from datetime import datetime from itertools import permutations import struct import numpy as np from numpy.random import RandomState import pytest from pandas._libs import algos as libalgos, groupby as libgroupby, hashtable as ht from pandas.compat.numpy import np_array_datetime64_compat import pandas.util._test_decorators as td from pandas.core.dtypes.common import ( is_bool_dtype, is_complex_dtype, is_float_dtype, is_integer_dtype, is_object_dtype, ) from pandas.core.dtypes.dtypes import CategoricalDtype as CDT import pandas as pd from pandas import ( Categorical, CategoricalIndex, DatetimeIndex, Index, IntervalIndex, Series, Timestamp, compat, ) import pandas._testing as tm from pandas.conftest import BYTES_DTYPES, STRING_DTYPES import pandas.core.algorithms as algos from pandas.core.arrays import DatetimeArray import pandas.core.common as com class TestFactorize: def test_basic(self): codes, uniques = algos.factorize(["a", "b", "b", "a", "a", "c", "c", "c"]) tm.assert_numpy_array_equal(uniques, np.array(["a", "b", "c"], dtype=object)) codes, uniques = algos.factorize( ["a", "b", "b", "a", "a", "c", "c", "c"], sort=True ) exp = np.array([0, 1, 1, 0, 0, 2, 2, 2], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = np.array(["a", "b", "c"], dtype=object) tm.assert_numpy_array_equal(uniques, exp) codes, uniques = algos.factorize(list(reversed(range(5)))) exp = np.array([0, 1, 2, 3, 4], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = np.array([4, 3, 2, 1, 0], dtype=np.int64) tm.assert_numpy_array_equal(uniques, exp) codes, uniques = algos.factorize(list(reversed(range(5))), sort=True) exp = np.array([4, 3, 2, 1, 0], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = np.array([0, 1, 2, 3, 4], dtype=np.int64) tm.assert_numpy_array_equal(uniques, exp) codes, uniques = algos.factorize(list(reversed(np.arange(5.0)))) exp = np.array([0, 1, 2, 3, 4], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = np.array([4.0, 3.0, 2.0, 1.0, 0.0], dtype=np.float64) tm.assert_numpy_array_equal(uniques, exp) codes, uniques = algos.factorize(list(reversed(np.arange(5.0))), sort=True) exp = np.array([4, 3, 2, 1, 0], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=np.float64) tm.assert_numpy_array_equal(uniques, exp) def test_mixed(self): # doc example reshaping.rst x = Series(["A", "A", np.nan, "B", 3.14, np.inf]) codes, uniques = algos.factorize(x) exp = np.array([0, 0, -1, 1, 2, 3], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = Index(["A", "B", 3.14, np.inf]) tm.assert_index_equal(uniques, exp) codes, uniques = algos.factorize(x, sort=True) exp = np.array([2, 2, -1, 3, 0, 1], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = Index([3.14, np.inf, "A", "B"]) tm.assert_index_equal(uniques, exp) def test_datelike(self): # M8 v1 = Timestamp("20130101 09:00:00.00004") v2 = Timestamp("20130101") x = Series([v1, v1, v1, v2, v2, v1]) codes, uniques = algos.factorize(x) exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = DatetimeIndex([v1, v2]) tm.assert_index_equal(uniques, exp) codes, uniques = algos.factorize(x, sort=True) exp = np.array([1, 1, 1, 0, 0, 1], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) exp = DatetimeIndex([v2, v1]) tm.assert_index_equal(uniques, exp) # period v1 = pd.Period("201302", freq="M") v2 = pd.Period("201303", freq="M") x = Series([v1, v1, v1, v2, v2, v1]) # periods are not 'sorted' as they are converted back into an index codes, uniques = algos.factorize(x) exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) tm.assert_index_equal(uniques, pd.PeriodIndex([v1, v2])) codes, uniques = algos.factorize(x, sort=True) exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) tm.assert_index_equal(uniques, pd.PeriodIndex([v1, v2])) # GH 5986 v1 = pd.to_timedelta("1 day 1 min") v2 = pd.to_timedelta("1 day") x = Series([v1, v2, v1, v1, v2, v2, v1]) codes, uniques = algos.factorize(x) exp = np.array([0, 1, 0, 0, 1, 1, 0], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) tm.assert_index_equal(uniques, pd.to_timedelta([v1, v2])) codes, uniques = algos.factorize(x, sort=True) exp = np.array([1, 0, 1, 1, 0, 0, 1], dtype=np.intp) tm.assert_numpy_array_equal(codes, exp) tm.assert_index_equal(uniques, pd.to_timedelta([v2, v1])) def test_factorize_nan(self): # nan should map to na_sentinel, not reverse_indexer[na_sentinel] # rizer.factorize should not raise an exception if na_sentinel indexes # outside of reverse_indexer key = np.array([1, 2, 1, np.nan], dtype="O") rizer = ht.Factorizer(len(key)) for na_sentinel in (-1, 20): ids = rizer.factorize(key, sort=True, na_sentinel=na_sentinel) expected = np.array([0, 1, 0, na_sentinel], dtype="int32") assert len(set(key)) == len(set(expected)) tm.assert_numpy_array_equal(pd.isna(key), expected == na_sentinel) # nan still maps to na_sentinel when sort=False key = np.array([0, np.nan, 1], dtype="O") na_sentinel = -1 # TODO(wesm): unused? ids = rizer.factorize(key, sort=False, na_sentinel=na_sentinel) # noqa expected = np.array([2, -1, 0], dtype="int32") assert len(set(key)) == len(set(expected)) tm.assert_numpy_array_equal(pd.isna(key), expected == na_sentinel) @pytest.mark.parametrize( "data, expected_codes, expected_uniques", [ ( [(1, 1), (1, 2), (0, 0), (1, 2), "nonsense"], [0, 1, 2, 1, 3], [(1, 1), (1, 2), (0, 0), "nonsense"], ), ( [(1, 1), (1, 2), (0, 0), (1, 2), (1, 2, 3)], [0, 1, 2, 1, 3], [(1, 1), (1, 2), (0, 0), (1, 2, 3)], ), ([(1, 1), (1, 2), (0, 0), (1, 2)], [0, 1, 2, 1], [(1, 1), (1, 2), (0, 0)]), ], ) def test_factorize_tuple_list(self, data, expected_codes, expected_uniques): # GH9454 codes, uniques = pd.factorize(data) tm.assert_numpy_array_equal(codes, np.array(expected_codes, dtype=np.intp)) expected_uniques_array = com.asarray_tuplesafe(expected_uniques, dtype=object) tm.assert_numpy_array_equal(uniques, expected_uniques_array) def test_complex_sorting(self): # gh 12666 - check no segfault x17 = np.array([complex(i) for i in range(17)], dtype=object) msg = ( "unorderable types: .* [<>] .*" "|" # the above case happens for numpy < 1.14 "'[<>]' not supported between instances of .*" ) with pytest.raises(TypeError, match=msg): algos.factorize(x17[::-1], sort=True) def test_float64_factorize(self, writable): data = np.array([1.0, 1e8, 1.0, 1e-8, 1e8, 1.0], dtype=np.float64) data.setflags(write=writable) expected_codes = np.array([0, 1, 0, 2, 1, 0], dtype=np.intp) expected_uniques = np.array([1.0, 1e8, 1e-8], dtype=np.float64) codes, uniques = algos.factorize(data) tm.assert_numpy_array_equal(codes, expected_codes) tm.assert_numpy_array_equal(uniques, expected_uniques) def test_uint64_factorize(self, writable): data = np.array([2 ** 64 - 1, 1, 2 ** 64 - 1], dtype=np.uint64) data.setflags(write=writable) expected_codes = np.array([0, 1, 0], dtype=np.intp) expected_uniques = np.array([2 ** 64 - 1, 1], dtype=np.uint64) codes, uniques = algos.factorize(data) tm.assert_numpy_array_equal(codes, expected_codes) tm.assert_numpy_array_equal(uniques, expected_uniques) def test_int64_factorize(self, writable): data = np.array([2 ** 63 - 1, -(2 ** 63), 2 ** 63 - 1], dtype=np.int64) data.setflags(write=writable) expected_codes = np.array([0, 1, 0], dtype=np.intp) expected_uniques = np.array([2 ** 63 - 1, -(2 ** 63)], dtype=np.int64) codes, uniques = algos.factorize(data) tm.assert_numpy_array_equal(codes, expected_codes) tm.assert_numpy_array_equal(uniques, expected_uniques) def