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test_algos.py
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# -*- coding: utf-8 -*-
from pandas.compat import range
import numpy as np
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from pandas import Series, Categorical, CategoricalIndex, Index
import pandas as pd
from pandas import compat
from pandas._libs import (groupby as libgroupby, algos as libalgos,
hashtable)
from pandas._libs.hashtable import unique_label_indices
from pandas.compat import lrange
import pandas.core.algorithms as algos
import pandas.util.testing as tm
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.util.testing import assert_almost_equal
class TestMatch(tm.TestCase):
def test_ints(self):
values = np.array([0, 2, 1])
to_match = np.array([0, 1, 2, 2, 0, 1, 3, 0])
result = algos.match(to_match, values)
expected = np.array([0, 2, 1, 1, 0, 2, -1, 0], dtype=np.int64)
self.assert_numpy_array_equal(result, expected)
result = Series(algos.match(to_match, values, np.nan))
expected = Series(np.array([0, 2, 1, 1, 0, 2, np.nan, 0]))
tm.assert_series_equal(result, expected)
s = pd.Series(np.arange(5), dtype=np.float32)
result = algos.match(s, [2, 4])
expected = np.array([-1, -1, 0, -1, 1], dtype=np.int64)
self.assert_numpy_array_equal(result, expected)
result = Series(algos.match(s, [2, 4], np.nan))
expected = Series(np.array([np.nan, np.nan, 0, np.nan, 1]))
tm.assert_series_equal(result, expected)
def test_strings(self):
values = ['foo', 'bar', 'baz']
to_match = ['bar', 'foo', 'qux', 'foo', 'bar', 'baz', 'qux']
result = algos.match(to_match, values)
expected = np.array([1, 0, -1, 0, 1, 2, -1], dtype=np.int64)
self.assert_numpy_array_equal(result, expected)
result = Series(algos.match(to_match, values, np.nan))
expected = Series(np.array([1, 0, np.nan, 0, 1, 2, np.nan]))
tm.assert_series_equal(result, expected)
class TestSafeSort(tm.TestCase):
def test_basic_sort(self):
values = [3, 1, 2, 0, 4]
result = algos.safe_sort(values)
expected = np.array([0, 1, 2, 3, 4])
tm.assert_numpy_array_equal(result, expected)
values = list("baaacb")
result = algos.safe_sort(values)
expected = np.array(list("aaabbc"))
tm.assert_numpy_array_equal(result, expected)
values = []
result = algos.safe_sort(values)
expected = np.array([])
tm.assert_numpy_array_equal(result, expected)
def test_labels(self):
values = [3, 1, 2, 0, 4]
expected = np.array([0, 1, 2, 3, 4])
labels = [0, 1, 1, 2, 3, 0, -1, 4]
result, result_labels = algos.safe_sort(values, labels)
expected_labels = np.array([3, 1, 1, 2, 0, 3, -1, 4], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
tm.assert_numpy_array_equal(result_labels, expected_labels)
# na_sentinel
labels = [0, 1, 1, 2, 3, 0, 99, 4]
result, result_labels = algos.safe_sort(values, labels,
na_sentinel=99)
expected_labels = np.array([3, 1, 1, 2, 0, 3, 99, 4], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
tm.assert_numpy_array_equal(result_labels, expected_labels)
# out of bound indices
labels = [0, 101, 102, 2, 3, 0, 99, 4]
result, result_labels = algos.safe_sort(values, labels)
expected_labels = np.array([3, -1, -1, 2, 0, 3, -1, 4], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
tm.assert_numpy_array_equal(result_labels, expected_labels)
labels = []
result, result_labels = algos.safe_sort(values, labels)
expected_labels = np.array([], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
tm.assert_numpy_array_equal(result_labels, expected_labels)
def test_mixed_integer(self):
values = np.array(['b', 1, 0, 'a', 0, 'b'], dtype=object)
result = algos.safe_sort(values)
expected = np.array([0, 0, 1, 'a', 'b', 'b'], dtype=object)
tm.assert_numpy_array_equal(result, expected)
values = np.array(['b', 1, 0, 'a'], dtype=object)
labels = [0, 1, 2, 3, 0, -1, 1]
result, result_labels = algos.safe_sort(values, labels)
expected = np.array([0, 1, 'a', 'b'], dtype=object)
expected_labels = np.array([3, 1, 0, 2, 3, -1, 1], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
