2278 lines
76 KiB
Python
2278 lines
76 KiB
Python
from datetime import datetime
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from itertools import permutations
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import struct
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import numpy as np
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from numpy import nan
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from numpy.random import RandomState
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import pytest
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from pandas._libs import algos as libalgos, groupby as libgroupby, hashtable as ht
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from pandas.compat.numpy import np_array_datetime64_compat
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.dtypes import CategoricalDtype as CDT
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import pandas as pd
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from pandas import (
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Categorical,
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CategoricalIndex,
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DatetimeIndex,
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Index,
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IntervalIndex,
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Series,
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Timestamp,
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compat,
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)
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import pandas.core.algorithms as algos
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from pandas.core.arrays import DatetimeArray
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import pandas.core.common as com
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from pandas.core.sorting import safe_sort
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import pandas.util.testing as tm
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from pandas.util.testing import assert_almost_equal
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class TestMatch:
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def test_ints(self):
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values = np.array([0, 2, 1])
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to_match = np.array([0, 1, 2, 2, 0, 1, 3, 0])
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result = algos.match(to_match, values)
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expected = np.array([0, 2, 1, 1, 0, 2, -1, 0], dtype=np.int64)
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tm.assert_numpy_array_equal(result, expected)
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result = Series(algos.match(to_match, values, np.nan))
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expected = Series(np.array([0, 2, 1, 1, 0, 2, np.nan, 0]))
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tm.assert_series_equal(result, expected)
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s = Series(np.arange(5), dtype=np.float32)
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result = algos.match(s, [2, 4])
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expected = np.array([-1, -1, 0, -1, 1], dtype=np.int64)
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tm.assert_numpy_array_equal(result, expected)
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result = Series(algos.match(s, [2, 4], np.nan))
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expected = Series(np.array([np.nan, np.nan, 0, np.nan, 1]))
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tm.assert_series_equal(result, expected)
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def test_strings(self):
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values = ["foo", "bar", "baz"]
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to_match = ["bar", "foo", "qux", "foo", "bar", "baz", "qux"]
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result = algos.match(to_match, values)
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expected = np.array([1, 0, -1, 0, 1, 2, -1], dtype=np.int64)
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tm.assert_numpy_array_equal(result, expected)
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result = Series(algos.match(to_match, values, np.nan))
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expected = Series(np.array([1, 0, np.nan, 0, 1, 2, np.nan]))
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tm.assert_series_equal(result, expected)
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class TestFactorize:
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def test_basic(self):
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labels, uniques = algos.factorize(["a", "b", "b", "a", "a", "c", "c", "c"])
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tm.assert_numpy_array_equal(uniques, np.array(["a", "b", "c"], dtype=object))
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labels, uniques = algos.factorize(
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["a", "b", "b", "a", "a", "c", "c", "c"], sort=True
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)
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exp = np.array([0, 1, 1, 0, 0, 2, 2, 2], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = np.array(["a", "b", "c"], dtype=object)
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tm.assert_numpy_array_equal(uniques, exp)
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labels, uniques = algos.factorize(list(reversed(range(5))))
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exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = np.array([4, 3, 2, 1, 0], dtype=np.int64)
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tm.assert_numpy_array_equal(uniques, exp)
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labels, uniques = algos.factorize(list(reversed(range(5))), sort=True)
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exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = np.array([0, 1, 2, 3, 4], dtype=np.int64)
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tm.assert_numpy_array_equal(uniques, exp)
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labels, uniques = algos.factorize(list(reversed(np.arange(5.0))))
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exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = np.array([4.0, 3.0, 2.0, 1.0, 0.0], dtype=np.float64)
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tm.assert_numpy_array_equal(uniques, exp)
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labels, uniques = algos.factorize(list(reversed(np.arange(5.0))), sort=True)
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exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=np.float64)
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tm.assert_numpy_array_equal(uniques, exp)
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def test_mixed(self):
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# doc example reshaping.rst
