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.venv/Lib/site-packages/pandas/tests/libs/__init__.py Normal file
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.venv/Lib/site-packages/pandas/tests/libs/test_hashtable.py Normal file
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from contextlib import contextmanager
import tracemalloc
import numpy as np
import pytest
from pandas._libs import hashtable as ht
import pandas as pd
import pandas._testing as tm
from pandas.core.algorithms import isin
@contextmanager
def activated_tracemalloc():
tracemalloc.start()
try:
yield
finally:
tracemalloc.stop()
def get_allocated_khash_memory():
snapshot = tracemalloc.take_snapshot()
snapshot = snapshot.filter_traces(
(tracemalloc.DomainFilter(True, ht.get_hashtable_trace_domain()),)
)
return sum(map(lambda x: x.size, snapshot.traces))
@pytest.mark.parametrize(
"table_type, dtype",
[
(ht.PyObjectHashTable, np.object_),
(ht.Complex128HashTable, np.complex128),
(ht.Int64HashTable, np.int64),
(ht.UInt64HashTable, np.uint64),
(ht.Float64HashTable, np.float64),
(ht.Complex64HashTable, np.complex64),
(ht.Int32HashTable, np.int32),
(ht.UInt32HashTable, np.uint32),
(ht.Float32HashTable, np.float32),
(ht.Int16HashTable, np.int16),
(ht.UInt16HashTable, np.uint16),
(ht.Int8HashTable, np.int8),
(ht.UInt8HashTable, np.uint8),
(ht.IntpHashTable, np.intp),
],
)
class TestHashTable:
def test_get_set_contains_len(self, table_type, dtype):
index = 5
table = table_type(55)
assert len(table) == 0
assert index not in table
table.set_item(index, 42)
assert len(table) == 1
assert index in table
assert table.get_item(index) == 42
table.set_item(index + 1, 41)
assert index in table
assert index + 1 in table
assert len(table) == 2
assert table.get_item(index) == 42
assert table.get_item(index + 1) == 41
table.set_item(index, 21)
assert index in table
assert index + 1 in table
assert len(table) == 2
assert table.get_item(index) == 21
assert table.get_item(index + 1) == 41
assert index + 2 not in table
with pytest.raises(KeyError, match=str(index + 2)):
table.get_item(index + 2)
def test_map(self, table_type, dtype, writable):
# PyObjectHashTable has no map-method
if table_type != ht.PyObjectHashTable:
N = 77
table = table_type()
keys = np.arange(N).astype(dtype)
vals = np.arange(N).astype(np.int64) + N
keys.flags.writeable = writable
vals.flags.writeable = writable
table.map(keys, vals)
for i in range(N):
assert table.get_item(keys[i]) == i + N
def test_map_locations(self, table_type, dtype, writable):
N = 8
table = table_type()
keys = (np.arange(N) + N).astype(dtype)
keys.flags.writeable = writable
table.map_locations(keys)
for i in range(N):
assert table.get_item(keys[i]) == i
def test_lookup(self, table_type, dtype, writable):
N = 3
table = table_type()
keys = (np.arange(N) + N).astype(dtype)
keys.flags.writeable = writable
table.map_locations(keys)
result = table.lookup(keys)
expected = np.arange(N)
tm.assert_numpy_array_equal(result.astype(np.int64), expected.astype(np.int64))
def test_lookup_wrong(self, table_type, dtype):
if dtype in (np.int8, np.uint8):
N = 100
else:
N = 512
table = table_type()
keys = (np.arange(N) + N).astype(dtype)
table.map_locations(keys)
wrong_keys = np.arange(N).astype(dtype)
result = table.lookup(wrong_keys)
assert np.all(result == -1)
def test_unique(self, table_type, dtype, writable):
if dtype in (np.int8, np.uint8):
N = 88
else:
N = 1000
table = table_type()
expected = (np.arange(N) + N).astype(dtype)
keys = np.repeat(expected, 5)
