projektAI/venv/Lib/site-packages/pandas/tests/util/test_hashing.py

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2021-06-06 22:13:05 +02:00
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
from pandas.core.util.hashing import hash_tuples
from pandas.util import hash_array, hash_pandas_object
@pytest.fixture(
params=[
Series([1, 2, 3] * 3, dtype="int32"),
Series([None, 2.5, 3.5] * 3, dtype="float32"),
Series(["a", "b", "c"] * 3, dtype="category"),
Series(["d", "e", "f"] * 3),
Series([True, False, True] * 3),
Series(pd.date_range("20130101", periods=9)),
Series(pd.date_range("20130101", periods=9, tz="US/Eastern")),
Series(pd.timedelta_range("2000", periods=9)),
]
)
def series(request):
return request.param
@pytest.fixture(params=[True, False])
def index(request):
return request.param
def _check_equal(obj, **kwargs):
"""
Check that hashing an objects produces the same value each time.
Parameters
----------
obj : object
The object to hash.
kwargs : kwargs
Keyword arguments to pass to the hashing function.
"""
a = hash_pandas_object(obj, **kwargs)
b = hash_pandas_object(obj, **kwargs)
tm.assert_series_equal(a, b)
def _check_not_equal_with_index(obj):
"""
Check the hash of an object with and without its index is not the same.
Parameters
----------
obj : object
The object to hash.
"""
if not isinstance(obj, Index):
a = hash_pandas_object(obj, index=True)
b = hash_pandas_object(obj, index=False)
if len(obj):
assert not (a == b).all()
def test_consistency():
# Check that our hash doesn't change because of a mistake
# in the actual code; this is the ground truth.
result = hash_pandas_object(Index(["foo", "bar", "baz"]))
expected = Series(
np.array(
[3600424527151052760, 1374399572096150070, 477881037637427054],
dtype="uint64",
),
index=["foo", "bar", "baz"],
)
tm.assert_series_equal(result, expected)
def test_hash_array(series):
arr = series.values
tm.assert_numpy_array_equal(hash_array(arr), hash_array(arr))
@pytest.mark.parametrize(
"arr2", [np.array([3, 4, "All"]), np.array([3, 4, "All"], dtype=object)]
)
def test_hash_array_mixed(arr2):
result1 = hash_array(np.array(["3", "4", "All"]))
result2 = hash_array(arr2)
tm.assert_numpy_array_equal(result1, result2)
@pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")])
def test_hash_array_errors(val):
msg = "must pass a ndarray-like"
with pytest.raises(TypeError, match=msg):
hash_array(val)
def test_hash_tuples():
tuples = [(1, "one"), (1, "two"), (2, "one")]
result = hash_tuples(tuples)
expected = hash_pandas_object(MultiIndex.from_tuples(tuples)).values
tm.assert_numpy_array_equal(result, expected)
result = hash_tuples(tuples[0])
assert result == expected[0]
@pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")])
def test_hash_tuples_err(val):
msg = "must be convertible to a list-of-tuples"
with pytest.raises(TypeError, match=msg):
hash_tuples(val)
def test_multiindex_unique():
mi = MultiIndex.from_tuples([(118, 472), (236, 118), (51, 204), (102, 51)])
assert mi.is_unique is True
result = hash_pandas_object(mi)
assert result.is_unique is True
def test_multiindex_objects():
mi = MultiIndex(
levels=[["b", "d", "a"], [1, 2, 3]],
codes=[[0, 1, 0, 2], [2, 0, 0, 1]],
names=["col1", "col2"],
)
recons = mi._sort_levels_monotonic()
# These are equal.
assert mi.equals(recons)
assert Index(mi.values).equals(Index(recons.values))
@pytest.mark.parametrize(
"obj",
[
Series([1, 2, 3]),
Series([1.0, 1.5, 3.2]),
Series([1.0, 1.5, np.nan]),
Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
Series(["a", "b", "c"]),
Series(["a", np.nan, "c"]),
Series(["a", None, "c"]),
Series([True, False, True]),
Series(dtype=object),
Index([1, 2, 3]),
Index([True, False, True]),
DataFrame({"x": ["a", "b", "c"], "y": [1, 2, 3]}),
DataFrame(),
tm.makeMissingDataframe(),
tm.makeMixedDataFrame(),
tm.makeTimeDataFrame(),
tm.makeTimeSeries(),
tm.makeTimedeltaIndex(),
tm.makePeriodIndex(),
Series(tm.makePeriodIndex()),
Series(pd.date_range("20130101", periods=3, tz="US/Eastern")),
MultiIndex.from_product(
[range(5), ["foo", "bar", "baz"], pd.date_range("20130101", periods=2)]
),
MultiIndex.from_product([pd.CategoricalIndex(list("aabc")), range(3)]),
],
)
def test_hash_pandas_object(obj, index):
_check_equal(obj, index=index)
_check_not_equal_with_index(obj)
def test_hash_pandas_object2(series, index):
_check_equal(series, index=index)
_check_not_equal_with_index(series)
@pytest.mark.parametrize(
"obj", [Series([], dtype="float64"), Series([], dtype="object"), Index([])]
)
def test_hash_pandas_empty_object(obj, index):
