laywerrobot/lib/python3.6/site-packages/pandas/tests/indexes/common.py
2020-08-27 21:55:39 +02:00

1110 lines
41 KiB
Python

# -*- coding: utf-8 -*-
import pytest
from pandas import compat
from pandas.compat import PY3
import numpy as np
from pandas import (Series, Index, Float64Index, Int64Index, UInt64Index,
RangeIndex, MultiIndex, CategoricalIndex, DatetimeIndex,
TimedeltaIndex, PeriodIndex, IntervalIndex, isna)
from pandas.core.indexes.base import InvalidIndexError
from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
from pandas.core.dtypes.common import needs_i8_conversion
from pandas.core.dtypes.dtypes import CategoricalDtype
from pandas._libs.tslib import iNaT
import pandas.util.testing as tm
import pandas as pd
class Base(object):
""" base class for index sub-class tests """
_holder = None
_compat_props = ['shape', 'ndim', 'size', 'nbytes']
def setup_indices(self):
for name, idx in self.indices.items():
setattr(self, name, idx)
def verify_pickle(self, indices):
unpickled = tm.round_trip_pickle(indices)
assert indices.equals(unpickled)
def test_pickle_compat_construction(self):
# this is testing for pickle compat
if self._holder is None:
return
# need an object to create with
pytest.raises(TypeError, 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
def test_to_frame(self):
# see gh-15230
idx = self.create_index()
name = idx.name or 0
df = idx.to_frame()
assert df.index is idx
assert len(df.columns) == 1
assert df.columns[0] == name
assert df[name].values is not idx.values
df = idx.to_frame(index=False)
assert df.index is not idx
def test_shift(self):
# GH8083 test the base class for shift
idx = self.create_index()
pytest.raises(NotImplementedError, idx.shift, 1)
pytest.raises(NotImplementedError, idx.shift, 1, 2)
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()
tm.assert_raises_regex(TypeError, "cannot perform __mul__",
lambda: idx * 1)
tm.assert_raises_regex(TypeError, "cannot perform __rmul__",
lambda: 1 * idx)
div_err = "cannot perform __truediv__" if PY3 \
else "cannot perform __div__"
tm.assert_raises_regex(TypeError, div_err, lambda: idx / 1)
div_err = div_err.replace(' __', ' __r')
tm.assert_raises_regex(TypeError, div_err, lambda: 1 / idx)
tm.assert_raises_regex(TypeError, "cannot perform __floordiv__",
lambda: idx // 1)
tm.assert_raises_regex(TypeError, "cannot perform __rfloordiv__",
lambda: 1 // idx)
def test_logical_compat(self):
idx = self.create_index()
tm.assert_raises_regex(TypeError, 'cannot perform all',
lambda: idx.all())
tm.assert_raises_regex(TypeError, 'cannot perform any',
lambda: idx.any())
def test_boolean_context_compat(self):
# boolean context compat
idx = self.create_index()
def f():
if idx:
pass
tm.assert_raises_regex(ValueError, 'The truth value of a', f)
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 tm.assert_raises_regex(ValueError, 'Invalid fill method'):
idx.get_indexer(idx, method='invalid')
def test_get_indexer_consistency(self):
# See GH 16819
for name, index in self.indices.items():
if isinstance(index, IntervalIndex):
continue
if index.is_unique or isinstance(index, CategoricalIndex):
indexer = index.get_indexer(index[0:2])
assert isinstance(indexer, np.ndarray)
assert indexer.dtype == np.intp
else:
e = "Reindexing only valid with uniquely valued Index objects"
with tm.assert_raises_regex(InvalidIndexError, e):
indexer = index.get_indexer(index[0:2])
indexer, _ = index.get_indexer_non_unique(index[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 idx.__class__.__name__ in str(idx)
def test_dtype_str(self, indices):
dtype = indices.dtype_str
assert isinstance(dtype, compat.string_types)
assert dtype == str(indices.dtype)
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_wrong_number_names(self, indices):
def testit(ind):
ind.names = ["apple", "banana", "carrot"]
tm.assert_raises_regex(ValueError, "^Length", testit, indices)
def test_set_name_methods(self, indices):
new_name = "This is the new name for this index"
# don't tests a MultiIndex here (as its tested separated)
if isinstance(indices, MultiIndex):
return
original_name = indices.name
new_ind = indices.set_names([new_name])
assert new_ind.name == new_name
assert indices.name == original_name
res = indices.rename(new_name, inplace=True)
# should return None
assert res is None
assert indices.name == new_name
assert indices.names == [new_name]
# with tm.assert_raises_regex(TypeError, "list-like"):
# # should still fail even if it would be the right length
# ind.set_names("a")
with tm.assert_raises_regex(ValueError, "Level must be None"):
indices.set_names("a", level=0)
# rename in place just leaves tuples and other containers alone
name = ('A', 'B')
indices.rename(name, inplace=True)
assert indices.name == name
assert indices.names == [name]
def test_hash_error(self, indices):
index = indices
tm.assert_raises_regex(TypeError, "unhashable type: %r" %
type(index).__name__, hash, indices)
def test_copy_name(self):