test_string_factorize(self, writable): data = np.array(["a", "c", "a", "b", "c"], dtype=object) data.setflags(write=writable) expected_codes = np.array([0, 1, 0, 2, 1], dtype=np.intp) expected_uniques = np.array(["a", "c", "b"], dtype=object) codes, uniques = algos.factorize(data) tm.assert_numpy_array_equal(codes, expected_codes) tm.assert_numpy_array_equal(uniques, expected_uniques) def test_object_factorize(self, writable): data = np.array(["a", "c", None, np.nan, "a", "b", pd.NaT, "c"], dtype=object) data.setflags(write=writable) expected_codes = np.array([0, 1, -1, -1, 0, 2, -1, 1], dtype=np.intp) expected_uniques = np.array(["a", "c", "b"], dtype=object) codes, uniques = algos.factorize(data) tm.assert_numpy_array_equal(codes, expected_codes) tm.assert_numpy_array_equal(uniques, expected_uniques) def test_deprecate_order(self): # gh 19727 - check warning is raised for deprecated keyword, order. # Test not valid once order keyword is removed. data = np.array([2 ** 63, 1, 2 ** 63], dtype=np.uint64) with pytest.raises(TypeError, match="got an unexpected keyword"): algos.factorize(data, order=True) with tm.assert_produces_warning(False): algos.factorize(data) @pytest.mark.parametrize( "data", [ np.array([0, 1, 0], dtype="u8"), np.array([-(2 ** 63), 1, -(2 ** 63)], dtype="i8"), np.array(["__nan__", "foo", "__nan__"], dtype="object"), ], ) def test_parametrized_factorize_na_value_default(self, data): # arrays that include the NA default for that type, but isn't used. codes, uniques = algos.factorize(data) expected_uniques = data[[0, 1]] expected_codes = np.array([0, 1, 0], dtype=np.intp) tm.assert_numpy_array_equal(codes, expected_codes) tm.assert_numpy_array_equal(uniques, expected_uniques) @pytest.mark.parametrize( "data, na_value", [ (np.array([0, 1, 0, 2], dtype="u8"), 0), (np.array([1, 0, 1, 2], dtype="u8"), 1), (np.array([-(2 ** 63), 1, -(2 ** 63), 0], dtype="i8"), -(2 ** 63)), (np.array([1, -(2 ** 63), 1, 0], dtype="i8"), 1), (np.array(["a", "", "a", "b"], dtype=object), "a"), (np.array([(), ("a", 1), (), ("a", 2)], dtype=object), ()), (np.array([("a", 1), (), ("a", 1), ("a", 2)], dtype=object), ("a", 1)), ], ) def test_parametrized_factorize_na_value(self, data, na_value): codes, uniques = algos._factorize_array(data, na_value=na_value) expected_uniques = data[[1, 3]] expected_codes = np.array([-1, 0, -1, 1], dtype=np.intp) tm.assert_numpy_array_equal(codes, expected_codes) tm.assert_numpy_array_equal(uniques, expected_uniques) @pytest.mark.parametrize("sort", [True, False]) @pytest.mark.parametrize("na_sentinel", [-1, -10, 100]) @pytest.mark.parametrize( "data, uniques", [ ( np.array(["b", "a", None, "b"], dtype=object), np.array(["b", "a"], dtype=object), ), ( pd.array([2, 1, np.nan, 2], dtype="Int64"), pd.array([2, 1], dtype="Int64"), ), ], ids=["numpy_array", "extension_array"], ) def test_factorize_na_sentinel(self, sort, na_sentinel, data, uniques): codes, uniques = algos.factorize(data, sort=sort, na_sentinel=na_sentinel) if sort: expected_codes = np.array([1, 0, na_sentinel, 1], dtype=np.intp) expected_uniques = algos.safe_sort(uniques) else: expected_codes = np.array([0, 1, na_sentinel, 0], dtype=np.intp) expected_uniques = uniques tm.assert_numpy_array_equal(codes, expected_codes) if isinstance(data, np.ndarray): tm.assert_numpy_array_equal(uniques, expected_uniques) else: tm.assert_extension_array_equal(uniques, expected_uniques) class TestUnique: def test_ints(self): arr = np.random.randint(0, 100, size=50) result = algos.unique(arr) assert isinstance(result, np.ndarray) def test_objects(self): arr = np.random.randint(0, 100, size=50).astype("O") result = algos.unique(arr) assert isinstance(result, np.ndarray) def test_object_refcount_bug(self): lst = ["A", "B", "C", "D", "E"] for i in range(1000): len(algos.unique(lst)) def test_on_index_object(self): mindex = pd.MultiIndex.from_arrays( [np.arange(5).repeat(5), np.tile(np.arange(5), 5)] ) expected = mindex.values expected.sort() mindex = mindex.repeat(2) result = pd.unique(mindex) result.sort() tm.assert_almost_equal(result, expected) def test_dtype_preservation(self, any_numpy_dtype): # GH 15442 if any_numpy_dtype in (BYTES_DTYPES + STRING_DTYPES): pytest.skip("skip string dtype") elif is_integer_dtype(any_numpy_dtype): data = [1, 2, 2] uniques = [1, 2] elif is_float_dtype(any_numpy_dtype): data = [1, 2, 2] uniques = [1.0, 2.0] elif is_complex_dtype(any_numpy_dtype): data = [complex(1, 0), complex(2, 0), complex(2, 0)] uniques = [complex(1, 0), complex(2, 0)] elif is_bool_dtype(any_numpy_dtype): data = [True, True, False] uniques = [True, False] elif is_object_dtype(any_numpy_dtype): data = ["A", "B", "B"] uniques = ["A", "B"] else: # datetime64[ns]/M8[ns]/timedelta64[ns]/m8[ns] tested elsewhere data = [1, 2, 2] uniques = [1, 2] result = Series(data, dtype=any_numpy_dtype).unique() expected = np.array(uniques, dtype=any_numpy_dtype) tm.assert_numpy_array_equal(result, expected) def test_datetime64_dtype_array_returned(self): # GH 9431 expected = np_array_datetime64_compat( [ "2015-01-03T00:00:00.000000000+0000", "2015-01-01T00:00:00.000000000+0000", ], dtype="M8[ns]", ) dt_index = pd.to_datetime( [ "2015-01-03T00:00:00.000000000", "2015-01-01T00:00:00.000000000", "2015-01-01T00:00:00.000000000", ] ) result = algos.unique(dt_index) tm.assert_numpy_array_equal(result, expected) assert result.dtype == expected.dtype s = Series(dt_index) result = algos.unique(s) tm.assert_numpy_array_equal(result, expected) assert result.dtype == expected.dtype arr = s.values result = algos.unique(arr) tm.assert_numpy_array_equal(result, expected) assert result.dtype == expected.dtype def test_datetime_non_ns(self): a = np.array(["2000", "2000", "2001"], dtype="datetime64[s]") result = pd.unique(a) expected = np.array(["2000", "2001"], dtype="datetime64[ns]") tm.assert_numpy_array_equal(result, expected) def test_timedelta_non_ns(self): a = np.array(["2000", "2000", "2001"], dtype="timedelta64[s]") result = pd.unique(a) expected = np.array([2000000000000, 2001000000000], dtype="timedelta64[ns]") tm.assert_numpy_array_equal(result, expected) def test_timedelta64_dtype_array_returned(self): # GH 9431 expected = np.array([31200, 45678, 10000], dtype="m8[ns]") td_index = pd.to_timedelta([31200, 45678, 31200, 10000, 45678]) result = algos.unique(td_index) tm.assert_numpy_array_equal(result, expected) assert result.dtype == expected.dtype s = Series(td_index) result = algos.unique(s) tm.assert_numpy_array_equal(result, expected) assert result.dtype == expected.dtype arr = s.values result = algos.unique(arr) tm.assert_numpy_array_equal(result, expected) assert result.dtype == expected.dtype def test_uint64_overflow(self): s = Series([1, 2, 2 ** 63, 2 ** 63], dtype=np.uint64) exp = np.array([1, 2, 2 ** 63], dtype=np.uint64) tm.assert_numpy_array_equal(algos.unique(s), exp) def test_nan_in_object_array(self): duplicated_items = ["a", np.nan, "c", "c"] result = pd.unique(duplicated_items) expected = np.array(["a", np.nan, "c"], dtype=object) tm.assert_numpy_array_equal(result, expected) def test_categorical(self): # we are expecting to return in the order # of appearance expected = Categorical(list("bac"), categories=list("bac")) # we are expecting to return in the order # of the categories expected_o = Categorical(list("bac"), categories=list("abc"), ordered=True) # GH 15939 c = Categorical(list("baabc")) result = c.unique() tm.assert_categorical_equal(result, expected) result = algos.unique(c) tm.assert_categorical_equal(result, expected) c = Categorical(list("baabc"), ordered=True) result = c.unique() tm.assert_categorical_equal(result, expected_o) result = algos.unique(c) tm.assert_categorical_equal(result, expected_o) # Series of categorical dtype s = Series(Categorical(list("baabc")), name="foo") result = s.unique() tm.assert_categorical_equal(result, expected) result = pd.unique(s) tm.assert_categorical_equal(result, expected) # CI -> return CI ci = CategoricalIndex(Categorical(list("baabc"), categories=list("bac"))) expected = CategoricalIndex(expected) result = ci.unique() tm.assert_index_equal(result, expected) result = pd.unique(ci) tm.assert_index_equal(result, expected) def test_datetime64tz_aware(self): # GH 15939 result = Series( Index( [ Timestamp("20160101", tz="US/Eastern"), Timestamp("20160101", tz="US/Eastern"), ] ) ).unique() expected = DatetimeArray._from_sequence( np.array([Timestamp("2016-01-01 00:00:00-0500", tz="US/Eastern")]) ) tm.assert_extension_array_equal(result, expected) result = Index( [ Timestamp("20160101", tz="US/Eastern"), Timestamp("20160101", tz="US/Eastern"), ] ).unique() expected = DatetimeIndex( ["2016-01-01 00:00:00"], dtype="datetime64[ns, US/Eastern]", freq=None ) tm.assert_index_equal(result, expected) result = pd.unique( Series( Index( [ Timestamp("20160101", tz="US/Eastern"), Timestamp("20160101", tz="US/Eastern"), ] ) ) ) expected = DatetimeArray._from_sequence( np.array([Timestamp("2016-01-01", tz="US/Eastern")]) ) tm.assert_extension_array_equal(result, expected) result = pd.unique( Index( [ Timestamp("20160101", tz="US/Eastern"), Timestamp("20160101", tz="US/Eastern"), ] ) ) expected = DatetimeIndex( ["2016-01-01 00:00:00"], dtype="datetime64[ns, US/Eastern]", freq=None ) tm.assert_index_equal(result, expected) def test_order_of_appearance(self): # 9346 # light testing of guarantee of order of appearance # these also are the doc-examples result = pd.unique(Series([2, 1, 3, 3])) tm.assert_numpy_array_equal(result, np.array([2, 1, 3], dtype="int64")) result = pd.unique(Series([2] + [1] * 5)) tm.assert_numpy_array_equal(result, np.array([2, 1], dtype="int64")) result = pd.unique(Series([Timestamp("20160101"), Timestamp("20160101")])) expected = np.array(["2016-01-01T00:00:00.000000000"], dtype="datetime64[ns]") tm.assert_numpy_array_equal(result, expected) result = pd.unique( Index( [ Timestamp("20160101", tz="US/Eastern"), Timestamp("20160101", tz="US/Eastern"), ] ) ) expected = DatetimeIndex( ["2016-01-01 00:00:00"], dtype="datetime64[ns, US/Eastern]", freq=None ) tm.assert_index_equal(result, expected) result = pd.unique(list("aabc")) expected = np.array(["a", "b", "c"], dtype=object) tm.assert_numpy_array_equal(result, expected) result = pd.unique(Series(Categorical(list("aabc")))) expected = Categorical(list("abc")) tm.assert_categorical_equal(result, expected) @pytest.mark.parametrize( "arg ,expected", [ (("1", "1", "2"), np.array(["1", "2"], dtype=object)), (("foo",), np.array(["foo"], dtype=object)), ], ) def test_tuple_with_strings(self, arg, expected): # see GH 17108 result = pd.unique(arg) tm.assert_numpy_array_equal(result, expected) def test_obj_none_preservation(self): # GH 20866 arr = np.array(["foo", None], dtype=object) result = pd.unique(arr) expected = np.array(["foo", None], dtype=object) tm.assert_numpy_array_equal(result, expected, strict_nan=True) def test_signed_zero(self): # GH 21866 a = np.array([-0.0, 0.0]) result = pd.unique(a) expected = np.array([-0.0]) # 0.0 and -0.0 are equivalent tm.assert_numpy_array_equal(result, expected) def test_different_nans(self): # GH 21866 # create different nans from bit-patterns: NAN1 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000000))[0] NAN2 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000001))[0] assert NAN1 != NAN1 assert NAN2 != NAN2 a = np.array([NAN1, NAN2]) # NAN1 and NAN2 are equivalent result = pd.unique(a) expected = np.array([np.nan]) tm.assert_numpy_array_equal(result, expected) def test_first_nan_kept(self): # GH 22295 # create different nans from bit-patterns: bits_for_nan1 = 0xFFF8000000000001 bits_for_nan2 = 0x7FF8000000000001 NAN1 = struct.unpack("d", struct.pack("=Q", bits_for_nan1))[0] NAN2 = struct.unpack("d", struct.pack("=Q", bits_for_nan2))[0] assert NAN1 != NAN1 assert NAN2 != NAN2 for el_type in [np.float64, np.object]: a = np.array([NAN1, NAN2], dtype=el_type) result = pd.unique(a) assert result.size == 1 # use bit patterns to identify which nan was kept: result_nan_bits = struct.unpack("=Q", struct.pack("d", result[0]))[0] assert result_nan_bits == bits_for_nan1 def test_do_not_mangle_na_values(self, unique_nulls_fixture, unique_nulls_fixture2): # GH 22295 if unique_nulls_fixture is unique_nulls_fixture2: return # skip it, values not unique a = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=np.object) result = pd.unique(a) assert result.size == 2 assert a[0] is unique_nulls_fixture assert a[1] is unique_nulls_fixture2 class TestIsin: def test_invalid(self): msg = ( r"only list-like objects are allowed to be passed to isin\(\)," r" you passed a \[int\]" ) with pytest.raises(TypeError, match=msg): algos.isin(1, 1) with pytest.raises(TypeError, match=msg): algos.isin(1, [1]) with pytest.raises(TypeError, match=msg): algos.isin([1], 1) def test_basic(self): result = algos.isin([1, 2], [1]) expected = np.array([True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(np.array([1, 2]), [1]) expected = np.array([True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(Series([1, 2]), [1]) expected = np.array([True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(Series([1, 2]), Series([1])) expected = np.array([True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(Series([1, 2]), {1}) expected = np.array([True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(["a", "b"], ["a"]) expected = np.array([True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(Series(["a", "b"]), Series(["a"])) expected = np.array([True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(Series(["a", "b"]), {"a"}) expected = np.array([True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(["a", "b"], [1]) expected = np.array([False, False]) tm.assert_numpy_array_equal(result, expected) def test_i8(self): arr = pd.date_range("20130101", periods=3).values result = algos.isin(arr, [arr[0]]) expected = np.array([True, False, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(arr, arr[0:2]) expected = np.array([True, True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(arr, set(arr[0:2])) expected = np.array([True, True, False]) tm.assert_numpy_array_equal(result, expected) arr = pd.timedelta_range("1 day", periods=3).values result = algos.isin(arr, [arr[0]]) expected = np.array([True, False, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(arr, arr[0:2]) expected = np.array([True, True, False]) tm.assert_numpy_array_equal(result, expected) result = algos.isin(arr, set(arr[0:2])) expected = np.array([True, True, False]) tm.assert_numpy_array_equal(result, expected) def test_large(self): s = pd.date_range("20000101", periods=2000000, freq="s").values result = algos.isin(s, s[0:2]) expected = np.zeros(len(s), dtype=bool) expected[0] = True expected[1] = True tm.assert_numpy_array_equal(result, expected) def