# These are by-definition the same with
# or without the index as the data is empty.
_check_equal(obj, index=index)
@pytest.mark.parametrize(
"s1",
[
Series(["a", "b", "c", "d"]),
Series([1000, 2000, 3000, 4000]),
Series(pd.date_range(0, periods=4)),
],
)
@pytest.mark.parametrize("categorize", [True, False])
def test_categorical_consistency(s1, categorize):
# see gh-15143
#
# Check that categoricals hash consistent with their values,
# not codes. This should work for categoricals of any dtype.
s2 = s1.astype("category").cat.set_categories(s1)
s3 = s2.cat.set_categories(list(reversed(s1)))
# These should all hash identically.
h1 = hash_pandas_object(s1, categorize=categorize)
h2 = hash_pandas_object(s2, categorize=categorize)
h3 = hash_pandas_object(s3, categorize=categorize)
tm.assert_series_equal(h1, h2)
tm.assert_series_equal(h1, h3)
def test_categorical_with_nan_consistency():
c = pd.Categorical.from_codes(
[-1, 0, 1, 2, 3, 4], categories=pd.date_range("2012-01-01", periods=5, name="B")
)
expected = hash_array(c, categorize=False)
c = pd.Categorical.from_codes([-1, 0], categories=[pd.Timestamp("2012-01-01")])
result = hash_array(c, categorize=False)
assert result[0] in expected
assert result[1] in expected
@pytest.mark.parametrize("obj", [pd.Timestamp("20130101")])
def test_pandas_errors(obj):
msg = "Unexpected type for hashing"
with pytest.raises(TypeError, match=msg):
hash_pandas_object(obj)
def test_hash_keys():
# Using different hash keys, should have
# different hashes for the same data.
#
# This only matters for object dtypes.
obj = Series(list("abc"))
a = hash_pandas_object(obj, hash_key="9876543210123456")
b = hash_pandas_object(obj, hash_key="9876543210123465")
assert (a != b).all()
def test_invalid_key():
# This only matters for object dtypes.
msg = "key should be a 16-byte string encoded"
with pytest.raises(ValueError, match=msg):
hash_pandas_object(Series(list("abc")), hash_key="foo")
def test_already_encoded(index):
# If already encoded, then ok.
obj = Series(list("abc")).str.encode("utf8")
_check_equal(obj, index=index)
def test_alternate_encoding(index):
obj = Series(list("abc"))
_check_equal(obj, index=index, encoding="ascii")
@pytest.mark.parametrize("l_exp", range(8))
@pytest.mark.parametrize("l_add", [0, 1])
def test_same_len_hash_collisions(l_exp, l_add):
length = 2 ** (l_exp + 8) + l_add
s = tm.rands_array(length, 2)
result = hash_array(s, "utf8")
assert not result[0] == result[1]
def test_hash_collisions():
# Hash collisions are bad.
#
# https://github.com/pandas-dev/pandas/issues/14711#issuecomment-264885726
hashes = [
"Ingrid-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", # noqa: E501
"Tim-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", # noqa: E501
]
# These should be different.
result1 = hash_array(np.asarray(hashes[0:1], dtype=object), "utf8")
expected1 = np.array([14963968704024874985], dtype=np.uint64)
tm.assert_numpy_array_equal(result1, expected1)
result2 = hash_array(np.asarray(hashes[1:2], dtype=object), "utf8")
expected2 = np.array([16428432627716348016], dtype=np.uint64)
tm.assert_numpy_array_equal(result2, expected2)
result = hash_array(np.asarray(hashes, dtype=object), "utf8")
tm.assert_numpy_array_equal(result, np.concatenate([expected1, expected2], axis=0))
def test_hash_with_tuple():
# GH#28969 array containing a tuple raises on call to arr.astype(str)
# apparently a numpy bug github.com/numpy/numpy/issues/9441
df = DataFrame({"data": [tuple("1"), tuple("2")]})
result = hash_pandas_object(df)
expected = Series([10345501319357378243, 8331063931016360761], dtype=np.uint64)
tm.assert_series_equal(result, expected)
df2 = DataFrame({"data": [(1,), (2,)]})
result = hash_pandas_object(df2)
expected = Series([9408946347443669104, 3278256261030523334], dtype=np.uint64)
tm.assert_series_equal(result, expected)
# require that the elements of such tuples are themselves hashable
df3 = DataFrame(
{
"data": [
(
1,
[],
),
(
2,
{},
),
]
}
)
with pytest.raises(TypeError, match="unhashable type: 'list'"):
hash_pandas_object(df3)
def test_hash_object_none_key():
# https://github.com/pandas-dev/pandas/issues/30887
result = pd.util.hash_pandas_object(Series(["a", "b"]), hash_key=None)
expected = Series([4578374827886788867, 17338122309987883691], dtype="uint64")
tm.assert_series_equal(result, expected)