# gh-12309: Check that the "name" argument
# passed at initialization is honored.
for name, index in compat.iteritems(self.indices):
if isinstance(index, MultiIndex):
continue
first = index.__class__(index, copy=True, name='mario')
second = first.__class__(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 index.equals(first)
assert first.name == 'mario'
assert second.name == 'mario'
s1 = Series(2, index=first)
s2 = Series(3, index=second[:-1])
if not isinstance(index, CategoricalIndex):
# See gh-13365
s3 = s1 * s2
assert s3.index.name == 'mario'
def test_ensure_copied_data(self):
# Check the "copy" argument of each Index.__new__ is honoured
# GH12309
for name, index in compat.iteritems(self.indices):
init_kwargs = {}
if isinstance(index, PeriodIndex):
# Needs "freq" specification:
init_kwargs['freq'] = index.freq
elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):
# RangeIndex cannot be initialized from data
# MultiIndex and CategoricalIndex are tested separately
continue
index_type = index.__class__
result = index_type(index.values, copy=True, **init_kwargs)
tm.assert_index_equal(index, result)
tm.assert_numpy_array_equal(index.values, result.values,
check_same='copy')
if isinstance(index, PeriodIndex):
# .values an object array of Period, thus copied
result = index_type(ordinal=index.asi8, copy=False,
**init_kwargs)
tm.assert_numpy_array_equal(index._ndarray_values,
result._ndarray_values,
check_same='same')
elif isinstance(index, IntervalIndex):
# checked in test_interval.py
pass
else:
result = index_type(index.values, copy=False, **init_kwargs)
tm.assert_numpy_array_equal(index.values, result.values,
check_same='same')
tm.assert_numpy_array_equal(index._ndarray_values,
result._ndarray_values,
check_same='same')
def test_copy_and_deepcopy(self, indices):
from copy import copy, deepcopy
if isinstance(indices, MultiIndex):
return
for func in (copy, deepcopy):
idx_copy = func(indices)
assert idx_copy is not indices
assert idx_copy.equals(indices)
new_copy = indices.copy(deep=True, name="banana")
assert new_copy.name == "banana"
def test_duplicates(self, indices):
if type(indices) is not self._holder:
return
if not len(indices) or isinstance(indices, MultiIndex):
return
idx = self._holder([indices[0]] * 5)
assert not idx.is_unique
assert idx.has_duplicates
def test_unique(self, indices):
# don't test a MultiIndex here (as its tested separated)
# don't test a CategoricalIndex because categories change (GH 18291)
if isinstance(indices, (MultiIndex, CategoricalIndex)):
return
# GH 17896
expected = indices.drop_duplicates()
for level in 0, indices.name, None:
result = indices.unique(level=level)
tm.assert_index_equal(result, expected)
for level in 3, 'wrong':
pytest.raises((IndexError, KeyError), indices.unique, level=level)
def test_unique_na(self):
idx = pd.Index([2, np.nan, 2, 1], name='my_index')
expected = pd.Index([2, np.nan, 1], name='my_index')
result = idx.unique()
tm.assert_index_equal(result, expected)
def test_get_unique_index(self, indices):
# MultiIndex tested separately
if not len(indices) or isinstance(indices, MultiIndex):
return
idx = indices[[0] * 5]
idx_unique = indices[[0]]
