893 lines
30 KiB
Python
893 lines
30 KiB
Python
import gc
|
|
from typing import Optional, Type
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas._libs.tslib import iNaT
|
|
|
|
from pandas.core.dtypes.dtypes import CategoricalDtype
|
|
|
|
import pandas as pd
|
|
from pandas import (
|
|
CategoricalIndex,
|
|
DatetimeIndex,
|
|
Index,
|
|
Int64Index,
|
|
IntervalIndex,
|
|
MultiIndex,
|
|
PeriodIndex,
|
|
RangeIndex,
|
|
Series,
|
|
TimedeltaIndex,
|
|
UInt64Index,
|
|
isna,
|
|
)
|
|
import pandas._testing as tm
|
|
from pandas.core.indexes.base import InvalidIndexError
|
|
from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
|
|
|
|
|
|
class Base:
|
|
""" base class for index sub-class tests """
|
|
|
|
_holder: Optional[Type[Index]] = None
|
|
_compat_props = ["shape", "ndim", "size", "nbytes"]
|
|
|
|
def test_pickle_compat_construction(self):
|
|
# need an object to create with
|
|
msg = (
|
|
r"Index\(\.\.\.\) must be called with a collection of some"
|
|
r" kind, None was passed|"
|
|
r"__new__\(\) missing 1 required positional argument: 'data'|"
|
|
r"__new__\(\) takes at least 2 arguments \(1 given\)"
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
self._holder()
|
|
|
|
def test_to_series(self):
|
|
# assert that we are creating a copy of the index
|
|
|
|
idx = self.create_index()
|
|
s = idx.to_series()
|
|
assert s.values is not idx.values
|
|
assert s.index is not idx
|
|
assert s.name == idx.name
|
|
|
|
def test_to_series_with_arguments(self):
|
|
# GH18699
|
|
|
|
# index kwarg
|
|
idx = self.create_index()
|
|
s = idx.to_series(index=idx)
|
|
|
|
assert s.values is not idx.values
|
|
assert s.index is idx
|
|
assert s.name == idx.name
|
|
|
|
# name kwarg
|
|
idx = self.create_index()
|
|
s = idx.to_series(name="__test")
|
|
|
|
assert s.values is not idx.values
|
|
assert s.index is not idx
|
|
assert s.name != idx.name
|
|
|
|
@pytest.mark.parametrize("name", [None, "new_name"])
|
|
def test_to_frame(self, name):
|
|
# see GH-15230, GH-22580
|
|
idx = self.create_index()
|
|
|
|
if name:
|
|
idx_name = name
|
|
else:
|
|
idx_name = idx.name or 0
|
|
|
|
df = idx.to_frame(name=idx_name)
|
|
|
|
assert df.index is idx
|
|
assert len(df.columns) == 1
|
|
assert df.columns[0] == idx_name
|
|
assert df[idx_name].values is not idx.values
|
|
|
|
df = idx.to_frame(index=False, name=idx_name)
|
|
assert df.index is not idx
|
|
|
|
def test_shift(self):
|
|
|
|
# GH8083 test the base class for shift
|
|
idx = self.create_index()
|
|
msg = "Not supported for type {}".format(type(idx).__name__)
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
idx.shift(1)
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
idx.shift(1, 2)
|
|
|
|
def test_constructor_name_unhashable(self):
|
|
# GH#29069 check that name is hashable
|
|
# See also same-named test in tests.series.test_constructors
|
|
idx = self.create_index()
|
|
with pytest.raises(TypeError, match="Index.name must be a hashable type"):
|
|
type(idx)(idx, name=[])
|
|
|
|
def test_create_index_existing_name(self):
|
|
|
|
# GH11193, when an existing index is passed, and a new name is not
|
|
# specified, the new index should inherit the previous object name
|
|
expected = self.create_index()
|
|
if not isinstance(expected, MultiIndex):
|
|
expected.name = "foo"
|
|
result = pd.Index(expected)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
result = pd.Index(expected, name="bar")
|
|
expected.name = "bar"
|
|
tm.assert_index_equal(result, expected)
|
|
else:
|
|
