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)