keys.flags.writeable = writable
unique = table.unique(keys)
tm.assert_numpy_array_equal(unique, expected)
def test_tracemalloc_works(self, table_type, dtype):
if dtype in (np.int8, np.uint8):
N = 256
else:
N = 30000
keys = np.arange(N).astype(dtype)
with activated_tracemalloc():
table = table_type()
table.map_locations(keys)
used = get_allocated_khash_memory()
my_size = table.sizeof()
assert used == my_size
del table
assert get_allocated_khash_memory() == 0
def test_tracemalloc_for_empty(self, table_type, dtype):
with activated_tracemalloc():
table = table_type()
used = get_allocated_khash_memory()
my_size = table.sizeof()
assert used == my_size
del table
assert get_allocated_khash_memory() == 0
def test_get_state(self, table_type, dtype):
table = table_type(1000)
state = table.get_state()
assert state["size"] == 0
assert state["n_occupied"] == 0
assert "n_buckets" in state
assert "upper_bound" in state
def test_no_reallocation(self, table_type, dtype):
for N in range(1, 110):
keys = np.arange(N).astype(dtype)
preallocated_table = table_type(N)
n_buckets_start = preallocated_table.get_state()["n_buckets"]
preallocated_table.map_locations(keys)
n_buckets_end = preallocated_table.get_state()["n_buckets"]
# original number of buckets was enough:
assert n_buckets_start == n_buckets_end
# check with clean table (not too much preallocated)
clean_table = table_type()
clean_table.map_locations(keys)
assert n_buckets_start == clean_table.get_state()["n_buckets"]
class TestPyObjectHashTableWithNans:
def test_nan_float(self):
nan1 = float("nan")
nan2 = float("nan")
assert nan1 is not nan2
table = ht.PyObjectHashTable()
table.set_item(nan1, 42)
assert table.get_item(nan2) == 42
def test_nan_complex_both(self):
nan1 = complex(float("nan"), float("nan"))
nan2 = complex(float("nan"), float("nan"))
assert nan1 is not nan2
table = ht.PyObjectHashTable()
table.set_item(nan1, 42)
assert table.get_item(nan2) == 42
def test_nan_complex_real(self):
nan1 = complex(float("nan"), 1)
nan2 = complex(float("nan"), 1)
other = complex(float("nan"), 2)
assert nan1 is not nan2
table = ht.PyObjectHashTable()
table.set_item(nan1, 42)
assert table.get_item(nan2) == 42
with pytest.raises(KeyError, match=None) as error:
table.get_item(other)
assert str(error.value) == str(other)
def test_nan_complex_imag(self):
nan1 = complex(1, float("nan"))
nan2 = complex(1, float("nan"))
other = complex(2, float("nan"))
assert nan1 is not nan2
table = ht.PyObjectHashTable()
table.set_item(nan1, 42)
assert table.get_item(nan2) == 42
with pytest.raises(KeyError, match=None) as error:
table.get_item(other)
assert str(error.value) == str(other)
def test_nan_in_tuple(self):
nan1 = (float("nan"),)
nan2 = (float("nan"),)
assert nan1[0] is not nan2[0]
table = ht.PyObjectHashTable()
table.set_item(nan1, 42)
assert table.get_item(nan2) == 42
def test_nan_in_nested_tuple(self):
nan1 = (1, (2, (float("nan"),)))
nan2 = (1, (2, (float("nan"),)))
other = (1, 2)
table = ht.PyObjectHashTable()
table.set_item(nan1, 42)
assert table.get_item(nan2) == 42
with pytest.raises(KeyError, match=None) as error:
table.get_item(other)
assert str(error.value) == str(other)
def test_hash_equal_tuple_with_nans():
a = (float("nan"), (float("nan"), float("nan")))
b = (float("nan"), (float("nan"), float("nan")))
assert ht.object_hash(a) == ht.object_hash(b)
assert ht.objects_are_equal(a, b)
def test_get_labels_groupby_for_Int64(writable):
table = ht.Int64HashTable()
vals = np.array([1, 2, -1, 2, 1, -1], dtype=np.int64)