expected.names = ["foo", "bar"]
|
|
result = pd.Index(expected)
|
|
tm.assert_index_equal(
|
|
result,
|
|
Index(
|
|
Index(
|
|
[
|
|
("foo", "one"),
|
|
("foo", "two"),
|
|
("bar", "one"),
|
|
("baz", "two"),
|
|
("qux", "one"),
|
|
("qux", "two"),
|
|
],
|
|
dtype="object",
|
|
),
|
|
names=["foo", "bar"],
|
|
),
|
|
)
|
|
|
|
result = pd.Index(expected, names=["A", "B"])
|
|
tm.assert_index_equal(
|
|
result,
|
|
Index(
|
|
Index(
|
|
[
|
|
("foo", "one"),
|
|
("foo", "two"),
|
|
("bar", "one"),
|
|
("baz", "two"),
|
|
("qux", "one"),
|
|
("qux", "two"),
|
|
],
|
|
dtype="object",
|
|
),
|
|
names=["A", "B"],
|
|
),
|
|
)
|
|
|
|
def test_numeric_compat(self):
|
|
|
|
idx = self.create_index()
|
|
with pytest.raises(TypeError, match="cannot perform __mul__"):
|
|
idx * 1
|
|
with pytest.raises(TypeError, match="cannot perform __rmul__"):
|
|
1 * idx
|
|
|
|
div_err = "cannot perform __truediv__"
|
|
with pytest.raises(TypeError, match=div_err):
|
|
idx / 1
|
|
|
|
div_err = div_err.replace(" __", " __r")
|
|
with pytest.raises(TypeError, match=div_err):
|
|
1 / idx
|
|
with pytest.raises(TypeError, match="cannot perform __floordiv__"):
|
|
idx // 1
|
|
with pytest.raises(TypeError, match="cannot perform __rfloordiv__"):
|
|
1 // idx
|
|
|
|
def test_logical_compat(self):
|
|
idx = self.create_index()
|
|
with pytest.raises(TypeError, match="cannot perform all"):
|
|
idx.all()
|
|
with pytest.raises(TypeError, match="cannot perform any"):
|
|
idx.any()
|
|
|
|
def test_boolean_context_compat(self):
|
|
|
|
# boolean context compat
|
|
idx = self.create_index()
|
|
|
|
with pytest.raises(ValueError, match="The truth value of a"):
|
|
if idx:
|
|
pass
|
|
|
|
def test_reindex_base(self):
|
|
idx = self.create_index()
|
|
expected = np.arange(idx.size, dtype=np.intp)
|
|
|
|
actual = idx.get_indexer(idx)
|
|
tm.assert_numpy_array_equal(expected, actual)
|
|
|
|
with pytest.raises(ValueError, match="Invalid fill method"):
|
|
idx.get_indexer(idx, method="invalid")
|
|
|
|
def test_get_indexer_consistency(self, indices):
|
|
# See GH 16819
|
|
if isinstance(indices, IntervalIndex):
|
|
return
|
|
|
|
if indices.is_unique or isinstance(indices, CategoricalIndex):
|
|
indexer = indices.get_indexer(indices[0:2])
|
|
assert isinstance(indexer, np.ndarray)
|
|
assert indexer.dtype == np.intp
|
|
else:
|
|
e = "Reindexing only valid with uniquely valued Index objects"
|
|
with pytest.raises(InvalidIndexError, match=e):
|
|
indices.get_indexer(indices[0:2])
|
|
|
|
indexer, _ = indices.get_indexer_non_unique(indices[0:2])
|
|
assert isinstance(indexer, np.ndarray)
|
|
assert indexer.dtype == np.intp
|
|
|
|
def test_ndarray_compat_properties(self):
|
|
idx = self.create_index()
|
|
assert idx.T.equals(idx)
|
|
assert idx.transpose().equals(idx)
|
|
|
|
values = idx.values
|
|
for prop in self._compat_props:
|
|
assert getattr(idx, prop) == getattr(values, prop)
|
|
|
|
# test for validity
|
|
idx.nbytes
|
|
idx.values.nbytes
|
|
|
|
def test_repr_roundtrip(self):
|
|
|
|
idx = self.create_index()
|
|
tm.assert_index_equal(eval(repr(idx)), idx)
|
|
|
|
def test_str(self):
|
|
|
|
# test the string repr
|
|
idx = self.create_index()
|
|
idx.name = "foo"
|
|
assert "'foo'" in str(idx)
|
|
assert type(idx).__name__ in str(idx)
|
|
|
|
def test_repr_max_seq_item_setting(self):
|
|
# GH10182
|
|
idx = self.create_index()
|
|
idx = idx.repeat(50)
|
|
with pd.option_context("display.max_seq_items", None):
|
|
repr(idx)
|
|
assert "..." not in str(idx)
|
|
|
|
def test_copy_name(self, indices):