# We test against `idx_unique`, so first we make sure it's unique
# and doesn't contain nans.
assert idx_unique.is_unique
try:
assert not idx_unique.hasnans
except NotImplementedError:
pass
for dropna in [False, True]:
result = idx._get_unique_index(dropna=dropna)
tm.assert_index_equal(result, idx_unique)
# nans:
if not indices._can_hold_na:
return
if needs_i8_conversion(indices):
vals = indices.asi8[[0] * 5]
vals[0] = iNaT
else:
vals = indices.values[[0] * 5]
vals[0] = np.nan
vals_unique = vals[:2]
idx_nan = indices._shallow_copy(vals)
idx_unique_nan = indices._shallow_copy(vals_unique)
assert idx_unique_nan.is_unique
assert idx_nan.dtype == indices.dtype
assert idx_unique_nan.dtype == indices.dtype
for dropna, expected in zip([False, True],
[idx_unique_nan,
idx_unique]):
for i in [idx_nan, idx_unique_nan]:
result = i._get_unique_index(dropna=dropna)
tm.assert_index_equal(result, expected)
def test_sort(self, indices):
pytest.raises(TypeError, indices.sort)
def test_mutability(self, indices):
if not len(indices):
return
pytest.raises(TypeError, indices.__setitem__, 0, indices[0])
def test_view(self, indices):
assert indices.view().name == indices.name
def test_compat(self, indices):
assert indices.tolist() == list(indices)
def test_memory_usage(self):
for name, index in compat.iteritems(self.indices):
result = index.memory_usage()
if len(index):
index.get_loc(index[0])
result2 = index.memory_usage()
result3 = index.memory_usage(deep=True)
# RangeIndex, IntervalIndex
# don't have engines
if not isinstance(index, (RangeIndex, IntervalIndex)):
assert result2 > result
if index.inferred_type == 'object':
assert result3 > result2
else:
# we report 0 for no-length
assert result == 0
def test_argsort(self):
for k, ind in self.indices.items():
# separately tested
if k in ['catIndex']:
continue
result = ind.argsort()
expected = np.array(ind).argsort()
tm.assert_numpy_array_equal(result, expected, check_dtype=False)
def test_numpy_argsort(self):
for k, ind in self.indices.items():
result = np.argsort(ind)
expected = ind.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(ind), (CategoricalIndex, RangeIndex)):
msg = "the 'axis' parameter is not supported"
tm.assert_raises_regex(ValueError, msg,
np.argsort, ind, axis=1)
msg = "the 'kind' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.argsort,
ind, kind='mergesort')
msg = "the 'order' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.argsort,
ind, order=('a', 'b'))
def test_pickle(self, indices):
self.verify_pickle(indices)
original_name, indices.name = indices.name, 'foo'
self.verify_pickle(indices)
indices.name = original_name
def test_take(self):
indexer = [4, 3, 0, 2]
for k, ind in self.indices.items():
# separate
if k in ['boolIndex', 'tuples', 'empty']:
continue
result = ind.take(indexer)
expected = ind[indexer]
assert result.equals(expected)
if not isinstance(ind,
(DatetimeIndex, PeriodIndex, TimedeltaIndex)):
# GH 10791
with pytest.raises(AttributeError):
ind.freq
def test_take_invalid_kwargs(self):
idx = self.create_index()
indices = [1, 2]
msg = r"take\(\) got an unexpected keyword argument 'foo'"
tm.assert_raises_regex(TypeError, msg, idx.take,
indices, foo=2)
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, idx.take,
indices, out=indices)
msg = "the 'mode' parameter is not supported"
tm.assert_raises_regex(ValueError, 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"
tm.assert_raises_regex(ValueError, 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)
def test_setops_errorcases(self):
for name, idx in compat.iteritems(self.indices):
# # non-iterable input
cases = [0.5, 'xxx']
methods = [idx.intersection, idx.union, idx.difference,
idx.symmetric_difference]
for method in methods:
for case in cases:
tm.assert_raises_regex(TypeError,
"Input must be Index "
"or array-like",
method, case)
def test_intersection_base(self):
for name, idx in compat.iteritems(self.indices):
first = idx[:5]
second = idx[:3]
intersect = first.intersection(second)
if isinstance(idx, CategoricalIndex):
pass
else:
assert tm.equalContents(intersect, second)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with tm.assert_raises_regex(ValueError, msg):
result = first.intersection(case)
elif isinstance(idx, CategoricalIndex):
pass
else:
result = first.intersection(case)
assert tm.equalContents(result, second)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with tm.assert_raises_regex(TypeError, msg):
result = first.intersection([1, 2, 3])