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x = Series(["A", "A", np.nan, "B", 3.14, np.inf])
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labels, uniques = algos.factorize(x)
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exp = np.array([0, 0, -1, 1, 2, 3], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = Index(["A", "B", 3.14, np.inf])
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tm.assert_index_equal(uniques, exp)
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labels, uniques = algos.factorize(x, sort=True)
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exp = np.array([2, 2, -1, 3, 0, 1], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = Index([3.14, np.inf, "A", "B"])
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tm.assert_index_equal(uniques, exp)
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def test_datelike(self):
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# M8
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v1 = Timestamp("20130101 09:00:00.00004")
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v2 = Timestamp("20130101")
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x = Series([v1, v1, v1, v2, v2, v1])
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labels, uniques = algos.factorize(x)
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exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = DatetimeIndex([v1, v2])
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tm.assert_index_equal(uniques, exp)
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labels, uniques = algos.factorize(x, sort=True)
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exp = np.array([1, 1, 1, 0, 0, 1], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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exp = DatetimeIndex([v2, v1])
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tm.assert_index_equal(uniques, exp)
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# period
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v1 = pd.Period("201302", freq="M")
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v2 = pd.Period("201303", freq="M")
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x = Series([v1, v1, v1, v2, v2, v1])
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# periods are not 'sorted' as they are converted back into an index
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labels, uniques = algos.factorize(x)
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exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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tm.assert_index_equal(uniques, pd.PeriodIndex([v1, v2]))
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labels, uniques = algos.factorize(x, sort=True)
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exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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tm.assert_index_equal(uniques, pd.PeriodIndex([v1, v2]))
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# GH 5986
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v1 = pd.to_timedelta("1 day 1 min")
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v2 = pd.to_timedelta("1 day")
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x = Series([v1, v2, v1, v1, v2, v2, v1])
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labels, uniques = algos.factorize(x)
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exp = np.array([0, 1, 0, 0, 1, 1, 0], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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tm.assert_index_equal(uniques, pd.to_timedelta([v1, v2]))
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labels, uniques = algos.factorize(x, sort=True)
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exp = np.array([1, 0, 1, 1, 0, 0, 1], dtype=np.intp)
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tm.assert_numpy_array_equal(labels, exp)
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tm.assert_index_equal(uniques, pd.to_timedelta([v2, v1]))
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def test_factorize_nan(self):
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# nan should map to na_sentinel, not reverse_indexer[na_sentinel]
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# rizer.factorize should not raise an exception if na_sentinel indexes
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# outside of reverse_indexer
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key = np.array([1, 2, 1, np.nan], dtype="O")
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rizer = ht.Factorizer(len(key))
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for na_sentinel in (-1, 20):
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ids = rizer.factorize(key, sort=True, na_sentinel=na_sentinel)
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expected = np.array([0, 1, 0, na_sentinel], dtype="int32")
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assert len(set(key)) == len(set(expected))
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tm.assert_numpy_array_equal(pd.isna(key), expected == na_sentinel)
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# nan still maps to na_sentinel when sort=False
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key = np.array([0, np.nan, 1], dtype="O")
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na_sentinel = -1
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# TODO(wesm): unused?
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ids = rizer.factorize(key, sort=False, na_sentinel=na_sentinel) # noqa
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expected = np.array([2, -1, 0], dtype="int32")
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assert len(set(key)) == len(set(expected))
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tm.assert_numpy_array_equal(pd.isna(key), expected == na_sentinel)
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@pytest.mark.parametrize(
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"data,expected_label,expected_level",
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[
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(
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[(1, 1), (1, 2), (0, 0), (1, 2), "nonsense"],
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[0, 1, 2, 1, 3],
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[(1, 1), (1, 2), (0, 0), "nonsense"],
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),
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(
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[(1, 1), (1, 2), (0, 0), (1, 2), (1, 2, 3)],
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[0, 1, 2, 1, 3],
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[(1, 1), (1, 2), (0, 0), (1, 2, 3)],
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),
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([(1, 1), (1, 2), (0, 0), (1, 2)], [0, 1, 2, 1], [(1, 1), (1, 2), (0, 0)]),
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],
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)