|
|
# gh-12309: Check that the "name" argument
|
|
# passed at initialization is honored.
|
|
if isinstance(indices, MultiIndex):
|
|
return
|
|
|
|
first = type(indices)(indices, copy=True, name="mario")
|
|
second = type(first)(first, copy=False)
|
|
|
|
# Even though "copy=False", we want a new object.
|
|
assert first is not second
|
|
|
|
# Not using tm.assert_index_equal() since names differ.
|
|
assert indices.equals(first)
|
|
|
|
assert first.name == "mario"
|
|
assert second.name == "mario"
|
|
|
|
s1 = Series(2, index=first)
|
|
s2 = Series(3, index=second[:-1])
|
|
|
|
if not isinstance(indices, CategoricalIndex):
|
|
# See gh-13365
|
|
s3 = s1 * s2
|
|
assert s3.index.name == "mario"
|
|
|
|
def test_ensure_copied_data(self, indices):
|
|
# Check the "copy" argument of each Index.__new__ is honoured
|
|
# GH12309
|
|
init_kwargs = {}
|
|
if isinstance(indices, PeriodIndex):
|
|
# Needs "freq" specification:
|
|
init_kwargs["freq"] = indices.freq
|
|
elif isinstance(indices, (RangeIndex, MultiIndex, CategoricalIndex)):
|
|
# RangeIndex cannot be initialized from data
|
|
# MultiIndex and CategoricalIndex are tested separately
|
|
return
|
|
|
|
index_type = type(indices)
|
|
result = index_type(indices.values, copy=True, **init_kwargs)
|
|
tm.assert_index_equal(indices, result)
|
|
tm.assert_numpy_array_equal(
|
|
indices._ndarray_values, result._ndarray_values, check_same="copy"
|
|
)
|
|
|
|
if isinstance(indices, PeriodIndex):
|
|
# .values an object array of Period, thus copied
|
|
result = index_type(ordinal=indices.asi8, copy=False, **init_kwargs)
|
|
tm.assert_numpy_array_equal(
|
|
indices._ndarray_values, result._ndarray_values, check_same="same"
|
|
)
|
|
elif isinstance(indices, IntervalIndex):
|
|
# checked in test_interval.py
|
|
pass
|
|
else:
|
|
result = index_type(indices.values, copy=False, **init_kwargs)
|
|
tm.assert_numpy_array_equal(
|
|
indices.values, result.values, check_same="same"
|
|
)
|
|
tm.assert_numpy_array_equal(
|
|
indices._ndarray_values, result._ndarray_values, check_same="same"
|
|
)
|
|
|
|
def test_memory_usage(self, indices):
|
|
indices._engine.clear_mapping()
|
|
result = indices.memory_usage()
|
|
if indices.empty:
|
|
# we report 0 for no-length
|
|
assert result == 0
|
|
return
|
|
|
|
# non-zero length
|
|
indices.get_loc(indices[0])
|
|
result2 = indices.memory_usage()
|
|
result3 = indices.memory_usage(deep=True)
|
|
|
|
# RangeIndex, IntervalIndex
|
|
# don't have engines
|
|
if not isinstance(indices, (RangeIndex, IntervalIndex)):
|
|
assert result2 > result
|
|
|
|
if indices.inferred_type == "object":
|
|
assert result3 > result2
|
|
|
|
def test_argsort(self, request, indices):
|
|
# separately tested
|
|
if isinstance(indices, CategoricalIndex):
|
|
return
|
|
|
|
result = indices.argsort()
|
|
expected = np.array(indices).argsort()
|
|
tm.assert_numpy_array_equal(result, expected, check_dtype=False)
|
|
|
|
def test_numpy_argsort(self, indices):
|
|
result = np.argsort(indices)
|
|
expected = indices.argsort()
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
# these are the only two types that perform
|
|
# pandas compatibility input validation - the
|
|
# rest already perform separate (or no) such
|
|
# validation via their 'values' attribute as
|
|
# defined in pandas.core.indexes/base.py - they
|