test_categorical_from_codes(self): # GH 16639 vals = np.array([0, 1, 2, 0]) cats = ["a", "b", "c"] Sd = Series(Categorical(1).from_codes(vals, cats)) St = Series(Categorical(1).from_codes(np.array([0, 1]), cats)) expected = np.array([True, True, False, True]) result = algos.isin(Sd, St) tm.assert_numpy_array_equal(expected, result) def test_same_nan_is_in(self): # GH 22160 # nan is special, because from " a is b" doesn't follow "a == b" # at least, isin() should follow python's "np.nan in [nan] == True" # casting to -> np.float64 -> another float-object somewhere on # the way could lead jepardize this behavior comps = [np.nan] # could be casted to float64 values = [np.nan] expected = np.array([True]) result = algos.isin(comps, values) tm.assert_numpy_array_equal(expected, result) def test_same_object_is_in(self): # GH 22160 # there could be special treatment for nans # the user however could define a custom class # with similar behavior, then we at least should # fall back to usual python's behavior: "a in [a] == True" class LikeNan: def __eq__(self, other) -> bool: return False def __hash__(self): return 0 a, b = LikeNan(), LikeNan() # same object -> True tm.assert_numpy_array_equal(algos.isin([a], [a]), np.array([True])) # different objects -> False tm.assert_numpy_array_equal(algos.isin([a], [b]), np.array([False])) def test_different_nans(self): # GH 22160 # all nans are handled as equivalent comps = [float("nan")] values = [float("nan")] assert comps[0] is not values[0] # different nan-objects # as list of python-objects: result = algos.isin(comps, values) tm.assert_numpy_array_equal(np.array([True]), result) # as object-array: result = algos.isin( np.asarray(comps, dtype=np.object), np.asarray(values, dtype=np.object) ) tm.assert_numpy_array_equal(np.array([True]), result) # as float64-array: result = algos.isin( np.asarray(comps, dtype=np.float64), np.asarray(values, dtype=np.float64) ) tm.assert_numpy_array_equal(np.array([True]), result) def test_no_cast(self): # GH 22160 # ensure 42 is not casted to a string comps = ["ss", 42] values = ["42"] expected = np.array([False, False]) result = algos.isin(comps, values) tm.assert_numpy_array_equal(expected, result) @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])]) def test_empty(self, empty): # see gh-16991 vals = Index(["a", "b"]) expected = np.array([False, False]) result = algos.isin(vals, empty) tm.assert_numpy_array_equal(expected, result) def test_different_nan_objects(self): # GH 22119 comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=np.object) vals = np.array([float("nan")], dtype=np.object) expected = np.array([False, False, True]) result = algos.isin(comps, vals) tm.assert_numpy_array_equal(expected, result) def test_different_nans_as_float64(self): # GH 21866 # create different nans from bit-patterns, # these nans will land in different buckets in the hash-table # if no special care is taken NAN1 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000000))[0] NAN2 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000001))[0] assert NAN1 != NAN1 assert NAN2 != NAN2 # check that NAN1 and NAN2 are equivalent: arr = np.array([NAN1, NAN2], dtype=np.float64) lookup1 = np.array([NAN1], dtype=np.float64) result = algos.isin(arr, lookup1) expected = np.array([True, True]) tm.assert_numpy_array_equal(result, expected) lookup2 = np.array([NAN2], dtype=np.float64) result = algos.isin(arr, lookup2) expected = np.array([True, True]) tm.assert_numpy_array_equal(result, expected) class TestValueCounts: def test_value_counts(self): np.random.seed(1234) from pandas.core.reshape.tile import cut arr = np.random.randn(4) factor = cut(arr, 4) # assert isinstance(factor, n) result = algos.value_counts(factor) breaks = [-1.194, -0.535, 0.121, 0.777, 1.433] index = IntervalIndex.from_breaks(breaks).astype(CDT(ordered=True)) expected = Series([1, 1, 1, 1], index=index) tm.assert_series_equal(result.sort_index(), expected.sort_index()) def test_value_counts_bins(self): s = [1, 2, 3, 4] result = algos.value_counts(s, bins=1) expected = Series([4], index=IntervalIndex.from_tuples([(0.996, 4.0)])) tm.assert_series_equal(result, expected) result = algos.value_counts(s, bins=2, sort=False) expected = Series( [2, 2], index=IntervalIndex.from_tuples([(0.996, 2.5), (2.5, 4.0)]) ) tm.assert_series_equal(result, expected) def test_value_counts_dtypes(self): result = algos.value_counts([1, 1.0]) assert len(result) == 1 result = algos.value_counts([1, 1.0], bins=1) assert len(result) == 1 result = algos.value_counts(Series([1, 1.0, "1"])) # object assert len(result) == 2 msg = "bins argument only works with numeric data" with pytest.raises(TypeError, match=msg): algos.value_counts(["1", 1], bins=1) def test_value_counts_nat(self): td = Series([np.timedelta64(10000), pd.NaT], dtype="timedelta64[ns]") dt = pd.to_datetime(["NaT", "2014-01-01"]) for s in [td, dt]: vc = algos.value_counts(s) vc_with_na = algos.value_counts(s, dropna=False) assert len(vc) == 1 assert len(vc_with_na) == 2 exp_dt = Series({Timestamp("2014-01-01 00:00:00"): 1}) tm.assert_series_equal(algos.value_counts(dt), exp_dt) # TODO same for (timedelta) def test_value_counts_datetime_outofbounds(self): # GH 13663 s = Series( [ datetime(3000, 1, 1), datetime(5000, 1, 1), datetime(5000, 1, 1), datetime(6000, 1, 1), datetime(3000, 1, 1), datetime(3000, 1, 1), ] ) res = s.value_counts() exp_index = Index( [datetime(3000, 1, 1), datetime(5000, 1, 1), datetime(6000, 1, 1)], dtype=object, ) exp = Series([3, 2, 1], index=exp_index) tm.assert_series_equal(res, exp) # GH 12424 res = pd.to_datetime(Series(["2362-01-01", np.nan]), errors="ignore") exp = Series(["2362-01-01", np.nan], dtype=object) tm.assert_series_equal(res, exp) def test_categorical(self): s = Series(Categorical(list("aaabbc"))) result = s.value_counts() expected = Series([3, 2, 1], index=CategoricalIndex(["a", "b", "c"])) tm.assert_series_equal(result, expected, check_index_type=True) # preserve order? s = s.cat.as_ordered() result = s.value_counts() expected.index = expected.index.as_ordered() tm.assert_series_equal(result, expected, check_index_type=True) def test_categorical_nans(self): s = Series(Categorical(list("aaaaabbbcc"))) # 4,3,2,1 (nan) s.iloc[1] = np.nan result = s.value_counts() expected = Series( [4, 3, 2], index=CategoricalIndex(["a", "b", "c"], categories=["a", "b", "c"]), ) tm.assert_series_equal(result, expected, check_index_type=True) result = s.value_counts(dropna=False) expected = Series([4, 3, 2, 1], index=CategoricalIndex(["a", "b", "c", np.nan])) tm.assert_series_equal(result, expected, check_index_type=True) # out of order s = Series( Categorical(list("aaaaabbbcc"), ordered=True, categories=["b", "a", "c"]) ) s.iloc[1] = np.nan result = s.value_counts() expected = Series( [4, 3, 2], index=CategoricalIndex( ["a", "b", "c"], categories=["b", "a", "c"], ordered=True ), ) tm.assert_series_equal(result, expected, check_index_type=True) result = s.value_counts(dropna=False) expected = Series( [4, 3, 2, 1], index=CategoricalIndex( ["a", "b", "c", np.nan], categories=["b", "a", "c"], ordered=True ), ) tm.assert_series_equal(result, expected, check_index_type=True) def test_categorical_zeroes(self): # keep the `d` category with 0 s = Series(Categorical(list("bbbaac"), categories=list("abcd"), ordered=True)) result = s.value_counts() expected = Series( [3, 2, 1, 