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def test_factorize_tuple_list(self, data, expected_label, expected_level):
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# GH9454
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result = pd.factorize(data)
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tm.assert_numpy_array_equal(result[0], np.array(expected_label, dtype=np.intp))
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expected_level_array = com.asarray_tuplesafe(expected_level, dtype=object)
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tm.assert_numpy_array_equal(result[1], expected_level_array)
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def test_complex_sorting(self):
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# gh 12666 - check no segfault
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x17 = np.array([complex(i) for i in range(17)], dtype=object)
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msg = (
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"unorderable types: .* [<>] .*"
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"|" # the above case happens for numpy < 1.14
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"'[<>]' not supported between instances of .*"
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)
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with pytest.raises(TypeError, match=msg):
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algos.factorize(x17[::-1], sort=True)
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def test_float64_factorize(self, writable):
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data = np.array([1.0, 1e8, 1.0, 1e-8, 1e8, 1.0], dtype=np.float64)
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data.setflags(write=writable)
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exp_labels = np.array([0, 1, 0, 2, 1, 0], dtype=np.intp)
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exp_uniques = np.array([1.0, 1e8, 1e-8], dtype=np.float64)
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labels, uniques = algos.factorize(data)
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tm.assert_numpy_array_equal(labels, exp_labels)
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tm.assert_numpy_array_equal(uniques, exp_uniques)
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def test_uint64_factorize(self, writable):
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data = np.array([2 ** 64 - 1, 1, 2 ** 64 - 1], dtype=np.uint64)
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data.setflags(write=writable)
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exp_labels = np.array([0, 1, 0], dtype=np.intp)
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exp_uniques = np.array([2 ** 64 - 1, 1], dtype=np.uint64)
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labels, uniques = algos.factorize(data)
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tm.assert_numpy_array_equal(labels, exp_labels)
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tm.assert_numpy_array_equal(uniques, exp_uniques)
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def test_int64_factorize(self, writable):
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data = np.array([2 ** 63 - 1, -2 ** 63, 2 ** 63 - 1], dtype=np.int64)
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data.setflags(write=writable)
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exp_labels = np.array([0, 1, 0], dtype=np.intp)
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exp_uniques = np.array([2 ** 63 - 1, -2 ** 63], dtype=np.int64)
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labels, uniques = algos.factorize(data)
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tm.assert_numpy_array_equal(labels, exp_labels)
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tm.assert_numpy_array_equal(uniques, exp_uniques)
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def test_string_factorize(self, writable):
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data = np.array(["a", "c", "a", "b", "c"], dtype=object)
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data.setflags(write=writable)
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exp_labels = np.array([0, 1, 0, 2, 1], dtype=np.intp)
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exp_uniques = np.array(["a", "c", "b"], dtype=object)
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labels, uniques = algos.factorize(data)
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tm.assert_numpy_array_equal(labels, exp_labels)
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tm.assert_numpy_array_equal(uniques, exp_uniques)
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def test_object_factorize(self, writable):
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data = np.array(["a", "c", None, np.nan, "a", "b", pd.NaT, "c"], dtype=object)
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data.setflags(write=writable)
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exp_labels = np.array([0, 1, -1, -1, 0, 2, -1, 1], dtype=np.intp)
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exp_uniques = np.array(["a", "c", "b"], dtype=object)
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labels, uniques = algos.factorize(data)
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tm.assert_numpy_array_equal(labels, exp_labels)
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tm.assert_numpy_array_equal(uniques, exp_uniques)
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def test_deprecate_order(self):
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# gh 19727 - check warning is raised for deprecated keyword, order.
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# Test not valid once order keyword is removed.
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data = np.array([2 ** 63, 1, 2 ** 63], dtype=np.uint64)
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with tm.assert_produces_warning(expected_warning=FutureWarning):
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algos.factorize(data, order=True)
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with tm.assert_produces_warning(False):
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algos.factorize(data)
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@pytest.mark.parametrize(
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"data",
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[
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np.array([0, 1, 0], dtype="u8"),
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np.array([-2 ** 63, 1, -2 ** 63], dtype="i8"),
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np.array(["__nan__", "foo", "__nan__"], dtype="object"),
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],
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)
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def test_parametrized_factorize_na_value_default(self, data):
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# arrays that include the NA default for that type, but isn't used.