tm.assert_numpy_array_equal(result_labels, expected_labels)
def test_unsortable(self):
# GH 13714
arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
if compat.PY2 and not pd._np_version_under1p10:
# RuntimeWarning: tp_compare didn't return -1 or -2 for exception
with tm.assert_produces_warning(RuntimeWarning):
tm.assertRaises(TypeError, algos.safe_sort, arr)
else:
tm.assertRaises(TypeError, algos.safe_sort, arr)
def test_exceptions(self):
with tm.assertRaisesRegexp(TypeError,
"Only list-like objects are allowed"):
algos.safe_sort(values=1)
with tm.assertRaisesRegexp(TypeError,
"Only list-like objects or None"):
algos.safe_sort(values=[0, 1, 2], labels=1)
with tm.assertRaisesRegexp(ValueError, "values should be unique"):
algos.safe_sort(values=[0, 1, 2, 1], labels=[0, 1])
class TestFactorize(tm.TestCase):
def test_basic(self):
labels, uniques = algos.factorize(['a', 'b', 'b', 'a', 'a', 'c', 'c',
'c'])
self.assert_numpy_array_equal(
uniques, np.array(['a', 'b', 'c'], dtype=object))
labels, 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)
self.assert_numpy_array_equal(labels, exp)
exp = np.array(['a', 'b', 'c'], dtype=object)
self.assert_numpy_array_equal(uniques, exp)
labels, uniques = algos.factorize(list(reversed(range(5))))
exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
exp = np.array([4, 3, 2, 1, 0], dtype=np.int64)
self.assert_numpy_array_equal(uniques, exp)
labels, uniques = algos.factorize(list(reversed(range(5))), sort=True)
exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
exp = np.array([0, 1, 2, 3, 4], dtype=np.int64)
self.assert_numpy_array_equal(uniques, exp)
labels, uniques = algos.factorize(list(reversed(np.arange(5.))))
exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
exp = np.array([4., 3., 2., 1., 0.], dtype=np.float64)
self.assert_numpy_array_equal(uniques, exp)
labels, uniques = algos.factorize(list(reversed(np.arange(5.))),
sort=True)
exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
exp = np.array([0., 1., 2., 3., 4.], dtype=np.float64)
self.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])
labels, uniques = algos.factorize(x)
exp = np.array([0, 0, -1, 1, 2, 3], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
exp = pd.Index(['A', 'B', 3.14, np.inf])
tm.assert_index_equal(uniques, exp)
labels, uniques = algos.factorize(x, sort=True)
exp = np.array([2, 2, -1, 3, 0, 1], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
exp = pd.Index([3.14, np.inf, 'A', 'B'])
tm.assert_index_equal(uniques, exp)
def test_datelike(self):
# M8
v1 = pd.Timestamp('20130101 09:00:00.00004')
v2 = pd.Timestamp('20130101')
x = Series([v1, v1, v1, v2, v2, v1])
labels, uniques = algos.factorize(x)
exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
exp = pd.DatetimeIndex([v1, v2])
self.assert_index_equal(uniques, exp)
labels, uniques = algos.factorize(x, sort=True)
exp = np.array([1, 1, 1, 0, 0, 1], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
exp = pd.DatetimeIndex([v2, v1])
self.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
labels, uniques = algos.factorize(x)
exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
self.assert_index_equal(uniques, pd.PeriodIndex([v1, v2]))
labels, uniques = algos.factorize(x, sort=True)
exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
self.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])
labels, uniques = algos.factorize(x)
exp = np.array([0, 1, 0, 0, 1, 1, 0], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
self.assert_index_equal(uniques, pd.to_timedelta([v1, v2]))
labels, uniques = algos.factorize(x, sort=True)
exp = np.array([1, 0, 1, 1, 0, 0, 1], dtype=np.intp)
self.assert_numpy_array_equal(labels, exp)
self.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 = hashtable.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')
self.assertEqual(len(set(key)), len(set(expected)))
self.assertTrue(np.array_equal(
pd.isnull(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')