|
# cannot be changed at the moment due to
|
|
# backwards compatibility concerns
|
|
if isinstance(type(indices), (CategoricalIndex, RangeIndex)):
|
|
msg = "the 'axis' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.argsort(indices, axis=1)
|
|
|
|
msg = "the 'kind' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.argsort(indices, kind="mergesort")
|
|
|
|
msg = "the 'order' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.argsort(indices, order=("a", "b"))
|
|
|
|
def test_take(self, indices):
|
|
indexer = [4, 3, 0, 2]
|
|
if len(indices) < 5:
|
|
# not enough elements; ignore
|
|
return
|
|
|
|
result = indices.take(indexer)
|
|
expected = indices[indexer]
|
|
assert result.equals(expected)
|
|
|
|
if not isinstance(indices, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
|
|
# GH 10791
|
|
with pytest.raises(AttributeError):
|
|
indices.freq
|
|
|
|
def test_take_invalid_kwargs(self):
|
|
idx = self.create_index()
|
|
indices = [1, 2]
|
|
|
|
msg = r"take\(\) got an unexpected keyword argument 'foo'"
|
|
with pytest.raises(TypeError, match=msg):
|
|
idx.take(indices, foo=2)
|
|
|
|
msg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
idx.take(indices, out=indices)
|
|
|
|
msg = "the 'mode' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
idx.take(indices, mode="clip")
|
|
|
|
def test_repeat(self):
|
|
rep = 2
|
|
i = self.create_index()
|
|
expected = pd.Index(i.values.repeat(rep), name=i.name)
|
|
tm.assert_index_equal(i.repeat(rep), expected)
|
|
|
|
i = self.create_index()
|
|
rep = np.arange(len(i))
|
|
expected = pd.Index(i.values.repeat(rep), name=i.name)
|
|
tm.assert_index_equal(i.repeat(rep), expected)
|
|
|
|
def test_numpy_repeat(self):
|
|
rep = 2
|
|
i = self.create_index()
|
|
expected = i.repeat(rep)
|
|
tm.assert_index_equal(np.repeat(i, rep), expected)
|
|
|
|
msg = "the 'axis' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.repeat(i, rep, axis=0)
|
|
|
|
@pytest.mark.parametrize("klass", [list, tuple, np.array, Series])
|
|
def test_where(self, klass):
|
|
i = self.create_index()
|
|
|
|
cond = [True] * len(i)
|
|
result = i.where(klass(cond))
|
|
expected = i
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
cond = [False] + [True] * len(i[1:])
|
|
expected = pd.Index([i._na_value] + i[1:].tolist(), dtype=i.dtype)
|
|
result = i.where(klass(cond))
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("case", [0.5, "xxx"])
|
|
@pytest.mark.parametrize(
|
|
"method", ["intersection", "union", "difference", "symmetric_difference"]
|
|
)
|
|
def test_set_ops_error_cases(self, case, method, indices):
|
|
# non-iterable input
|
|
msg = "Input must be Index or array-like"
|
|
with pytest.raises(TypeError, match=msg):
|
|
getattr(indices, method)(case)
|
|
|
|
def test_intersection_base(self, indices):
|
|
if isinstance(indices, CategoricalIndex):
|
|
return
|
|
|
|
first = indices[:5]
|
|
second = indices[:3]
|
|
intersect = first.intersection(second)
|
|
assert tm.equalContents(intersect, second)
|
|
|
|
# GH 10149
|
|
cases = [klass(second.values) for klass in [np.array, Series, list]]
|
|
for case in cases:
|
|
result = first.intersection(case)
|
|
assert tm.equalContents(result, second)
|
|
|
|
if isinstance(indices, MultiIndex):
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
|