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l, u = algos.factorize(data)
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expected_uniques = data[[0, 1]]
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expected_labels = np.array([0, 1, 0], dtype=np.intp)
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tm.assert_numpy_array_equal(l, expected_labels)
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tm.assert_numpy_array_equal(u, expected_uniques)
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@pytest.mark.parametrize(
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"data, na_value",
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[
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(np.array([0, 1, 0, 2], dtype="u8"), 0),
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(np.array([1, 0, 1, 2], dtype="u8"), 1),
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(np.array([-2 ** 63, 1, -2 ** 63, 0], dtype="i8"), -2 ** 63),
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(np.array([1, -2 ** 63, 1, 0], dtype="i8"), 1),
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(np.array(["a", "", "a", "b"], dtype=object), "a"),
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(np.array([(), ("a", 1), (), ("a", 2)], dtype=object), ()),
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(np.array([("a", 1), (), ("a", 1), ("a", 2)], dtype=object), ("a", 1)),
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],
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)
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def test_parametrized_factorize_na_value(self, data, na_value):
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l, u = algos._factorize_array(data, na_value=na_value)
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expected_uniques = data[[1, 3]]
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expected_labels = np.array([-1, 0, -1, 1], dtype=np.intp)
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tm.assert_numpy_array_equal(l, expected_labels)
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tm.assert_numpy_array_equal(u, expected_uniques)
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@pytest.mark.parametrize("sort", [True, False])
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@pytest.mark.parametrize("na_sentinel", [-1, -10, 100])
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@pytest.mark.parametrize(
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"data, uniques",
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[
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(
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np.array(["b", "a", None, "b"], dtype=object),
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np.array(["b", "a"], dtype=object),
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),
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(
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pd.array([2, 1, np.nan, 2], dtype="Int64"),
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pd.array([2, 1], dtype="Int64"),
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),
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],
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ids=["numpy_array", "extension_array"],
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)
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def test_factorize_na_sentinel(self, sort, na_sentinel, data, uniques):
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labels, uniques = algos.factorize(data, sort=sort, na_sentinel=na_sentinel)
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if sort:
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expected_labels = np.array([1, 0, na_sentinel, 1], dtype=np.intp)
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expected_uniques = safe_sort(uniques)
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else:
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expected_labels = np.array([0, 1, na_sentinel, 0], dtype=np.intp)
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expected_uniques = uniques
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tm.assert_numpy_array_equal(labels, expected_labels)
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if isinstance(data, np.ndarray):
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tm.assert_numpy_array_equal(uniques, expected_uniques)
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else:
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tm.assert_extension_array_equal(uniques, expected_uniques)
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class TestUnique:
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def test_ints(self):
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arr = np.random.randint(0, 100, size=50)
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result = algos.unique(arr)
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assert isinstance(result, np.ndarray)
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def test_objects(self):
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arr = np.random.randint(0, 100, size=50).astype("O")
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result = algos.unique(arr)
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assert isinstance(result, np.ndarray)
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def test_object_refcount_bug(self):
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lst = ["A", "B", "C", "D", "E"]
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for i in range(1000):
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len(algos.unique(lst))
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def test_on_index_object(self):
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mindex = pd.MultiIndex.from_arrays(
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[np.arange(5).repeat(5), np.tile(np.arange(5), 5)]
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)
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expected = mindex.values
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expected.sort()
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mindex = mindex.repeat(2)
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result = pd.unique(mindex)
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result.sort()
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tm.assert_almost_equal(result, expected)
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def test_datetime64_dtype_array_returned(self):
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# GH 9431
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expected = np_array_datetime64_compat(
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[
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"2015-01-03T00:00:00.000000000+0000",
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"2015-01-01T00:00:00.000000000+0000",
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],
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dtype="M8[ns]",
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)
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dt_index = pd.to_datetime(
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[
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"2015-01-03T00:00:00.000000000",
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"2015-01-01T00:00:00.000000000",
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"2015-01-01T00:00:00.000000000",
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]
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)
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result = algos.unique(dt_index)
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tm.assert_numpy_array_equal(result, expected)
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assert result.dtype == expected.dtype
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|
|
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_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):
|
|
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(), 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_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_float64(arr)
|
|
arr[mask] = np.inf
|
|
exp = rankdata(arr)
|
|
exp[mask] = nan
|
|
assert_almost_equal(result, exp)
|
|
|
|
_check(np.array([nan, nan, 5.0, 5.0, 5.0, nan, 1, 2, 3, nan]))
|
|
_check(np.array([4.0, nan, 5.0, 5.0, 5.0, nan, 1, 2, 4.0, 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)
|
|
|
|
|
|
def test_arrmap():
|
|
values = np.array(["foo", "foo", "bar", "bar", "baz", "qux"], dtype="O")
|
|
result = libalgos.arrmap_object(values, lambda x: x in ["foo", "bar"])
|
|
assert result.dtype == np.bool_
|
|
|
|
|
|
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)
|