vals.flags.writeable = writable
arr, unique = table.get_labels_groupby(vals)
expected_arr = np.array([0, 1, -1, 1, 0, -1], dtype=np.intp)
expected_unique = np.array([1, 2], dtype=np.int64)
tm.assert_numpy_array_equal(arr, expected_arr)
tm.assert_numpy_array_equal(unique, expected_unique)
def test_tracemalloc_works_for_StringHashTable():
N = 1000
keys = np.arange(N).astype(np.compat.unicode).astype(np.object_)
with activated_tracemalloc():
table = ht.StringHashTable()
table.map_locations(keys)
used = get_allocated_khash_memory()
my_size = table.sizeof()
assert used == my_size
del table
assert get_allocated_khash_memory() == 0
def test_tracemalloc_for_empty_StringHashTable():
with activated_tracemalloc():
table = ht.StringHashTable()
used = get_allocated_khash_memory()
my_size = table.sizeof()
assert used == my_size
del table
assert get_allocated_khash_memory() == 0
def test_no_reallocation_StringHashTable():
for N in range(1, 110):
keys = np.arange(N).astype(np.compat.unicode).astype(np.object_)
preallocated_table = ht.StringHashTable(N)
n_buckets_start = preallocated_table.get_state()["n_buckets"]
preallocated_table.map_locations(keys)
n_buckets_end = preallocated_table.get_state()["n_buckets"]
# original number of buckets was enough:
assert n_buckets_start == n_buckets_end
# check with clean table (not too much preallocated)
clean_table = ht.StringHashTable()
clean_table.map_locations(keys)
assert n_buckets_start == clean_table.get_state()["n_buckets"]
@pytest.mark.parametrize(
"table_type, dtype",
[
(ht.Float64HashTable, np.float64),
(ht.Float32HashTable, np.float32),
(ht.Complex128HashTable, np.complex128),
(ht.Complex64HashTable, np.complex64),
],
)
class TestHashTableWithNans:
def test_get_set_contains_len(self, table_type, dtype):
index = float("nan")
table = table_type()
assert index not in table
table.set_item(index, 42)
assert len(table) == 1
assert index in table
assert table.get_item(index) == 42
table.set_item(index, 41)
assert len(table) == 1
assert index in table
assert table.get_item(index) == 41
def test_map(self, table_type, dtype):
N = 332
table = table_type()
keys = np.full(N, np.nan, dtype=dtype)
vals = (np.arange(N) + N).astype(np.int64)
table.map(keys, vals)
assert len(table) == 1
assert table.get_item(np.nan) == 2 * N - 1
def test_map_locations(self, table_type, dtype):
N = 10
table = table_type()
keys = np.full(N, np.nan, dtype=dtype)
table.map_locations(keys)
assert len(table) == 1
assert table.get_item(np.nan) == N - 1
def test_unique(self, table_type, dtype):
N = 1020
table = table_type()
keys = np.full(N, np.nan, dtype=dtype)
unique = table.unique(keys)
assert np.all(np.isnan(unique)) and len(unique) == 1
def test_unique_for_nan_objects_floats():
table = ht.PyObjectHashTable()
keys = np.array([float("nan") for i in range(50)], dtype=np.object_)
unique = table.unique(keys)
assert len(unique) == 1
def test_unique_for_nan_objects_complex():
table = ht.PyObjectHashTable()
keys = np.array([complex(float("nan"), 1.0) for i in range(50)], dtype=np.object_)
unique = table.unique(keys)
assert len(unique) == 1
def test_unique_for_nan_objects_tuple():
table = ht.PyObjectHashTable()
keys = np.array(
[1] + [(1.0, (float("nan"), 1.0)) for i in range(50)], dtype=np.object_
)
unique = table.unique(keys)
assert len(unique) == 2
@pytest.mark.parametrize(
"dtype",
[
np.object_,
np.complex128,
np.int64,
np.uint64,
np.float64,
np.complex64,
np.int32,
np.uint32,
np.float32,
np.int16,
np.uint16,
np.int8,
np.uint8,
np.intp,
],
)
class TestHelpFunctions:
def test_value_count(self, dtype, writable):
N = 43
expected = (np.arange(N) + N).astype(dtype)
values = np.repeat(expected, 5)
values.flags.writeable = writable
keys, counts = ht.value_count(values, False)
tm.assert_numpy_array_equal(np.sort(keys), expected)
assert np.all(counts == 5)
def test_value_count_stable(self, dtype, writable):
# GH12679
values = np.array([2, 1, 5, 22, 3, -1, 8]).astype(dtype)
values.flags.writeable = writable
keys, counts = ht.value_count(values, False)
tm.assert_numpy_array_equal(keys, values)
assert np.all(counts == 1)
def test_duplicated_first(self, dtype, writable):
N = 100
values = np.repeat(np.arange(N).astype(dtype), 5)
values.flags.writeable = writable
result = ht.duplicated(values)
expected = np.ones_like(values, dtype=np.bool_)
expected[::5] = False
tm.assert_numpy_array_equal(result, expected)
def test_ismember_yes(self, dtype, writable):
N = 127
arr = np.arange(N).astype(dtype)
values = np.arange(N).astype(dtype)
arr.flags.writeable = writable
values.flags.writeable = writable
result = ht.ismember(arr, values)
expected = np.ones_like(values, dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)
def test_ismember_no(self, dtype):
N = 17
arr = np.arange(N).astype(dtype)
values = (np.arange(N) + N).astype(dtype)
result = ht.ismember(arr, values)
expected = np.zeros_like(values, dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)
def test_mode(self, dtype, writable):
if dtype in (np.int8, np.uint8):
N = 53
else:
N = 11111
values = np.repeat(np.arange(N).astype(dtype), 5)
values[0] = 42
values.flags.writeable = writable
result = ht.mode(values, False)
assert result == 42
def test_mode_stable(self, dtype, writable):
values = np.array([2, 1, 5, 22, 3, -1, 8]).astype(dtype)
values.flags.writeable = writable
keys = ht.mode(values, False)
tm.assert_numpy_array_equal(keys, values)
def test_modes_with_nans():
# GH42688, nans aren't mangled
nulls = [pd.NA, np.nan, pd.NaT, None]
values = np.array([True] + nulls * 2, dtype=np.object_)
modes = ht.mode(values, False)
assert modes.size == len(nulls)
def test_unique_label_indices_intp(writable):
keys = np.array([1, 2, 2, 2, 1, 3], dtype=np.intp)
keys.flags.writeable = writable
result = ht.unique_label_indices(keys)
expected = np.array([0, 1, 5], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize(
"dtype",
[
np.float64,
np.float32,
np.complex128,
np.complex64,
],
)
class TestHelpFunctionsWithNans:
def test_value_count(self, dtype):
values = np.array([np.nan, np.nan, np.nan], dtype=dtype)
keys, counts = ht.value_count(values, True)
assert len(keys) == 0
keys, counts = ht.value_count(values, False)
assert len(keys) == 1 and np.all(np.isnan(keys))
assert counts[0] == 3
def test_duplicated_first(self, dtype):
values = np.array([np.nan, np.nan, np.nan], dtype=dtype)
result = ht.duplicated(values)
expected = np.array([False, True, True])
tm.assert_numpy_array_equal(result, expected)
def test_ismember_yes(self, dtype):
arr = np.array([np.nan, np.nan, np.nan], dtype=dtype)
values = np.array([np.nan, np.nan], dtype=dtype)
result = ht.ismember(arr, values)
expected = np.array([True, True, True], dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)
def test_ismember_no(self, dtype):
arr = np.array([np.nan, np.nan, np.nan], dtype=dtype)
values = np.array([1], dtype=dtype)
result = ht.ismember(arr, values)
expected = np.array([False, False, False], dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)
def test_mode(self, dtype):
values = np.array([42, np.nan, np.nan, np.nan], dtype=dtype)