def test_union_base(self):
for name, idx in compat.iteritems(self.indices):
first = idx[3:]
second = idx[:5]
everything = idx
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 isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with tm.assert_raises_regex(ValueError, msg):
result = first.union(case)
elif isinstance(idx, CategoricalIndex):
pass
else:
result = first.union(case)
assert tm.equalContents(result, everything)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with tm.assert_raises_regex(TypeError, msg):
result = first.union([1, 2, 3])
def test_difference_base(self):
for name, idx in compat.iteritems(self.indices):
first = idx[2:]
second = idx[:4]
answer = idx[4:]
result = first.difference(second)
if isinstance(idx, CategoricalIndex):
pass
else:
assert tm.equalContents(result, answer)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with tm.assert_raises_regex(ValueError, msg):
result = first.difference(case)
elif isinstance(idx, CategoricalIndex):
pass
elif isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
assert result.__class__ == answer.__class__
tm.assert_numpy_array_equal(result.sort_values().asi8,
answer.sort_values().asi8)
else:
result = first.difference(case)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with tm.assert_raises_regex(TypeError, msg):
result = first.difference([1, 2, 3])
def test_symmetric_difference(self):
for name, idx in compat.iteritems(self.indices):
first = idx[1:]
second = idx[:-1]
if isinstance(idx, CategoricalIndex):
pass
else:
answer = idx[[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:
if isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with tm.assert_raises_regex(ValueError, msg):
result = first.symmetric_difference(case)
elif isinstance(idx, CategoricalIndex):
pass
else:
result = first.symmetric_difference(case)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with tm.assert_raises_regex(TypeError, msg):
first.symmetric_difference([1, 2, 3])
def test_insert_base(self):
for name, idx in compat.iteritems(self.indices):
result = idx[1:4]
if not len(idx):
continue
# test 0th element
assert idx[0:4].equals(result.insert(0, idx[0]))
def test_delete_base(self):
for name, idx in compat.iteritems(self.indices):
if not len(idx):
continue
if isinstance(idx, RangeIndex):
# tested in class
continue
expected = idx[1:]
result = idx.delete(0)
assert result.equals(expected)
assert result.name == expected.name
expected = idx[:-1]
result = idx.delete(-1)
assert result.equals(expected)
assert result.name == expected.name
with pytest.raises((IndexError, ValueError)):
# either depending on numpy version
result = idx.delete(len(idx))
def test_equals(self):
for name, idx in compat.iteritems(self.indices):
assert idx.equals(idx)
assert idx.equals(idx.copy())
assert idx.equals(idx.astype(object))
assert not idx.equals(list(idx))
assert not idx.equals(np.array(idx))
# Cannot pass in non-int64 dtype to RangeIndex
if not isinstance(idx, RangeIndex):
same_values = Index(idx, dtype=object)
assert idx.equals(same_values)
assert same_values.equals(idx)
if idx.nlevels == 1:
# do not test MultiIndex
assert not idx.equals(pd.Series(idx))
def test_equals_op(self):
# GH9947, GH10637
index_a = self.create_index()
if isinstance(index_a, PeriodIndex):
return
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]
with tm.assert_raises_regex(ValueError, "Lengths must match"):
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 tm.assert_raises_regex(ValueError, "Lengths must match"):
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 tm.assert_raises_regex(ValueError, "Lengths must match"):
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 tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == index_d
with tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == series_d
with tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == array_d
msg = "Can only compare identically-labeled Series objects"
with tm.assert_raises_regex(ValueError, msg):
series_a == series_d
with tm.assert_raises_regex(ValueError, "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_numpy_ufuncs(self):
# test ufuncs of numpy 1.9.2. see:
# http://docs.scipy.org/doc/numpy/reference/ufuncs.html
# some functions are skipped because it may return different result
# for unicode input depending on numpy version
for name, idx in compat.iteritems(self.indices):
for func in [np.exp, np.exp2, np.expm1, np.log, np.log2, np.log10,
np.log1p, np.sqrt, np.sin, np.cos, np.tan, np.arcsin,