with pytest.raises(TypeError, match=msg):
|
|
first.intersection([1, 2, 3])
|
|
|
|
def test_union_base(self, indices):
|
|
first = indices[3:]
|
|
second = indices[:5]
|
|
everything = indices
|
|
union = first.union(second)
|
|
assert tm.equalContents(union, everything)
|
|
|
|
# GH 10149
|
|
cases = [klass(second.values) for klass in [np.array, Series, list]]
|
|
for case in cases:
|
|
if not isinstance(indices, CategoricalIndex):
|
|
result = first.union(case)
|
|
assert tm.equalContents(result, everything)
|
|
|
|
if isinstance(indices, MultiIndex):
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
|
with pytest.raises(TypeError, match=msg):
|
|
first.union([1, 2, 3])
|
|
|
|
@pytest.mark.parametrize("sort", [None, False])
|
|
def test_difference_base(self, sort, indices):
|
|
if isinstance(indices, CategoricalIndex):
|
|
return
|
|
|
|
first = indices[2:]
|
|
second = indices[:4]
|
|
answer = indices[4:]
|
|
result = first.difference(second, sort)
|
|
assert tm.equalContents(result, answer)
|
|
|
|
# GH 10149
|
|
cases = [klass(second.values) for klass in [np.array, Series, list]]
|
|
for case in cases:
|
|
if isinstance(indices, (DatetimeIndex, TimedeltaIndex)):
|
|
assert type(result) == type(answer)
|
|
tm.assert_numpy_array_equal(
|
|
result.sort_values().asi8, answer.sort_values().asi8
|
|
)
|
|
else:
|
|
result = first.difference(case, sort)
|
|
assert tm.equalContents(result, answer)
|
|
|
|
if isinstance(indices, MultiIndex):
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
|
with pytest.raises(TypeError, match=msg):
|
|
first.difference([1, 2, 3], sort)
|
|
|
|
def test_symmetric_difference(self, indices):
|
|
if isinstance(indices, CategoricalIndex):
|
|
return
|
|
|
|
first = indices[1:]
|
|
second = indices[:-1]
|
|
answer = indices[[0, -1]]
|
|
result = first.symmetric_difference(second)
|
|
assert tm.equalContents(result, answer)
|
|
|
|
# GH 10149
|
|
cases = [klass(second.values) for klass in [np.array, Series, list]]
|
|
for case in cases:
|
|
result = first.symmetric_difference(case)
|
|
assert tm.equalContents(result, answer)
|
|
|
|
if isinstance(indices, MultiIndex):
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
|
with pytest.raises(TypeError, match=msg):
|
|
first.symmetric_difference([1, 2, 3])
|
|
|
|
def test_insert_base(self, indices):
|
|
result = indices[1:4]
|
|
|
|
if not len(indices):
|
|
return
|
|
|
|
# test 0th element
|
|
assert indices[0:4].equals(result.insert(0, indices[0]))
|
|
|
|
def test_delete_base(self, indices):
|
|
if not len(indices):
|
|
return
|
|
|
|
if isinstance(indices, RangeIndex):
|
|
# tested in class
|
|
return
|
|
|
|
expected = indices[1:]
|
|
result = indices.delete(0)
|
|
assert result.equals(expected)
|
|
assert result.name == expected.name
|
|
|
|
expected = indices[:-1]
|
|
result = indices.delete(-1)
|
|
assert result.equals(expected)
|
|
assert result.name == expected.name
|
|
|
|
with pytest.raises((IndexError, ValueError)):
|
|
# either depending on numpy version
|
|
indices.delete(len(indices))
|
|
|
|
def test_equals(self, indices):
|
|
if isinstance(indices, IntervalIndex):
|
|
# IntervalIndex tested separately
|
|
return
|
|
|
|
assert indices.equals(indices)
|
|
assert indices.equals(indices.copy())
|
|
assert indices.equals(indices.astype(object))
|
|
|
|