assert ht.mode(values, True) == 42
assert np.isnan(ht.mode(values, False))
def test_ismember_tuple_with_nans():
# GH-41836
values = [("a", float("nan")), ("b", 1)]
comps = [("a", float("nan"))]
result = isin(values, comps)
expected = np.array([True, False], dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)
def test_float_complex_int_are_equal_as_objects():
values = ["a", 5, 5.0, 5.0 + 0j]
comps = list(range(129))
result = isin(values, comps)
expected = np.array([False, True, True, True], dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)

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.venv/Lib/site-packages/pandas/tests/libs/test_join.py Normal file
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import numpy as np
import pytest
from pandas._libs import join as libjoin
from pandas._libs.join import (
inner_join,
left_outer_join,
)
import pandas._testing as tm
class TestIndexer:
@pytest.mark.parametrize(
"dtype", ["int32", "int64", "float32", "float64", "object"]
)
def test_outer_join_indexer(self, dtype):
indexer = libjoin.outer_join_indexer
left = np.arange(3, dtype=dtype)
right = np.arange(2, 5, dtype=dtype)
empty = np.array([], dtype=dtype)
result, lindexer, rindexer = indexer(left, right)
assert isinstance(result, np.ndarray)
assert isinstance(lindexer, np.ndarray)
assert isinstance(rindexer, np.ndarray)
tm.assert_numpy_array_equal(result, np.arange(5, dtype=dtype))
exp = np.array([0, 1, 2, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(lindexer, exp)
exp = np.array([-1, -1, 0, 1, 2], dtype=np.intp)
tm.assert_numpy_array_equal(rindexer, exp)
result, lindexer, rindexer = indexer(empty, right)
tm.assert_numpy_array_equal(result, right)
exp = np.array([-1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(lindexer, exp)
exp = np.array([0, 1, 2], dtype=np.intp)
tm.assert_numpy_array_equal(rindexer, exp)
result, lindexer, rindexer = indexer(left, empty)
tm.assert_numpy_array_equal(result, left)
exp = np.array([0, 1, 2], dtype=np.intp)
tm.assert_numpy_array_equal(lindexer, exp)
exp = np.array([-1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(rindexer, exp)
def test_cython_left_outer_join(self):
left = np.array([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.intp)
right = np.array([1, 1, 0, 4, 2, 2, 1], dtype=np.intp)
max_group = 5
ls, rs = left_outer_join(left, right, max_group)
exp_ls = left.argsort(kind="mergesort")
exp_rs = right.argsort(kind="mergesort")
exp_li = np.array([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 8, 8, 9, 10])
exp_ri = np.array(
[0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5, 4, 5, 4, 5, -1, -1]
)
exp_ls = exp_ls.take(exp_li)
exp_ls[exp_li == -1] = -1
exp_rs = exp_rs.take(exp_ri)
exp_rs[exp_ri == -1] = -1
tm.assert_numpy_array_equal(ls, exp_ls, check_dtype=False)
tm.assert_numpy_array_equal(rs, exp_rs, check_dtype=False)
def test_cython_right_outer_join(self):
left = np.array([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.intp)
right = np.array([1, 1, 0, 4, 2, 2, 1], dtype=np.intp)
max_group = 5
rs, ls = left_outer_join(right, left, max_group)
exp_ls = left.argsort(kind="mergesort")
exp_rs = right.argsort(kind="mergesort")
# 0 1 1 1
exp_li = np.array(
[
0,
1,
2,
3,
4,
5,
3,
4,
5,
3,
4,
5,
# 2 2 4
6,
7,
8,
6,
7,
8,
-1,
]
)
exp_ri = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6])
exp_ls = exp_ls.take(exp_li)
exp_ls[exp_li == -1] = -1
exp_rs = exp_rs.take(exp_ri)
exp_rs[exp_ri == -1] = -1
tm.assert_numpy_array_equal(ls, exp_ls)