0], index=Categorical( ["b", "a", "c", "d"], categories=list("abcd"), ordered=True ), ) tm.assert_series_equal(result, expected, check_index_type=True) def test_dropna(self): # https://github.com/pandas-dev/pandas/issues/9443#issuecomment-73719328 tm.assert_series_equal( Series([True, True, False]).value_counts(dropna=True), Series([2, 1], index=[True, False]), ) tm.assert_series_equal( Series([True, True, False]).value_counts(dropna=False), Series([2, 1], index=[True, False]), ) tm.assert_series_equal( Series([True, True, False, None]).value_counts(dropna=True), Series([2, 1], index=[True, False]), ) tm.assert_series_equal( Series([True, True, False, None]).value_counts(dropna=False), Series([2, 1, 1], index=[True, False, np.nan]), ) tm.assert_series_equal( Series([10.3, 5.0, 5.0]).value_counts(dropna=True), Series([2, 1], index=[5.0, 10.3]), ) tm.assert_series_equal( Series([10.3, 5.0, 5.0]).value_counts(dropna=False), Series([2, 1], index=[5.0, 10.3]), ) tm.assert_series_equal( Series([10.3, 5.0, 5.0, None]).value_counts(dropna=True), Series([2, 1], index=[5.0, 10.3]), ) # 32-bit linux has a different ordering if not compat.is_platform_32bit(): result = Series([10.3, 5.0, 5.0, None]).value_counts(dropna=False) expected = Series([2, 1, 1], index=[5.0, 10.3, np.nan]) tm.assert_series_equal(result, expected) def test_value_counts_normalized(self): # GH12558 s = Series([1, 2, np.nan, np.nan, np.nan]) dtypes = (np.float64, np.object, "M8[ns]") for t in dtypes: s_typed = s.astype(t) result = s_typed.value_counts(normalize=True, dropna=False) expected = Series( [0.6, 0.2, 0.2], index=Series([np.nan, 2.0, 1.0], dtype=t) ) tm.assert_series_equal(result, expected) result = s_typed.value_counts(normalize=True, dropna=True) expected = Series([0.5, 0.5], index=Series([2.0, 1.0], dtype=t)) tm.assert_series_equal(result, expected) def test_value_counts_uint64(self): arr = np.array([2 ** 63], dtype=np.uint64) expected = Series([1], index=[2 ** 63]) result = algos.value_counts(arr) tm.assert_series_equal(result, expected) arr = np.array([-1, 2 ** 63], dtype=object) expected = Series([1, 1], index=[-1, 2 ** 63]) result = algos.value_counts(arr) # 32-bit linux has a different ordering if not compat.is_platform_32bit(): tm.assert_series_equal(result, expected) class TestDuplicated: def test_duplicated_with_nas(self): keys = np.array([0, 1, np.nan, 0, 2, np.nan], dtype=object) result = algos.duplicated(keys) expected = np.array([False, False, False, True, False, True]) tm.assert_numpy_array_equal(result, expected) result = algos.duplicated(keys, keep="first") expected = np.array([False, False, False, True, False, True]) tm.assert_numpy_array_equal(result, expected) result = algos.duplicated(keys, keep="last") expected = np.array([True, False, True, False, False, False]) tm.assert_numpy_array_equal(result, expected) result = algos.duplicated(keys, keep=False) expected = np.array([True, False, True, True, False, True]) tm.assert_numpy_array_equal(result, expected) keys = np.empty(8, dtype=object) for i, t in enumerate( zip([0, 0, np.nan, np.nan] * 2, [0, np.nan, 0, np.nan] * 2) ): keys[i] = t result = algos.duplicated(keys) falses = [False] * 4 trues = [True] * 4 expected = np.array(falses + trues) tm.assert_numpy_array_equal(result, expected) result = algos.duplicated(keys, keep="last") expected = np.array(trues + falses) tm.assert_numpy_array_equal(result, expected) result = algos.duplicated(keys, keep=False) expected = np.array(trues + trues) tm.assert_numpy_array_equal(result, expected) @pytest.mark.parametrize( "case", [ np.array([1, 2, 1, 5, 3, 2, 4, 1, 5, 6]), np.array([1.1, 2.2, 1.1, np.nan, 3.3, 2.2, 4.4, 1.1, np.nan, 6.6]), np.array( [ 1 + 1j, 2 + 2j, 1 + 1j, 5 + 5j, 3 + 3j, 2 + 2j, 4 + 4j, 1 + 1j, 5 + 5j, 6 + 6j, ] ), np.array(["a", "b", "a", "e", "c", "b", "d", "a", "e", "f"], dtype=object), np.array( [1, 2 ** 63, 1, 3 ** 5, 10, 2 ** 63, 39, 1, 3 ** 5, 7], dtype=np.uint64 ), ], ) def test_numeric_object_likes(self, case): exp_first = np.array( [False, False, True, False, False, True, False, True, True, False] ) exp_last = np.array( [True, True, True, True, False, False, False, False, False, False] ) exp_false = exp_first | exp_last res_first = algos.duplicated(case, keep="first") tm.assert_numpy_array_equal(res_first, exp_first) res_last = algos.duplicated(case, keep="last") tm.assert_numpy_array_equal(res_last, exp_last) res_false = algos.duplicated(case, keep=False) tm.assert_numpy_array_equal(res_false, exp_false) # index for idx in [Index(case), Index(case, dtype="category")]: res_first = idx.duplicated(keep="first") tm.assert_numpy_array_equal(res_first, exp_first) res_last = idx.duplicated(keep="last") tm.assert_numpy_array_equal(res_last, exp_last) res_false = idx.duplicated(keep=False) tm.assert_numpy_array_equal(res_false, exp_false) # series for s in [Series(case), Series(case, dtype="category")]: res_first = s.duplicated(keep="first") tm.assert_series_equal(res_first, Series(exp_first)) res_last = s.duplicated(keep="last") tm.assert_series_equal(res_last, Series(exp_last)) res_false = s.duplicated(keep=False) tm.assert_series_equal(res_false, Series(exp_false)) def test_datetime_likes(self): dt = [ "2011-01-01", "2011-01-02", "2011-01-01", "NaT", "2011-01-03", "2011-01-02", "2011-01-04", "2011-01-01", "NaT", "2011-01-06", ] td = [ "1 days", "2 days", "1 days", "NaT", "3 days", "2 days", "4 days", "1 days", "NaT", "6 days", ] cases = [ np.array([Timestamp(d) for d in dt]), np.array([Timestamp(d, tz="US/Eastern") for d in dt]), np.array([pd.Period(d, freq="D") for d in dt]), np.array([np.datetime64(d) for d in dt]), np.array([pd.Timedelta(d) for d in td]), ] exp_first = np.array( [False, False, True, False, False, True, False, True, True, False] ) exp_last = np.array( [True, True, True, True, False, False, False, False, False, False] ) exp_false = exp_first | exp_last for case in cases: res_first = algos.duplicated(case, keep="first") tm.assert_numpy_array_equal(res_first, exp_first) res_last = algos.duplicated(case, keep="last") tm.assert_numpy_array_equal(res_last, exp_last) res_false = algos.duplicated(case, keep=False) tm.assert_numpy_array_equal(res_false, exp_false) # index for idx in [ Index(case), Index(case, dtype="category"), Index(case, dtype=object), ]: res_first = idx.duplicated(keep="first") tm.assert_numpy_array_equal(res_first, exp_first) res_last = idx.duplicated(keep="last") tm.assert_numpy_array_equal(res_last, exp_last) res_false = idx.duplicated(keep=False) tm.assert_numpy_array_equal(res_false, exp_false) # series for s in [ Series(case), Series(case, dtype="category"), Series(case, dtype=object), ]: res_first = s.duplicated(keep="first") tm.assert_series_equal(res_first, Series(exp_first)) res_last = s.duplicated(keep="last") tm.assert_series_equal(res_last, Series(exp_last)) res_false = s.duplicated(keep=False) tm.assert_series_equal(res_false, Series(exp_false)) def test_unique_index(self): cases = [Index([1, 2, 3]), pd.RangeIndex(0, 3)] for case in cases: assert case.is_unique is True tm.assert_numpy_array_equal( case.duplicated(), np.array([False, False, False]) ) @pytest.mark.parametrize( "arr, unique", [ ( [(0, 0), (0, 1), (1, 0), (1, 1), (0, 0), (0, 1), (1, 0), (1, 1)], [(0, 0), (0, 1), (1, 0), (1, 1)], ), ( [("b", "c"), ("a", "b"), ("a", "b"), ("b", "c")], [("b", "c"), ("a", "b")], ), ([("a", 1), ("b", 2), ("a", 3), ("a", 1)], [("a", 