self.assertEqual(len(set(key)), len(set(expected)))
self.assertTrue(
np.array_equal(pd.isnull(key), expected == na_sentinel))
def test_complex_sorting(self):
# gh 12666 - check no segfault
# Test not valid numpy versions older than 1.11
if pd._np_version_under1p11:
self.skipTest("Test valid only for numpy 1.11+")
x17 = np.array([complex(i) for i in range(17)], dtype=object)
self.assertRaises(TypeError, algos.factorize, x17[::-1], sort=True)
def test_uint64_factorize(self):
data = np.array([2**63, 1, 2**63], dtype=np.uint64)
exp_labels = np.array([0, 1, 0], dtype=np.intp)
exp_uniques = np.array([2**63, 1], dtype=np.uint64)
labels, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(labels, exp_labels)
tm.assert_numpy_array_equal(uniques, exp_uniques)
data = np.array([2**63, -1, 2**63], dtype=object)
exp_labels = np.array([0, 1, 0], dtype=np.intp)
exp_uniques = np.array([2**63, -1], dtype=object)
labels, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(labels, exp_labels)
tm.assert_numpy_array_equal(uniques, exp_uniques)
class TestUnique(tm.TestCase):
def test_ints(self):
arr = np.random.randint(0, 100, size=50)
result = algos.unique(arr)
tm.assertIsInstance(result, np.ndarray)
def test_objects(self):
arr = np.random.randint(0, 100, size=50).astype('O')
result = algos.unique(arr)
tm.assertIsInstance(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_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+0000',
'2015-01-01T00:00:00.000000000+0000',
'2015-01-01T00:00:00.000000000+0000'])
result = algos.unique(dt_index)
tm.assert_numpy_array_equal(result, expected)
self.assertEqual(result.dtype, expected.dtype)
s = pd.Series(dt_index)
result = algos.unique(s)
tm.assert_numpy_array_equal(result, expected)
self.assertEqual(result.dtype, expected.dtype)
arr = s.values
result = algos.unique(arr)
tm.assert_numpy_array_equal(result, expected)
self.assertEqual(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)
self.assertEqual(result.dtype, expected.dtype)
s = pd.Series(td_index)
result = algos.unique(s)
tm.assert_numpy_array_equal(result, expected)
self.assertEqual(result.dtype, expected.dtype)
arr = s.values
result = algos.unique(arr)
tm.assert_numpy_array_equal(result, expected)
self.assertEqual(result.dtype, expected.dtype)
def test_uint64_overflow(self):
s = pd.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)
class TestIsin(tm.TestCase):
def test_invalid(self):
self.assertRaises(TypeError, lambda: algos.isin(1, 1))
self.assertRaises(TypeError, lambda: algos.isin(1, [1]))
self.assertRaises(TypeError, lambda: 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(pd.Series([1, 2]), [1])
expected = np.array([True, False])
tm.assert_numpy_array_equal(result, expected)
result = algos.isin(pd.Series([1, 2]), pd.Series([1]))
expected = np.array([True, False])
tm.assert_numpy_array_equal(result, expected)
result = algos.isin(pd.Series([1, 2]), set([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(pd.Series(['a', 'b']), pd.Series(['a']))
expected = np.array([True, False])
tm.assert_numpy_array_equal(result, expected)
result = algos.isin(pd.Series(['a', 'b']), set(['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)
class TestValueCounts(tm.TestCase):
def test_value_counts(self):
np.random.seed(1234)
from pandas.tools.tile import cut
arr = np.random.randn(4)
factor = cut(arr, 4)
tm.assertIsInstance(factor, Categorical)
result = algos.value_counts(factor)
cats = ['(-1.194, -0.535]', '(-0.535, 0.121]', '(0.121, 0.777]',
'(0.777, 1.433]']
expected_index = CategoricalIndex(cats, cats, ordered=True)
expected = Series([1, 1, 1, 1], index=expected_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)
self.assertEqual(result.tolist(), [4])
self.assertEqual(result.index[0], 0.997)
result = algos.value_counts(s, bins=2, sort=False)
self.assertEqual(result.tolist(), [2, 2])
self.assertEqual(result.index[0], 0.997)
self.assertEqual(result.index[1], 2.5)
def test_value_counts_dtypes(self):
result = algos.value_counts([1, 1.])