tm.assert_numpy_array_equal(rs, exp_rs)
def test_cython_inner_join(self):
left = np.array([0, 1, 2, 1, 2, 0, 0, 1, 2, 3, 3], dtype=np.intp)
right = np.array([1, 1, 0, 4, 2, 2, 1, 4], dtype=np.intp)
max_group = 5
ls, rs = inner_join(left, right, max_group)
exp_ls = left.argsort(kind="mergesort")
exp_rs = right.argsort(kind="mergesort")
exp_li = np.array([0, 1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 8, 8])
exp_ri = np.array([0, 0, 0, 1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5, 4, 5, 4, 5])
exp_ls = exp_ls.take(exp_li)
exp_ls[exp_li == -1] = -1
exp_rs = exp_rs.take(exp_ri)
exp_rs[exp_ri == -1] = -1
tm.assert_numpy_array_equal(ls, exp_ls)
tm.assert_numpy_array_equal(rs, exp_rs)
@pytest.mark.parametrize("readonly", [True, False])
def test_left_join_indexer_unique(readonly):
a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
b = np.array([2, 2, 3, 4, 4], dtype=np.int64)
if readonly:
# GH#37312, GH#37264
a.setflags(write=False)
b.setflags(write=False)
result = libjoin.left_join_indexer_unique(b, a)
expected = np.array([1, 1, 2, 3, 3], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
def test_left_outer_join_bug():
left = np.array(
[
0,
1,
0,
1,
1,
2,
3,
1,
0,
2,
1,
2,
0,
1,
1,
2,
3,
2,
3,
2,
1,
1,
3,
0,
3,
2,
3,
0,
0,
2,
3,
2,
0,
3,
1,
3,
0,
1,
3,
0,
0,
1,
0,
3,
1,
0,
1,
0,
1,
1,
0,
2,
2,
2,
2,
2,
0,
3,
1,
2,
0,
0,
3,
1,
3,
2,
2,
0,
1,
3,
0,
2,
3,
2,
3,
3,
2,
3,
3,
1,
3,
2,
0,
0,
3,
1,
1,
1,
0,
2,
3,
3,
1,
2,
0,
3,
1,
2,
0,
2,
],
dtype=np.intp,
)
right = np.array([3, 1], dtype=np.intp)
max_groups = 4
lidx, ridx = libjoin.left_outer_join(left, right, max_groups, sort=False)
exp_lidx = np.arange(len(left), dtype=np.intp)
exp_ridx = -np.ones(len(left), dtype=np.intp)
exp_ridx[left == 1] = 1
exp_ridx[left == 3] = 0
tm.assert_numpy_array_equal(lidx, exp_lidx)
tm.assert_numpy_array_equal(ridx, exp_ridx)
def test_inner_join_indexer():
a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
index, ares, bres = libjoin.inner_join_indexer(a, b)
index_exp = np.array([3, 5], dtype=np.int64)
tm.assert_almost_equal(index, index_exp)
aexp = np.array([2, 4], dtype=np.intp)
bexp = np.array([1, 2], dtype=np.intp)
tm.assert_almost_equal(ares, aexp)
tm.assert_almost_equal(bres, bexp)
a = np.array([5], dtype=np.int64)
b = np.array([5], dtype=np.int64)
index, ares, bres = libjoin.inner_join_indexer(a, b)
tm.assert_numpy_array_equal(index, np.array([5], dtype=np.int64))
tm.assert_numpy_array_equal(ares, np.array([0], dtype=np.intp))
tm.assert_numpy_array_equal(bres, np.array([0], dtype=np.intp))
def test_outer_join_indexer():
a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
index, ares, bres = libjoin.outer_join_indexer(a, b)
index_exp = np.array([0, 1, 2, 3, 4, 5, 7, 9], dtype=np.int64)
tm.assert_almost_equal(index, index_exp)
aexp = np.array([-1, 0, 1, 2, 3, 4, -1, -1], dtype=np.intp)
bexp = np.array([0, -1, -1, 1, -1, 2, 3, 4], dtype=np.intp)
tm.assert_almost_equal(ares, aexp)
tm.assert_almost_equal(bres, bexp)
a = np.array([5], dtype=np.int64)
b = np.array([5], dtype=np.int64)
index, ares, bres = libjoin.outer_join_indexer(a, b)
tm.assert_numpy_array_equal(index, np.array([5], dtype=np.int64))
tm.assert_numpy_array_equal(ares, np.array([0], dtype=np.intp))
tm.assert_numpy_array_equal(bres, np.array([0], dtype=np.intp))
def test_left_join_indexer():
a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
index, ares, bres = libjoin.left_join_indexer(a, b)
tm.assert_almost_equal(index, a)
aexp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