assert not indices.equals(list(indices))
|
|
assert not indices.equals(np.array(indices))
|
|
|
|
# Cannot pass in non-int64 dtype to RangeIndex
|
|
if not isinstance(indices, RangeIndex):
|
|
same_values = Index(indices, dtype=object)
|
|
assert indices.equals(same_values)
|
|
assert same_values.equals(indices)
|
|
|
|
if indices.nlevels == 1:
|
|
# do not test MultiIndex
|
|
assert not indices.equals(Series(indices))
|
|
|
|
def test_equals_op(self):
|
|
# GH9947, GH10637
|
|
index_a = self.create_index()
|
|
if isinstance(index_a, PeriodIndex):
|
|
pytest.skip("Skip check for PeriodIndex")
|
|
|
|
n = len(index_a)
|
|
index_b = index_a[0:-1]
|
|
index_c = index_a[0:-1].append(index_a[-2:-1])
|
|
index_d = index_a[0:1]
|
|
|
|
msg = "Lengths must match|could not be broadcast"
|
|
with pytest.raises(ValueError, match=msg):
|
|
index_a == index_b
|
|
expected1 = np.array([True] * n)
|
|
expected2 = np.array([True] * (n - 1) + [False])
|
|
tm.assert_numpy_array_equal(index_a == index_a, expected1)
|
|
tm.assert_numpy_array_equal(index_a == index_c, expected2)
|
|
|
|
# test comparisons with numpy arrays
|
|
array_a = np.array(index_a)
|
|
array_b = np.array(index_a[0:-1])
|
|
array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
|
|
array_d = np.array(index_a[0:1])
|
|
with pytest.raises(ValueError, match=msg):
|
|
index_a == array_b
|
|
tm.assert_numpy_array_equal(index_a == array_a, expected1)
|
|
tm.assert_numpy_array_equal(index_a == array_c, expected2)
|
|
|
|
# test comparisons with Series
|
|
series_a = Series(array_a)
|
|
series_b = Series(array_b)
|
|
series_c = Series(array_c)
|
|
series_d = Series(array_d)
|
|
with pytest.raises(ValueError, match=msg):
|
|
index_a == series_b
|
|
|
|
tm.assert_numpy_array_equal(index_a == series_a, expected1)
|
|
tm.assert_numpy_array_equal(index_a == series_c, expected2)
|
|
|
|
# cases where length is 1 for one of them
|
|
with pytest.raises(ValueError, match="Lengths must match"):
|
|
index_a == index_d
|
|
with pytest.raises(ValueError, match="Lengths must match"):
|
|
index_a == series_d
|
|
with pytest.raises(ValueError, match="Lengths must match"):
|
|
index_a == array_d
|
|
msg = "Can only compare identically-labeled Series objects"
|
|
with pytest.raises(ValueError, match=msg):
|
|
series_a == series_d
|
|
with pytest.raises(ValueError, match="Lengths must match"):
|
|
series_a == array_d
|
|
|
|
# comparing with a scalar should broadcast; note that we are excluding
|
|
# MultiIndex because in this case each item in the index is a tuple of
|
|
# length 2, and therefore is considered an array of length 2 in the
|
|
# comparison instead of a scalar
|
|
if not isinstance(index_a, MultiIndex):
|
|
expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
|
|
# assuming the 2nd to last item is unique in the data
|
|
item = index_a[-2]
|
|
tm.assert_numpy_array_equal(index_a == item, expected3)
|
|
tm.assert_series_equal(series_a == item, Series(expected3))
|
|
|
|
def test_hasnans_isnans(self, indices):
|
|
# GH 11343, added tests for hasnans / isnans
|
|
if isinstance(indices, MultiIndex):
|
|
return
|
|
|
|
# cases in indices doesn't include NaN
|
|
idx = indices.copy(deep=True)
|
|
expected = np.array([False] * len(idx), dtype=bool)
|
|
tm.assert_numpy_array_equal(idx._isnan, expected)
|
|
assert idx.hasnans is False