self.assertEqual(len(result), 1)
result = algos.value_counts([1, 1.], bins=1)
self.assertEqual(len(result), 1)
result = algos.value_counts(Series([1, 1., '1'])) # object
self.assertEqual(len(result), 2)
self.assertRaises(TypeError, lambda s: algos.value_counts(s, bins=1),
['1', 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)
self.assertEqual(len(vc), 1)
self.assertEqual(len(vc_with_na), 2)
exp_dt = pd.Series({pd.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 = pd.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 = pd.Index([datetime(3000, 1, 1), datetime(5000, 1, 1),
datetime(6000, 1, 1)], dtype=object)
exp = pd.Series([3, 2, 1], index=exp_index)
tm.assert_series_equal(res, exp)
# GH 12424
res = pd.to_datetime(pd.Series(['2362-01-01', np.nan]),
errors='ignore')
exp = pd.Series(['2362-01-01', np.nan], dtype=object)
tm.assert_series_equal(res, exp)
def test_categorical(self):
s = Series(pd.Categorical(list('aaabbc')))
result = s.value_counts()
expected = pd.Series([3, 2, 1],
index=pd.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(pd.Categorical(list('aaaaabbbcc'))) # 4,3,2,1 (nan)
s.iloc[1] = np.nan
result = s.value_counts()
expected = pd.Series([4, 3, 2], index=pd.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 = pd.Series([
4, 3, 2, 1
], index=pd.CategoricalIndex(['a', 'b', 'c', np.nan]))
tm.assert_series_equal(result, expected, check_index_type=True)
# out of order
s = Series(pd.Categorical(
list('aaaaabbbcc'), ordered=True, categories=['b', 'a', 'c']))
s.iloc[1] = np.nan
result = s.value_counts()
expected = pd.Series([4, 3, 2], index=pd.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 = pd.Series([4, 3, 2, 1], index=pd.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(pd.Categorical(
list('bbbaac'), categories=list('abcd'), ordered=True))
result = s.value_counts()
expected = Series([3, 2, 1, 0], index=pd.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(
pd.Series([True, True, False]).value_counts(dropna=True),
pd.Series([2, 1], index=[True, False]))
tm.assert_series_equal(
pd.Series([True, True, False]).value_counts(dropna=False),
pd.Series([2, 1], index=[True, False]))
tm.assert_series_equal(
pd.Series([True, True, False, None]).value_counts(dropna=True),
pd.Series([2, 1], index=[True, False]))
tm.assert_series_equal(
pd.Series([True, True, False, None]).value_counts(dropna=False),
pd.Series([2, 1, 1], index=[True, False, np.nan]))
tm.assert_series_equal(
pd.Series([10.3, 5., 5.]).value_counts(dropna=True),
pd.Series([2, 1], index=[5., 10.3]))
tm.assert_series_equal(
pd.Series([10.3, 5., 5.]).value_counts(dropna=False),
pd.Series([2, 1], index=[5., 10.3]))
tm.assert_series_equal(
pd.Series([10.3, 5., 5., None]).value_counts(dropna=True),
pd.Series([2, 1], index=[5., 10.3]))
# 32-bit linux has a different ordering
if not compat.is_platform_32bit():
tm.assert_series_equal(
pd.Series([10.3, 5., 5., None]).value_counts(dropna=False),
pd.Series([2, 1, 1], index=[5., 10.3, np.nan]))
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(tm.TestCase):
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)
def test_numeric_object_likes(self):
cases = [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)]
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 [pd.Index(case), pd.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 [pd.Series(case), pd.Series(case, dtype='category')]:
res_first = s.duplicated(keep='first')
tm.assert_series_equal(res_first, pd.Series(exp_first))
res_last = s.duplicated(keep='last')
tm.assert_series_equal(res_last, pd.Series(exp_last))
res_false = s.duplicated(keep=False)