np.arccos, np.arctan, np.sinh, np.cosh, np.tanh,
np.arcsinh, np.arccosh, np.arctanh, np.deg2rad,
np.rad2deg]:
if isinstance(idx, DatetimeIndexOpsMixin):
# raise TypeError or ValueError (PeriodIndex)
# PeriodIndex behavior should be changed in future version
with pytest.raises(Exception):
with np.errstate(all='ignore'):
func(idx)
elif isinstance(idx, (Float64Index, Int64Index, UInt64Index)):
# coerces to float (e.g. np.sin)
with np.errstate(all='ignore'):
result = func(idx)
exp = Index(func(idx.values), name=idx.name)
tm.assert_index_equal(result, exp)
assert isinstance(result, pd.Float64Index)
else:
# raise AttributeError or TypeError
if len(idx) == 0:
continue
else:
with pytest.raises(Exception):
with np.errstate(all='ignore'):
func(idx)
for func in [np.isfinite, np.isinf, np.isnan, np.signbit]:
if isinstance(idx, DatetimeIndexOpsMixin):
# raise TypeError or ValueError (PeriodIndex)
with pytest.raises(Exception):
func(idx)
elif isinstance(idx, (Float64Index, Int64Index, UInt64Index)):
# Results in bool array
result = func(idx)
assert isinstance(result, np.ndarray)
assert not isinstance(result, Index)
else:
if len(idx) == 0:
continue
else:
with pytest.raises(Exception):
func(idx)
def test_hasnans_isnans(self):
# GH 11343, added tests for hasnans / isnans
for name, index in self.indices.items():
if isinstance(index, MultiIndex):
pass
else:
idx = index.copy()
# cases in indices doesn't include NaN
expected = np.array([False] * len(idx), dtype=bool)
tm.assert_numpy_array_equal(idx._isnan, expected)
assert not idx.hasnans
idx = index.copy()
values = idx.values
if len(index) == 0:
continue
elif isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans
def test_fillna(self):
# GH 11343
for name, index in self.indices.items():
if len(index) == 0:
pass
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with tm.assert_raises_regex(NotImplementedError, msg):
idx.fillna(idx[0])
else:
idx = index.copy()
result = idx.fillna(idx[0])
tm.assert_index_equal(result, idx)
assert result is not idx
msg = "'value' must be a scalar, passed: "
with tm.assert_raises_regex(TypeError, msg):
idx.fillna([idx[0]])
idx = index.copy()
values = idx.values
if isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans
def test_nulls(self):
# this is really a smoke test for the methods
# as these are adequately tested for function elsewhere
for name, index in self.indices.items():
if len(index) == 0:
tm.assert_numpy_array_equal(
index.isna(), np.array([], dtype=bool))
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isna is not defined for MultiIndex"
with tm.assert_raises_regex(NotImplementedError, msg):
idx.isna()
else:
if not index.hasnans:
tm.assert_numpy_array_equal(
index.isna(), np.zeros(len(index), dtype=bool))
tm.assert_numpy_array_equal(
index.notna(), np.ones(len(index), dtype=bool))
else:
result = isna(index)
tm.assert_numpy_array_equal(index.isna(), result)
tm.assert_numpy_array_equal(index.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_searchsorted_monotonic(self, indices):
# GH17271
# not implemented for tuple searches in MultiIndex
# or Intervals searches in IntervalIndex
if isinstance(indices, (MultiIndex, IntervalIndex)):
return
# nothing to test if the index is empty
if indices.empty:
return
value = indices[0]
# determine the expected results (handle dupes for 'right')
expected_left, expected_right = 0, (indices == value).argmin()
if expected_right == 0:
# all values are the same, expected_right should be length
expected_right = len(indices)
# test _searchsorted_monotonic in all cases
# test searchsorted only for increasing
if indices.is_monotonic_increasing:
ssm_left = indices._searchsorted_monotonic(value, side='left')
assert expected_left == ssm_left
ssm_right = indices._searchsorted_monotonic(value, side='right')
assert expected_right == ssm_right
ss_left = indices.searchsorted(value, side='left')
assert expected_left == ss_left
ss_right = indices.searchsorted(value, side='right')
assert expected_right == ss_right
elif indices.is_monotonic_decreasing:
ssm_left = indices._searchsorted_monotonic(value, side='left')
assert expected_left == ssm_left
ssm_right = indices._searchsorted_monotonic(value, side='right')
assert expected_right == ssm_right
else:
# non-monotonic should raise.
with pytest.raises(ValueError):
indices._searchsorted_monotonic(value, side='left')
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_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)