|
|
|
|
idx = indices.copy(deep=True)
|
|
values = np.asarray(idx.values)
|
|
|
|
if len(indices) == 0:
|
|
return
|
|
elif isinstance(indices, DatetimeIndexOpsMixin):
|
|
values[1] = iNaT
|
|
elif isinstance(indices, (Int64Index, UInt64Index)):
|
|
return
|
|
else:
|
|
values[1] = np.nan
|
|
|
|
if isinstance(indices, PeriodIndex):
|
|
idx = type(indices)(values, freq=indices.freq)
|
|
else:
|
|
idx = type(indices)(values)
|
|
|
|
expected = np.array([False] * len(idx), dtype=bool)
|
|
expected[1] = True
|
|
tm.assert_numpy_array_equal(idx._isnan, expected)
|
|
assert idx.hasnans is True
|
|
|
|
def test_fillna(self, indices):
|
|
# GH 11343
|
|
if len(indices) == 0:
|
|
pass
|
|
elif isinstance(indices, MultiIndex):
|
|
idx = indices.copy(deep=True)
|
|
msg = "isna is not defined for MultiIndex"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
idx.fillna(idx[0])
|
|
else:
|
|
idx = indices.copy(deep=True)
|
|
result = idx.fillna(idx[0])
|
|
tm.assert_index_equal(result, idx)
|
|
assert result is not idx
|
|
|
|
msg = "'value' must be a scalar, passed: "
|
|
with pytest.raises(TypeError, match=msg):
|
|
idx.fillna([idx[0]])
|
|
|
|
idx = indices.copy(deep=True)
|
|
values = np.asarray(idx.values)
|
|
|
|
if isinstance(indices, DatetimeIndexOpsMixin):
|
|
values[1] = iNaT
|
|
elif isinstance(indices, (Int64Index, UInt64Index)):
|
|
return
|
|
else:
|
|
values[1] = np.nan
|
|
|
|
if isinstance(indices, PeriodIndex):
|
|
idx = type(indices)(values, freq=indices.freq)
|
|
else:
|
|
idx = type(indices)(values)
|
|
|
|
expected = np.array([False] * len(idx), dtype=bool)
|
|
expected[1] = True
|
|
tm.assert_numpy_array_equal(idx._isnan, expected)
|
|
assert idx.hasnans is True
|
|
|
|
def test_nulls(self, indices):
|
|
# this is really a smoke test for the methods
|
|
# as these are adequately tested for function elsewhere
|
|
if len(indices) == 0:
|
|
tm.assert_numpy_array_equal(indices.isna(), np.array([], dtype=bool))
|
|
elif isinstance(indices, MultiIndex):
|
|
idx = indices.copy()
|
|
msg = "isna is not defined for MultiIndex"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
idx.isna()
|
|
elif not indices.hasnans:
|
|
tm.assert_numpy_array_equal(
|
|
indices.isna(), np.zeros(len(indices), dtype=bool)
|
|
)
|
|
tm.assert_numpy_array_equal(
|
|
indices.notna(), np.ones(len(indices), dtype=bool)
|
|
)
|
|
else:
|
|
result = isna(indices)
|
|
tm.assert_numpy_array_equal(indices.isna(), result)
|
|
tm.assert_numpy_array_equal(indices.notna(), ~result)
|
|
|
|
def test_empty(self):
|
|
# GH 15270
|
|
index = self.create_index()
|
|
assert not index.empty
|
|
assert index[:0].empty
|
|
|
|
def test_join_self_unique(self, join_type):
|
|
index = self.create_index()
|
|
if index.is_unique:
|
|
joined = index.join(index, how=join_type)
|
|
assert (index == joined).all()
|
|
|
|
def test_map(self):
|
|
# callable
|
|
index = self.create_index()
|
|
|
|
# we don't infer UInt64
|
|
if isinstance(index, pd.UInt64Index):
|
|
expected = index.astype("int64")
|
|
else:
|
|
expected = index
|
|
|
|
result = index.map(lambda x: x)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"mapper",
|
|
[
|
|
lambda values, index: {i: e for e, i in zip(values, index)},
|
|
lambda values, index: pd.Series(values, index),
|
|
],
|
|
)
|
|
def test_map_dictlike(self, mapper):
|
|