tm.assert_series_equal(res_false, pd.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([pd.Timestamp(d) for d in dt]),
np.array([pd.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 [pd.Index(case), pd.Index(case, dtype='category'),
pd.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 [pd.Series(case), pd.Series(case, dtype='category'),
pd.Series(case, dtype=object)]:
res_first = s.duplicated(keep='first')
tm.assert_series_equal(res_first, pd.Series(exp_first))
res_last = s.duplicated(keep='last')
tm.assert_series_equal(res_last, pd.Series(exp_last))
res_false = s.duplicated(keep=False)
tm.assert_series_equal(res_false, pd.Series(exp_false))
def test_unique_index(self):
cases = [pd.Index([1, 2, 3]), pd.RangeIndex(0, 3)]
for case in cases:
self.assertTrue(case.is_unique)
tm.assert_numpy_array_equal(case.duplicated(),
np.array([False, False, False]))
class GroupVarTestMixin(object):
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)
self.assertTrue(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)
self.assertTrue(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)
self.assertTrue(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)
self.assertEqual(counts[0], 3)
self.assertTrue(out[0, 0] >= 0)
tm.assert_almost_equal(out[0, 0], 0.0)
class TestGroupVarFloat64(tm.TestCase, GroupVarTestMixin):
__test__ = True
algo = 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)
self.assertEqual(counts[0], 10 ** 6)
tm.assert_almost_equal(out[0, 0], 1.0 / 12, check_less_precise=True)
class TestGroupVarFloat32(tm.TestCase, GroupVarTestMixin):
__test__ = True
algo = libgroupby.group_var_float32
dtype = np.float32
rtol = 1e-2
class TestHashTable(tm.TestCase):
def test_lookup_nan(self):
xs = np.array([2.718, 3.14, np.nan, -7, 5, 2, 3])
m = hashtable.Float64HashTable()
m.map_locations(xs)
self.assert_numpy_array_equal(m.lookup(xs),
np.arange(len(xs), dtype=np.int64))
def test_lookup_overflow(self):
xs = np.array([1, 2, 2**63], dtype=np.uint64)
m = hashtable.UInt64HashTable()
m.map_locations(xs)
self.assert_numpy_array_equal(m.lookup(xs),
np.arange(len(xs), dtype=np.int64))
def test_get_unique(self):
s = pd.Series([1, 2, 2**63, 2**63], dtype=np.uint64)
exp = np.array([1, 2, 2**63], dtype=np.uint64)
self.assert_numpy_array_equal(s.unique(), exp)
def test_vector_resize(self):
# Test for memory errors after internal vector
# reallocations (pull request #7157)
def _test_vector_resize(htable, uniques, dtype, nvals):
vals = np.array(np.random.randn(1000), dtype=dtype)
# get_labels appends to the vector
htable.get_labels(vals[:nvals], uniques, 0, -1)
# to_array resizes the vector
uniques.to_array()
htable.get_labels(vals, uniques, 0, -1)
test_cases = [
(hashtable.PyObjectHashTable, hashtable.ObjectVector, 'object'),
(hashtable.StringHashTable, hashtable.ObjectVector, 'object'),
(hashtable.Float64HashTable, hashtable.Float64Vector, 'float64'),
(hashtable.Int64HashTable, hashtable.Int64Vector, 'int64'),
(hashtable.UInt64HashTable, hashtable.UInt64Vector, 'uint64')]
for (tbl, vect, dtype) in test_cases:
# resizing to empty is a special case
_test_vector_resize(tbl(), vect(), dtype, 0)
_test_vector_resize(tbl(), vect(), dtype, 10)
def test_quantile():
s = Series(np.random.randn(100))
result = algos.quantile(s, [0, .25, .5, .75, 1.])
expected = algos.quantile(s.values, [0, .25, .5, .75, 1.])
tm.assert_almost_equal(result, expected)
def test_unique_label_indices():
a = np.random.randint(1, 1 << 10, 1 << 15).astype('i8')
left = 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 = 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(tm.TestCase):
def test_scipy_compat(self):
tm._skip_if_no_scipy()
from scipy.stats import rankdata