232 4233 4233 -
if isinstance(self, ABCMultiIndex):
4234 -
new_index = type(self).from_tuples(new_labels, names=self.names)
4234 +
if not isinstance(self, ABCMultiIndex):
4235 +
new_index = Index(new_labels, name=self.name)
4235 4236
else:
4236 -
new_index = Index._with_infer(new_labels, name=self.name)
4237 +
new_index = type(self).from_tuples(new_labels, names=self.names)
4237 4238
return new_index, indexer, new_indexer
4238 4239 4239 4240
# --------------------------------------------------------------------
@@ -6477,7 +6478,7 @@ def insert(self, loc: int, item) -> Index:
6477 6478
if self._typ == "numericindex":
6478 6479
# Use self._constructor instead of Index to retain NumericIndex GH#43921
6479 6480
# TODO(2.0) can use Index instead of self._constructor
6480 -
return self._constructor._with_infer(new_values, name=self.name)
6481 +
return self._constructor(new_values, name=self.name)
6481 6482
else:
6482 6483
return Index._with_infer(new_values, name=self.name)
6483 6484
@@ -6850,7 +6851,7 @@ def ensure_index_from_sequences(sequences, names=None) -> Index:
6850 6851
if len(sequences) == 1:
6851 6852
if names is not None:
6852 6853
names = names[0]
6853 -
return Index._with_infer(sequences[0], name=names)
6854 +
return Index(sequences[0], name=names)
6854 6855
else:
6855 6856
return MultiIndex.from_arrays(sequences, names=names)
6856 6857
@@ -6893,7 +6894,7 @@ def ensure_index(index_like: Axes, copy: bool = False) -> Index:
6893 6894 6894 6895
if isinstance(index_like, ABCSeries):
6895 6896
name = index_like.name
6896 -
return Index._with_infer(index_like, name=name, copy=copy)
6897 +
return Index(index_like, name=name, copy=copy)
6897 6898 6898 6899
if is_iterator(index_like):
6899 6900
index_like = list(index_like)
@@ -6909,9 +6910,9 @@ def ensure_index(index_like: Axes, copy: bool = False) -> Index:
6909 6910 6910 6911
return MultiIndex.from_arrays(index_like)
6911 6912
else:
6912 -
return Index._with_infer(index_like, copy=copy, tupleize_cols=False)
6913 +
return Index(index_like, copy=copy, tupleize_cols=False)
6913 6914
else:
6914 -
return Index._with_infer(index_like, copy=copy)
6915 +
return Index(index_like, copy=copy)
6915 6916 6916 6917 6917 6918
def ensure_has_len(seq):
@@ -2112,7 +2112,7 @@ def append(self, other):
2112 2112
# setting names to None automatically
2113 2113
return MultiIndex.from_tuples(new_tuples)
2114 2114
except (TypeError, IndexError):
2115 -
return Index._with_infer(new_tuples)
2115 +
return Index(new_tuples)
2116 2116 2117 2117
def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
2118 2118
if len(args) == 0 and len(kwargs) == 0:
@@ -319,7 +319,7 @@ def cons_row(x):
319 319
out = out.get_level_values(0)
320 320
return out
321 321
else:
322 -
return Index._with_infer(result, name=name)
322 +
return Index(result, name=name)
323 323
else:
324 324
index = self._orig.index
325 325
# This is a mess.
@@ -344,9 +344,7 @@ def _hash_ndarray(
344 344
)
345 345 346 346
codes, categories = factorize(vals, sort=False)
347 -
cat = Categorical(
348 -
codes, Index._with_infer(categories), ordered=False, fastpath=True
349 -
)
347 +
cat = Categorical(codes, Index(categories), ordered=False, fastpath=True)
350 348
return _hash_categorical(cat, encoding, hash_key)
351 349 352 350
try:
@@ -1147,6 +1147,9 @@ def test_numarr_with_dtype_add_nan(self, dtype, box_with_array):
1147 1147 1148 1148
ser = tm.box_expected(ser, box)
1149 1149
expected = tm.box_expected(expected, box)
1150 +
if box is Index and dtype is object:
1151 +
# TODO: avoid this; match behavior with Series
1152 +
expected = expected.astype(np.float64)
1150 1153 1151 1154
result = np.nan + ser
1152 1155
tm.assert_equal(result, expected)
@@ -1162,6 +1165,9 @@ def test_numarr_with_dtype_add_int(self, dtype, box_with_array):
1162 1165 1163 1166
ser = tm.box_expected(ser, box)
1164 1167
expected = tm.box_expected(expected, box)
1168 +
if box is Index and dtype is object:
1169 +
# TODO: avoid this; match behavior with Series
1170 +
expected = expected.astype(np.int64)
1165 1171 1166 1172
result = 1 + ser
1167 1173
tm.assert_equal(result, expected)
@@ -89,7 +89,7 @@ def test_astype_index(all_data, dropna):
89 89
other = all_data
90 90 91 91
dtype = all_data.dtype
92 -
idx = pd.Index._with_infer(np.array(other))
92 +
idx = pd.Index(np.array(other))
93 93
assert isinstance(idx, ABCIndex)
94 94 95 95
result = idx.astype(dtype)
@@ -33,7 +33,7 @@ def test_groupby_extension_agg(self, as_index, data_for_grouping):
33 33
_, uniques = pd.factorize(data_for_grouping, sort=True)
34 34 35 35
if as_index:
36 -
index = pd.Index._with_infer(uniques, name="B")
36 +
index = pd.Index(uniques, name="B")
37 37
expected = pd.Series([3.0, 1.0, 4.0], index=index, name="A")
38 38
self.assert_series_equal(result, expected)
39 39
else:
@@ -61,7 +61,7 @@ def test_groupby_extension_no_sort(self, data_for_grouping):
61 61
result = df.groupby("B", sort=False).A.mean()
62 62
_, index = pd.factorize(data_for_grouping, sort=False)
63 63 64 -
index = pd.Index._with_infer(index, name="B")
64 +
index = pd.Index(index, name="B")
65 65
expected = pd.Series([1.0, 3.0, 4.0], index=index, name="A")
66 66
self.assert_series_equal(result, expected)
67 67
@@ -391,7 +391,7 @@ def test_groupby_extension_agg(self, as_index, data_for_grouping):
391 391
_, uniques = pd.factorize(data_for_grouping, sort=True)
392 392 393 393
if as_index:
394 -
index = pd.Index._with_infer(uniques, name="B")
394 +
index = pd.Index(uniques, name="B")
395 395
expected = pd.Series([3.0, 1.0, 4.0], index=index, name="A")
396 396
self.assert_series_equal(result, expected)
397 397
else:
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