|
|
index = self.create_index()
|
|
if isinstance(index, (pd.CategoricalIndex, pd.IntervalIndex)):
|
|
pytest.skip("skipping tests for {}".format(type(index)))
|
|
|
|
identity = mapper(index.values, index)
|
|
|
|
# we don't infer to UInt64 for a dict
|
|
if isinstance(index, pd.UInt64Index) and isinstance(identity, dict):
|
|
expected = index.astype("int64")
|
|
else:
|
|
expected = index
|
|
|
|
result = index.map(identity)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
# empty mappable
|
|
expected = pd.Index([np.nan] * len(index))
|
|
result = index.map(mapper(expected, index))
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
def test_map_str(self):
|
|
# GH 31202
|
|
index = self.create_index()
|
|
result = index.map(str)
|
|
expected = Index([str(x) for x in index], dtype=object)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
def test_putmask_with_wrong_mask(self):
|
|
# GH18368
|
|
index = self.create_index()
|
|
|
|
with pytest.raises(ValueError):
|
|
index.putmask(np.ones(len(index) + 1, np.bool), 1)
|
|
|
|
with pytest.raises(ValueError):
|
|
index.putmask(np.ones(len(index) - 1, np.bool), 1)
|
|
|
|
with pytest.raises(ValueError):
|
|
index.putmask("foo", 1)
|
|
|
|
@pytest.mark.parametrize("copy", [True, False])
|
|
@pytest.mark.parametrize("name", [None, "foo"])
|
|
@pytest.mark.parametrize("ordered", [True, False])
|
|
def test_astype_category(self, copy, name, ordered):
|
|
# GH 18630
|
|
index = self.create_index()
|
|
if name:
|
|
index = index.rename(name)
|
|
|
|
# standard categories
|
|
dtype = CategoricalDtype(ordered=ordered)
|
|
result = index.astype(dtype, copy=copy)
|
|
expected = CategoricalIndex(index.values, name=name, ordered=ordered)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
# non-standard categories
|
|
dtype = CategoricalDtype(index.unique().tolist()[:-1], ordered)
|
|
result = index.astype(dtype, copy=copy)
|
|
expected = CategoricalIndex(index.values, name=name, dtype=dtype)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
if ordered is False:
|
|
# dtype='category' defaults to ordered=False, so only test once
|
|
result = index.astype("category", copy=copy)
|
|
expected = CategoricalIndex(index.values, name=name)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
def test_is_unique(self):
|
|
# initialize a unique index
|
|
index = self.create_index().drop_duplicates()
|
|
assert index.is_unique is True
|
|
|
|
# empty index should be unique
|
|
index_empty = index[:0]
|
|
assert index_empty.is_unique is True
|
|
|
|
# test basic dupes
|
|
index_dup = index.insert(0, index[0])
|
|
assert index_dup.is_unique is False
|
|
|
|
# single NA should be unique
|
|
index_na = index.insert(0, np.nan)
|
|
assert index_na.is_unique is True
|
|
|
|
# multiple NA should not be unique
|
|
index_na_dup = index_na.insert(0, np.nan)
|
|
assert index_na_dup.is_unique is False
|
|
|
|
def test_engine_reference_cycle(self):
|
|
# GH27585
|
|
index = self.create_index()
|
|
nrefs_pre = len(gc.get_referrers(index))
|
|
index._engine
|
|
assert len(gc.get_referrers(index)) == nrefs_pre
|
|
|
|
def test_getitem_2d_deprecated(self):
|
|
# GH#30588
|
|
idx = self.create_index()
|
|
with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
|
|
res = idx[:, None]
|
|
|
|
assert isinstance(res, np.ndarray), type(res)
|