1), ("b", 2), ("a", 3)]), ], ) def test_unique_tuples(self, arr, unique): # https://github.com/pandas-dev/pandas/issues/16519 expected = np.empty(len(unique), dtype=object) expected[:] = unique result = pd.unique(arr) tm.assert_numpy_array_equal(result, expected) class GroupVarTestMixin: def test_group_var_generic_1d(self): prng = RandomState(1234) out = (np.nan * np.ones((5, 1))).astype(self.dtype) counts = np.zeros(5, dtype="int64") values = 10 * prng.rand(15, 1).astype(self.dtype) labels = np.tile(np.arange(5), (3,)).astype("int64") expected_out = ( np.squeeze(values).reshape((5, 3), order="F").std(axis=1, ddof=1) ** 2 )[:, np.newaxis] expected_counts = counts + 3 self.algo(out, counts, values, labels) assert np.allclose(out, expected_out, self.rtol) tm.assert_numpy_array_equal(counts, expected_counts) def test_group_var_generic_1d_flat_labels(self): prng = RandomState(1234) out = (np.nan * np.ones((1, 1))).astype(self.dtype) counts = np.zeros(1, dtype="int64") values = 10 * prng.rand(5, 1).astype(self.dtype) labels = np.zeros(5, dtype="int64") expected_out = np.array([[values.std(ddof=1) ** 2]]) expected_counts = counts + 5 self.algo(out, counts, values, labels) assert np.allclose(out, expected_out, self.rtol) tm.assert_numpy_array_equal(counts, expected_counts) def test_group_var_generic_2d_all_finite(self): prng = RandomState(1234) out = (np.nan * np.ones((5, 2))).astype(self.dtype) counts = np.zeros(5, dtype="int64") values = 10 * prng.rand(10, 2).astype(self.dtype) labels = np.tile(np.arange(5), (2,)).astype("int64") expected_out = np.std(values.reshape(2, 5, 2), ddof=1, axis=0) ** 2 expected_counts = counts + 2 self.algo(out, counts, values, labels) assert np.allclose(out, expected_out, self.rtol) tm.assert_numpy_array_equal(counts, expected_counts) def test_group_var_generic_2d_some_nan(self): prng = RandomState(1234) out = (np.nan * np.ones((5, 2))).astype(self.dtype) counts = np.zeros(5, dtype="int64") values = 10 * prng.rand(10, 2).astype(self.dtype) values[:, 1] = np.nan labels = np.tile(np.arange(5), (2,)).astype("int64") expected_out = np.vstack( [ values[:, 0].reshape(5, 2, order="F").std(ddof=1, axis=1) ** 2, np.nan * np.ones(5), ] ).T.astype(self.dtype) expected_counts = counts + 2 self.algo(out, counts, values, labels) tm.assert_almost_equal(out, expected_out, check_less_precise=6) tm.assert_numpy_array_equal(counts, expected_counts) def test_group_var_constant(self): # Regression test from GH 10448. out = np.array([[np.nan]], dtype=self.dtype) counts = np.array([0], dtype="int64") values = 0.832845131556193 * np.ones((3, 1), dtype=self.dtype) labels = np.zeros(3, dtype="int64") self.algo(out, counts, values, labels) assert counts[0] == 3 assert out[0, 0] >= 0 tm.assert_almost_equal(out[0, 0], 0.0) class TestGroupVarFloat64(GroupVarTestMixin): __test__ = True algo = staticmethod(libgroupby.group_var_float64) dtype = np.float64 rtol = 1e-5 def test_group_var_large_inputs(self): prng = RandomState(1234) out = np.array([[np.nan]], dtype=self.dtype) counts = np.array([0], dtype="int64") values = (prng.rand(10 ** 6) + 10 ** 12).astype(self.dtype) values.shape = (10 ** 6, 1) labels = np.zeros(10 ** 6, dtype="int64") self.algo(out, counts, values, labels) assert counts[0] == 10 ** 6 tm.assert_almost_equal(out[0, 0], 1.0 / 12, check_less_precise=True) class TestGroupVarFloat32(GroupVarTestMixin): __test__ = True algo = staticmethod(libgroupby.group_var_float32) dtype = np.float32 rtol = 1e-2 class TestHashTable: def test_string_hashtable_set_item_signature(self): # GH#30419 fix typing in StringHashTable.set_item to prevent segfault tbl = ht.StringHashTable() tbl.set_item("key", 1) assert tbl.get_item("key") == 1 with pytest.raises(TypeError, match="'key' has incorrect type"): # key arg typed as string, not object tbl.set_item(4, 6) with pytest.raises(TypeError, match="'val' has incorrect type"): tbl.get_item(4) def test_lookup_nan(self, writable): xs = np.array([2.718, 3.14, np.nan, -7, 5, 2, 3]) # GH 21688 ensure we can deal with readonly memory views xs.setflags(write=writable) m = ht.Float64HashTable() m.map_locations(xs) tm.assert_numpy_array_equal(m.lookup(xs), np.arange(len(xs), dtype=np.int64)) def test_add_signed_zeros(self): # GH 21866 inconsistent hash-function for float64 # default hash-function would lead to different hash-buckets # for 0.0 and -0.0 if there are more than 2^30 hash-buckets # but this would mean 16GB N = 4 # 12 * 10**8 would trigger the error, if you have enough memory m = ht.Float64HashTable(N) m.set_item(0.0, 0) m.set_item(-0.0, 0) assert len(m) == 1 # 0.0 and -0.0 are equivalent def test_add_different_nans(self): # GH 21866 inconsistent hash-function for float64 # create different nans from bit-patterns: NAN1 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000000))[0] NAN2 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000001))[0] assert NAN1 != NAN1 assert NAN2 != NAN2 # default hash function would lead to different hash-buckets # for NAN1 and NAN2 even if there are only 4 buckets: m = ht.Float64HashTable() m.set_item(NAN1, 0) m.set_item(NAN2, 0) assert len(m) == 1 # NAN1 and NAN2 are equivalent def test_lookup_overflow(self, writable): xs = np.array([1, 2, 2 ** 63], dtype=np.uint64) # GH 21688 ensure we can deal with readonly memory views xs.setflags(write=writable) m = ht.UInt64HashTable() m.map_locations(xs) tm.assert_numpy_array_equal(m.lookup(xs), np.arange(len(xs), dtype=np.int64)) def test_get_unique(self): s = Series([1, 2, 2 ** 63, 2 ** 63], dtype=np.uint64) exp = np.array([1, 2, 2 ** 63], dtype=np.uint64) tm.assert_numpy_array_equal(s.unique(), exp) @pytest.mark.parametrize("nvals", [0, 10]) # resizing to 0 is special case @pytest.mark.parametrize( "htable, uniques, dtype, safely_resizes", [ (ht.PyObjectHashTable, ht.ObjectVector, "object", False), (ht.StringHashTable, ht.ObjectVector, "object", True), (ht.Float64HashTable, ht.Float64Vector, "float64", False), (ht.Int64HashTable, ht.Int64Vector, "int64", False), (ht.UInt64HashTable, ht.UInt64Vector, "uint64", False), ], ) def test_vector_resize( self, writable, htable, uniques, dtype, safely_resizes, nvals ): # Test for memory errors after internal vector # reallocations (GH 7157) vals = np.array(np.random.randn(1000), dtype=dtype) # GH 21688 ensures we can deal with read-only memory views vals.setflags(write=writable) # initialise instances; cannot initialise in parametrization, # as otherwise external views would be held on the array (which is # one of the things this test is checking) htable = htable() uniques = uniques() # get_labels may append to uniques htable.get_labels(vals[:nvals], uniques, 0, -1) # to_array() sets an external_view_exists flag on uniques. tmp = uniques.to_array() oldshape = tmp.shape # subsequent get_labels() calls can no longer append to it # (except for StringHashTables + ObjectVector) if safely_resizes: htable.get_labels(vals, uniques, 0, -1) else: with pytest.raises(ValueError, match="external reference.