bexp = np.array([-1, -1, 1, -1, 2], dtype=np.intp)
tm.assert_almost_equal(ares, aexp)
tm.assert_almost_equal(bres, bexp)
a = np.array([5], dtype=np.int64)
b = np.array([5], dtype=np.int64)
index, ares, bres = libjoin.left_join_indexer(a, b)
tm.assert_numpy_array_equal(index, np.array([5], dtype=np.int64))
tm.assert_numpy_array_equal(ares, np.array([0], dtype=np.intp))
tm.assert_numpy_array_equal(bres, np.array([0], dtype=np.intp))
def test_left_join_indexer2():
idx = np.array([1, 1, 2, 5], dtype=np.int64)
idx2 = np.array([1, 2, 5, 7, 9], dtype=np.int64)
res, lidx, ridx = libjoin.left_join_indexer(idx2, idx)
exp_res = np.array([1, 1, 2, 5, 7, 9], dtype=np.int64)
tm.assert_almost_equal(res, exp_res)
exp_lidx = np.array([0, 0, 1, 2, 3, 4], dtype=np.intp)
tm.assert_almost_equal(lidx, exp_lidx)
exp_ridx = np.array([0, 1, 2, 3, -1, -1], dtype=np.intp)
tm.assert_almost_equal(ridx, exp_ridx)
def test_outer_join_indexer2():
idx = np.array([1, 1, 2, 5], dtype=np.int64)
idx2 = np.array([1, 2, 5, 7, 9], dtype=np.int64)
res, lidx, ridx = libjoin.outer_join_indexer(idx2, idx)
exp_res = np.array([1, 1, 2, 5, 7, 9], dtype=np.int64)
tm.assert_almost_equal(res, exp_res)
exp_lidx = np.array([0, 0, 1, 2, 3, 4], dtype=np.intp)
tm.assert_almost_equal(lidx, exp_lidx)
exp_ridx = np.array([0, 1, 2, 3, -1, -1], dtype=np.intp)
tm.assert_almost_equal(ridx, exp_ridx)
def test_inner_join_indexer2():
idx = np.array([1, 1, 2, 5], dtype=np.int64)
idx2 = np.array([1, 2, 5, 7, 9], dtype=np.int64)
res, lidx, ridx = libjoin.inner_join_indexer(idx2, idx)
exp_res = np.array([1, 1, 2, 5], dtype=np.int64)
tm.assert_almost_equal(res, exp_res)
exp_lidx = np.array([0, 0, 1, 2], dtype=np.intp)
tm.assert_almost_equal(lidx, exp_lidx)
exp_ridx = np.array([0, 1, 2, 3], dtype=np.intp)
tm.assert_almost_equal(ridx, exp_ridx)

208
.venv/Lib/site-packages/pandas/tests/libs/test_lib.py Normal file
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@ -0,0 +1,208 @@
import numpy as np
import pytest
from pandas._libs import (
lib,
writers as libwriters,
)
from pandas import Index
import pandas._testing as tm
class TestMisc:
def test_max_len_string_array(self):
arr = a = np.array(["foo", "b", np.nan], dtype="object")
assert libwriters.max_len_string_array(arr) == 3
# unicode
arr = a.astype("U").astype(object)
assert libwriters.max_len_string_array(arr) == 3
# bytes for python3
arr = a.astype("S").astype(object)
assert libwriters.max_len_string_array(arr) == 3
# raises
msg = "No matching signature found"
with pytest.raises(TypeError, match=msg):
libwriters.max_len_string_array(arr.astype("U"))
def test_fast_unique_multiple_list_gen_sort(self):
keys = [["p", "a"], ["n", "d"], ["a", "s"]]
gen = (key for key in keys)
expected = np.array(["a", "d", "n", "p", "s"])
out = lib.fast_unique_multiple_list_gen(gen, sort=True)
tm.assert_numpy_array_equal(np.array(out), expected)
gen = (key for key in keys)
expected = np.array(["p", "a", "n", "d", "s"])
out = lib.fast_unique_multiple_list_gen(gen, sort=False)
tm.assert_numpy_array_equal(np.array(out), expected)
class TestIndexing:
def test_maybe_indices_to_slice_left_edge(self):
target = np.arange(100)
# slice
indices = np.array([], dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
for end in [1, 2, 5, 20, 99]:
for step in [1, 2, 4]:
indices = np.arange(0, end, step, dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
# reverse
indices = indices[::-1]
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
# not slice
for case in [[2, 1, 2, 0], [2, 2, 1, 0], [0, 1, 2, 1], [-2, 0, 2], [2, 0, -2]]:
indices = np.array(case, dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert not isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(maybe_slice, indices)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
def test_maybe_indices_to_slice_right_edge(self):
target = np.arange(100)
# slice
for start in [0, 2, 5, 20, 97, 98]:
for step in [1, 2, 4]:
indices = np.arange(start, 99, step, dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
# reverse
indices = indices[::-1]
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
# not slice
indices = np.array([97, 98, 99, 100], dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert not isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(maybe_slice, indices)
msg = "index 100 is out of bounds for axis (0|1) with size 100"
with pytest.raises(IndexError, match=msg):
target[indices]
with pytest.raises(IndexError, match=msg):
target[maybe_slice]
indices = np.array([100, 99, 98, 97], dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert not isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(maybe_slice, indices)
with pytest.raises(IndexError, match=msg):
target[indices]
with pytest.raises(IndexError, match=msg):
target[maybe_slice]
for case in [[99, 97, 99, 96], [99, 99, 98, 97], [98, 98, 97, 96]]:
indices = np.array(case, dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert not isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(maybe_slice, indices)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
def test_maybe_indices_to_slice_both_edges(self):
target = np.arange(10)
# slice
for step in [1, 2, 4, 5, 8, 9]:
indices = np.arange(0, 9, step, dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
# reverse
indices = indices[::-1]
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
# not slice
for case in [[4, 2, 0, -2], [2, 2, 1, 0], [0, 1, 2, 1]]:
indices = np.array(case, dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert not isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(maybe_slice, indices)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
def test_maybe_indices_to_slice_middle(self):
target = np.arange(100)
# slice
for start, end in [(2, 10), (5, 25), (65, 97)]:
for step in [1, 2, 4, 20]:
indices = np.arange(start, end, step, dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
# reverse
indices = indices[::-1]
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
# not slice
for case in [[14, 12, 10, 12], [12, 12, 11, 10], [10, 11, 12, 11]]:
indices = np.array(case, dtype=np.intp)
maybe_slice = lib.maybe_indices_to_slice(indices, len(target))
assert not isinstance(maybe_slice, slice)
tm.assert_numpy_array_equal(maybe_slice, indices)
tm.assert_numpy_array_equal(target[indices], target[maybe_slice])
def test_maybe_booleans_to_slice(self):
arr = np.array([0, 0, 1, 1, 1, 0, 1], dtype=np.uint8)
result = lib.maybe_booleans_to_slice(arr)
assert result.dtype == np.bool_
result = lib.maybe_booleans_to_slice(arr[:0])
assert result == slice(0, 0)
def test_get_reverse_indexer(self):
indexer = np.array([-1, -1, 1, 2, 0, -1, 3, 4], dtype=np.intp)
result = lib.get_reverse_indexer(indexer, 5)
expected = np.array([4, 2, 3, 6, 7], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
def test_cache_readonly_preserve_docstrings():
# GH18197
assert Index.hasnans.__doc__ is not None
def test_no_default_pickle():
# GH#40397
obj = tm.round_trip_pickle(lib.no_default)
assert obj is lib.no_default