*"): htable.get_labels(vals, uniques, 0, -1) uniques.to_array() # should not raise here assert tmp.shape == oldshape @pytest.mark.parametrize( "htable, tm_dtype", [ (ht.PyObjectHashTable, "String"), (ht.StringHashTable, "String"), (ht.Float64HashTable, "Float"), (ht.Int64HashTable, "Int"), (ht.UInt64HashTable, "UInt"), ], ) def test_hashtable_unique(self, htable, tm_dtype, writable): # output of maker has guaranteed unique elements maker = getattr(tm, "make" + tm_dtype + "Index") s = Series(maker(1000)) if htable == ht.Float64HashTable: # add NaN for float column s.loc[500] = np.nan elif htable == ht.PyObjectHashTable: # use different NaN types for object column s.loc[500:502] = [np.nan, None, pd.NaT] # create duplicated selection s_duplicated = s.sample(frac=3, replace=True).reset_index(drop=True) s_duplicated.values.setflags(write=writable) # drop_duplicates has own cython code (hash_table_func_helper.pxi) # and is tested separately; keeps first occurrence like ht.unique() expected_unique = s_duplicated.drop_duplicates(keep="first").values result_unique = htable().unique(s_duplicated.values) tm.assert_numpy_array_equal(result_unique, expected_unique) # test return_inverse=True # reconstruction can only succeed if the inverse is correct result_unique, result_inverse = htable().unique( s_duplicated.values, return_inverse=True ) tm.assert_numpy_array_equal(result_unique, expected_unique) reconstr = result_unique[result_inverse] tm.assert_numpy_array_equal(reconstr, s_duplicated.values) @pytest.mark.parametrize( "htable, tm_dtype", [ (ht.PyObjectHashTable, "String"), (ht.StringHashTable, "String"), (ht.Float64HashTable, "Float"), (ht.Int64HashTable, "Int"), (ht.UInt64HashTable, "UInt"), ], ) def test_hashtable_factorize(self, htable, tm_dtype, writable): # output of maker has guaranteed unique elements maker = getattr(tm, "make" + tm_dtype + "Index") s = Series(maker(1000)) if htable == ht.Float64HashTable: # add NaN for float column s.loc[500] = np.nan elif htable == ht.PyObjectHashTable: # use different NaN types for object column s.loc[500:502] = [np.nan, None, pd.NaT] # create duplicated selection s_duplicated = s.sample(frac=3, replace=True).reset_index(drop=True) s_duplicated.values.setflags(write=writable) na_mask = s_duplicated.isna().values result_unique, result_inverse = htable().factorize(s_duplicated.values) # drop_duplicates has own cython code (hash_table_func_helper.pxi) # and is tested separately; keeps first occurrence like ht.factorize() # since factorize removes all NaNs, we do the same here expected_unique = s_duplicated.dropna().drop_duplicates().values tm.assert_numpy_array_equal(result_unique, expected_unique) # reconstruction can only succeed if the inverse is correct. Since # factorize removes the NaNs, those have to be excluded here as well result_reconstruct = result_unique[result_inverse[~na_mask]] expected_reconstruct = s_duplicated.dropna().values tm.assert_numpy_array_equal(result_reconstruct, expected_reconstruct) @pytest.mark.parametrize( "hashtable", [ ht.PyObjectHashTable, ht.StringHashTable, ht.Float64HashTable, ht.Int64HashTable, ht.UInt64HashTable, ], ) def test_hashtable_large_sizehint(self, hashtable): # GH 22729 size_hint = np.iinfo(np.uint32).max + 1 tbl = hashtable(size_hint=size_hint) # noqa def test_quantile(): s = Series(np.random.randn(100)) result = algos.quantile(s, [0, 0.25, 0.5, 0.75, 1.0]) expected = algos.quantile(s.values, [0, 0.25, 0.5, 0.75, 1.0]) tm.assert_almost_equal(result, expected) def test_unique_label_indices(): a = np.random.randint(1, 1 << 10, 1 << 15).astype("i8") left = ht.unique_label_indices(a) right = np.unique(a, return_index=True)[1] tm.assert_numpy_array_equal(left, right, check_dtype=False) a[np.random.choice(len(a), 10)] = -1 left = ht.unique_label_indices(a) right = np.unique(a, return_index=True)[1][1:] tm.assert_numpy_array_equal(left, right, check_dtype=False) class TestRank: @td.skip_if_no_scipy def test_scipy_compat(self): from scipy.stats import rankdata def _check(arr): mask = ~np.isfinite(arr) arr = arr.copy() result = libalgos.rank_1d(arr) arr[mask] = np.inf exp = rankdata(arr) exp[mask] = np.nan tm.assert_almost_equal(result, exp) _check(np.array([np.nan, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 3, np.nan])) _check(np.array([4.0, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 4.0, np.nan])) def test_basic(self): exp = np.array([1, 2], dtype=np.float64) for dtype in np.typecodes["AllInteger"]: s = Series([1, 100], dtype=dtype) tm.assert_numpy_array_equal(algos.rank(s), exp) def test_uint64_overflow(self): exp = np.array([1, 2], dtype=np.float64) for dtype in [np.float64, np.uint64]: s = Series([1, 2 ** 63], dtype=dtype) tm.assert_numpy_array_equal(algos.rank(s), exp) def test_too_many_ndims(self): arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]) msg = "Array with ndim > 2 are not supported" with pytest.raises(TypeError, match=msg): algos.rank(arr) @pytest.mark.single @pytest.mark.high_memory @pytest.mark.parametrize( "values", [np.arange(2 ** 24 + 1), np.arange(2 ** 25 + 2).reshape(2 ** 24 + 1, 2)], ids=["1d", "2d"], ) def test_pct_max_many_rows(self, values): # GH 18271 result = algos.rank(values, pct=True).max() assert result == 1 def test_pad_backfill_object_segfault(): old = np.array([], dtype="O") new = np.array([datetime(2010, 12, 31)], dtype="O") result = libalgos.pad["object"](old, new) expected = np.array([-1], dtype=np.int64) tm.assert_numpy_array_equal(result, expected) result = libalgos.pad["object"](new, old) expected = np.array([], dtype=np.int64) tm.assert_numpy_array_equal(result, expected) result = libalgos.backfill["object"](old, new) expected = np.array([-1], dtype=np.int64) tm.assert_numpy_array_equal(result, expected) result = libalgos.backfill["object"](new, old) expected = np.array([], dtype=np.int64) tm.assert_numpy_array_equal(result, expected) class TestTseriesUtil: def test_combineFunc(self): pass def test_reindex(self): pass def test_isna(self): pass def test_groupby(self): pass def test_groupby_withnull(self): pass def test_backfill(self): old = Index([1, 5, 10]) new = Index(list(range(12))) filler = libalgos.backfill["int64_t"](old.values, new.values) expect_filler = np.array([0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, -1], dtype=np.int64) tm.assert_numpy_array_equal(filler, expect_filler) # corner case old = Index([1, 4]) new = Index(list(range(5, 10))) filler = libalgos.backfill["int64_t"](old.values, new.values) expect_filler = np.array([-1, -1, -1, -1, -1], dtype=np.int64) tm.assert_numpy_array_equal(filler, expect_filler) def test_pad(self): old = Index([1, 5, 10]) new = Index(list(range(12))) filler = libalgos.pad["int64_t"](old.values, new.values) expect_filler = np.array([-1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2], dtype=np.int64) tm.assert_numpy_array_equal(filler, expect_filler) # corner case old = Index([5, 10]) new = Index(np.arange(5)) filler = libalgos.pad["int64_t"](old.values, new.values) expect_filler = np.array([-1, -1, -1, -1, -1], dtype=np.int64) tm.assert_numpy_array_equal(filler, expect_filler) def test_is_lexsorted(): failure = [ np.array( [ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], dtype="int64", ), np.array( [ 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, ], dtype="int64", ), ] assert not libalgos.is_lexsorted(failure) def test_groupsort_indexer(): a = np.random.randint(0, 1000, 100).astype(np.int64) b = np.random.randint(0, 1000, 100).astype(np.int64) result = libalgos.groupsort_indexer(a, 1000)[0] # need to use a stable sort # np.argsort returns int, groupsort_indexer # always returns int64 expected = np.argsort(a, kind="mergesort") expected = expected.astype(np.int64) tm.assert_numpy_array_equal(result, expected) # compare with lexsort # np.lexsort returns int, groupsort_indexer # always returns int64 key = a * 1000 + b result = libalgos.groupsort_indexer(key, 1000000)[0] expected = np.lexsort((b, a)) expected = expected.astype(np.int64) tm.assert_numpy_array_equal(result, expected) def test_infinity_sort(): # GH 13445 # numpy's argsort can be unhappy if something is less than # itself. Instead, let's give our infinities a self-consistent # ordering, but outside the float extended real line. Inf = libalgos.Infinity() NegInf = libalgos.NegInfinity() ref_nums = [NegInf, float("-inf"), -1e100, 0, 1e100, float("inf"), Inf] assert all(Inf >= x for x in ref_nums) assert all(Inf > x or x is Inf for x in ref_nums) assert Inf >= Inf and Inf == Inf assert not Inf < Inf and not Inf > Inf assert libalgos.Infinity() == libalgos.Infinity() assert not libalgos.Infinity() != libalgos.Infinity() assert all(NegInf <= x for x in ref_nums) assert all(NegInf < x or x is NegInf for x in ref_nums) assert NegInf <= NegInf and NegInf == NegInf assert not NegInf < NegInf and not NegInf > NegInf assert libalgos.NegInfinity() == libalgos.NegInfinity() assert not libalgos.NegInfinity() != libalgos.NegInfinity() for perm in permutations(ref_nums): assert sorted(perm) == ref_nums # smoke tests np.array([libalgos.Infinity()] * 32).argsort() np.array([libalgos.NegInfinity()] * 32).argsort() def test_infinity_against_nan(): Inf = libalgos.Infinity() NegInf = libalgos.NegInfinity() assert not Inf > np.nan assert not Inf >= np.nan assert not Inf < np.nan assert not Inf <= np.nan assert not Inf == np.nan assert Inf != np.nan assert not NegInf > np.nan assert not NegInf >= np.nan assert not NegInf < np.nan assert not NegInf <= np.nan assert not NegInf == np.nan assert NegInf != np.nan def test_ensure_platform_int(): arr = np.arange(100, dtype=np.intp) result = libalgos.ensure_platform_int(arr) assert result is arr def test_int64_add_overflow(): # see gh-14068 msg = "Overflow in int64 addition" m = np.iinfo(np.int64).max n = np.iinfo(np.int64).min with pytest.raises(OverflowError, match=msg): algos.checked_add_with_arr(np.array([m, m]), m) with pytest.raises(OverflowError, match=msg): algos.checked_add_with_arr(np.array([m, m]), np.array([m, m])) with pytest.raises(OverflowError, match=msg): algos.checked_add_with_arr(np.array([n, n]), n) with pytest.raises(OverflowError, match=msg): algos.checked_add_with_arr(np.array([n, n]), np.array([n, n])) with pytest.raises(OverflowError, match=msg): algos.checked_add_with_arr(np.array([m, n]), np.array([n, n])) with pytest.raises(OverflowError, match=msg): algos.checked_add_with_arr( np.array([m, m]), np.array([m, m]), arr_mask=np.array([False, True]) ) with pytest.raises(OverflowError, match=msg): algos.checked_add_with_arr( np.array([m, m]), np.array([m, m]), b_mask=np.array([False, True]) ) with pytest.raises(OverflowError, match=msg): algos.checked_add_with_arr( np.array([m, m]), np.array([m, m]), arr_mask=np.array([False, True]), b_mask=np.array([False, True]), ) with pytest.raises(OverflowError, match=msg): with tm.assert_produces_warning(RuntimeWarning): algos.checked_add_with_arr(np.array([m, m]), np.array([np.nan, m])) # Check that the nan boolean arrays override whether or not # the addition overflows. We don't check the result but just # the fact that an OverflowError is not raised. algos.checked_add_with_arr( np.array([m, m]), np.array([m, m]), arr_mask=np.array([True, True]) ) algos.checked_add_with_arr( np.array([m, m]), np.array([m, m]), b_mask=np.array([True, True]) ) algos.checked_add_with_arr( np.array([m, m]), np.array([m, m]), arr_mask=np.array([True, False]), b_mask=np.array([False, True]), ) class TestMode: def test_no_mode(self): exp = Series([], dtype=np.float64) tm.assert_series_equal(algos.mode([]), exp) def test_mode_single(self): # GH 15714 exp_single = [1] data_single = [1] exp_multi = [1] data_multi = [1, 1] for dt in np.typecodes["AllInteger"] + np.typecodes["Float"]: s = Series(data_single, dtype=dt) exp = Series(exp_single, dtype=dt) tm.assert_series_equal(algos.mode(s), exp) s = Series(data_multi, dtype=dt) exp = Series(exp_multi, dtype=dt) tm.assert_series_equal(algos.mode(s), exp) exp = Series([1], dtype=np.int) tm.assert_series_equal(algos.mode([1]), exp) exp = Series(["a", "b", "c"], dtype=np.object) tm.assert_series_equal(algos.mode(["a", "b", "c"]), exp) def test_number_mode(self): exp_single = [1] data_single = [1] * 5 + [2] * 3 exp_multi = [1, 3] data_multi = [1] * 5 + [2] * 3 + [3] * 5 for dt in np.typecodes["AllInteger"] + np.typecodes["Float"]: s = Series(data_single, dtype=dt) exp = Series(exp_single, dtype=dt) tm.assert_series_equal(algos.mode(s), exp) s = Series(data_multi, dtype=dt) exp = Series(exp_multi, dtype=dt) tm.assert_series_equal(algos.mode(s), exp) def test_strobj_mode(self): exp = ["b"] data = ["a"] * 2 + ["b"] * 3 s = Series(data, dtype="c") exp = Series(exp, dtype="c") tm.assert_series_equal(algos.mode(s), exp) exp = ["bar"] data = ["foo"] * 2 + ["bar"] * 3 for dt in [str, object]: s = Series(data, dtype=dt) exp = Series(exp, dtype=dt) tm.assert_series_equal(algos.mode(s), exp) def test_datelike_mode(self): exp = Series(["1900-05-03", "2011-01-03", "2013-01-02"], dtype="M8[ns]") s = Series(["2011-01-03", "2013-01-02", "1900-05-03"], dtype="M8[ns]") tm.assert_series_equal(algos.mode(s), exp) exp = Series(["2011-01-03", "2013-01-02"], dtype="M8[ns]") s = Series( ["2011-01-03", "2013-01-02", "1900-05-03", "2011-01-03", "2013-01-02"], dtype="M8[ns]", ) tm.assert_series_equal(algos.mode(s), exp) def test_timedelta_mode(self): exp = Series(["-1 days", "0 days", "1 days"], dtype="timedelta64[ns]") s = Series(["1 days", "-1 days", "0 days"], dtype="timedelta64[ns]") tm.assert_series_equal(algos.mode(s), exp) exp = Series(["2 min", "1 day"], dtype="timedelta64[ns]") s = Series( ["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"], dtype="timedelta64[ns]", ) tm.assert_series_equal(algos.mode(s), exp) def test_mixed_dtype(self): exp = Series(["foo"]) s = Series([1, "foo", "foo"]) tm.assert_series_equal(algos.mode(s), exp) def test_uint64_overflow(self): exp = Series([2 ** 63], dtype=np.uint64) s = Series([1, 2 ** 63, 2 ** 63], dtype=np.uint64) tm.assert_series_equal(algos.mode(s), exp) exp = Series([1, 2 ** 63], dtype=np.uint64) s = Series([1, 2 ** 63], dtype=np.uint64) tm.assert_series_equal(algos.mode(s), exp) def test_categorical(self): c = Categorical([1, 2]) exp = c tm.assert_categorical_equal(algos.mode(c), exp) tm.assert_categorical_equal(c.mode(), exp) c = Categorical([1, "a", "a"]) exp = Categorical(["a"], categories=[1, "a"]) tm.assert_categorical_equal(algos.mode(c), exp) tm.assert_categorical_equal(c.mode(), exp) c = Categorical([1, 1, 2, 3, 3]) exp = Categorical([1, 3], categories=[1, 2, 3]) tm.assert_categorical_equal(algos.mode(c), exp) tm.assert_categorical_equal(c.mode(), exp) def test_index(self): idx = Index([1, 2, 3]) exp = Series([1, 2, 3], dtype=np.int64) tm.assert_series_equal(algos.mode(idx), exp) idx = Index([1, "a", "a"]) exp = Series(["a"], dtype=object) tm.assert_series_equal(algos.mode(idx), exp) idx = Index([1, 1, 2, 3, 3]) exp = Series([1, 3], dtype=np.int64) tm.assert_series_equal(algos.mode(idx), exp) exp = Series(["2 min", "1 day"], dtype="timedelta64[ns]") idx = Index( ["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"], dtype="timedelta64[ns]", ) tm.assert_series_equal(algos.mode(idx), exp)