from datetime import datetime
import struct
import numpy as np
import pytest
from pandas._libs import (
algos as libalgos,
hashtable as ht,
)
from pandas.core.dtypes.common import (
is_bool_dtype,
is_complex_dtype,
is_float_dtype,
is_integer_dtype,
is_object_dtype,
)
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
MultiIndex,
NaT,
Period,
PeriodIndex,
Series,
Timedelta,
Timestamp,
cut,
date_range,
timedelta_range,
to_datetime,
to_timedelta,
)
import pandas._testing as tm
import pandas.core.algorithms as algos
from pandas.core.arrays import (
DatetimeArray,
TimedeltaArray,
)
import pandas.core.common as com
class TestFactorize:
def test_factorize_complex(self):
# GH#17927
array = [1, 2, 2 + 1j]
msg = "factorize with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
labels, uniques = algos.factorize(array)
expected_labels = np.array([0, 1, 2], dtype=np.intp)
tm.assert_numpy_array_equal(labels, expected_labels)
# Should return a complex dtype in the future
expected_uniques = np.array([(1 + 0j), (2 + 0j), (2 + 1j)], dtype=object)
tm.assert_numpy_array_equal(uniques, expected_uniques)
@pytest.mark.parametrize("sort", [True, False])
def test_factorize(self, index_or_series_obj, sort):
obj = index_or_series_obj
result_codes, result_uniques = obj.factorize(sort=sort)
constructor = Index
if isinstance(obj, MultiIndex):
constructor = MultiIndex.from_tuples
expected_arr = obj.unique()
if expected_arr.dtype == np.float16:
expected_arr = expected_arr.astype(np.float32)
expected_uniques = constructor(expected_arr)
if (
isinstance(obj, Index)
and expected_uniques.dtype == bool
and obj.dtype == object
):
expected_uniques = expected_uniques.astype(object)
if sort:
expected_uniques = expected_uniques.sort_values()
# construct an integer ndarray so that
# `expected_uniques.take(expected_codes)` is equal to `obj`
expected_uniques_list = list(expected_uniques)
expected_codes = [expected_uniques_list.index(val) for val in obj]
expected_codes = np.asarray(expected_codes, dtype=np.intp)
tm.assert_numpy_array_equal(result_codes, expected_codes)
tm.assert_index_equal(result_uniques, expected_uniques, exact=True)
def test_series_factorize_use_na_sentinel_false(self):
# GH#35667
values = np.array([1, 2, 1, np.nan])
ser = Series(values)
codes, uniques = ser.factorize(use_na_sentinel=False)
expected_codes = np.array([0, 1, 0, 2], dtype=np.intp)
expected_uniques = Index([1.0, 2.0, np.nan])
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_index_equal(uniques, expected_uniques)
def test_basic(self):
items = np.array(["a", "b", "b", "a", "a", "c", "c", "c"], dtype=object)
codes, uniques = algos.factorize(items)
tm.assert_numpy_array_equal(uniques, np.array(["a", "b", "c"], dtype=object))
codes, uniques = algos.factorize(items, sort=True)
exp = np.array([0, 1, 1, 0, 0, 2, 2, 2], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array(["a", "b", "c"], dtype=object)
tm.assert_numpy_array_equal(uniques, exp)
arr = np.arange(5, dtype=np.intp)[::-1]
codes, uniques = algos.factorize(arr)
exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array([4, 3, 2, 1, 0], dtype=arr.dtype)
tm.assert_numpy_array_equal(uniques, exp)
codes, uniques = algos.factorize(arr, sort=True)
exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array([0, 1, 2, 3, 4], dtype=arr.dtype)
tm.assert_numpy_array_equal(uniques, exp)
arr = np.arange(5.0)[::-1]
codes, uniques = algos.factorize(arr)
exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array([4.0, 3.0, 2.0, 1.0, 0.0], dtype=arr.dtype)
tm.assert_numpy_array_equal(uniques, exp)
codes, uniques = algos.factorize(arr, sort=True)
exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=arr.dtype)
tm.assert_numpy_array_equal(uniques, exp)
def test_mixed(self):
# doc example reshaping.rst
x = Series(["A", "A", np.nan, "B", 3.14, np.inf])
codes, uniques = algos.factorize(x)
exp = np.array([0, 0, -1, 1, 2, 3], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = Index(["A", "B", 3.14, np.inf])
tm.assert_index_equal(uniques, exp)
codes, uniques = algos.factorize(x, sort=True)
exp = np.array([2, 2, -1, 3, 0, 1], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = Index([3.14, np.inf, "A", "B"])
tm.assert_index_equal(uniques, exp)
def test_factorize_datetime64(self):
# M8
v1 = Timestamp("20130101 09:00:00.00004")
v2 = Timestamp("20130101")
x = Series([v1, v1, v1, v2, v2, v1])
codes, uniques = algos.factorize(x)
exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = DatetimeIndex([v1, v2])
tm.assert_index_equal(uniques, exp)
codes, uniques = algos.factorize(x, sort=True)
exp = np.array([1, 1, 1, 0, 0, 1], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
exp = DatetimeIndex([v2, v1])
tm.assert_index_equal(uniques, exp)
def test_factorize_period(self):
# period
v1 = Period("201302", freq="M")
v2 = Period("201303", freq="M")
x = Series([v1, v1, v1, v2, v2, v1])
# periods are not 'sorted' as they are converted back into an index
codes, uniques = algos.factorize(x)
exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
tm.assert_index_equal(uniques, PeriodIndex([v1, v2]))
codes, uniques = algos.factorize(x, sort=True)
exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
tm.assert_index_equal(uniques, PeriodIndex([v1, v2]))
def test_factorize_timedelta(self):
# GH 5986
v1 = to_timedelta("1 day 1 min")
v2 = to_timedelta("1 day")
x = Series([v1, v2, v1, v1, v2, v2, v1])
codes, uniques = algos.factorize(x)
exp = np.array([0, 1, 0, 0, 1, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
tm.assert_index_equal(uniques, to_timedelta([v1, v2]))
codes, uniques = algos.factorize(x, sort=True)
exp = np.array([1, 0, 1, 1, 0, 0, 1], dtype=np.intp)
tm.assert_numpy_array_equal(codes, exp)
tm.assert_index_equal(uniques, to_timedelta([v2, v1]))
def test_factorize_nan(self):
# nan should map to na_sentinel, not reverse_indexer[na_sentinel]
# rizer.factorize should not raise an exception if na_sentinel indexes
# outside of reverse_indexer
key = np.array([1, 2, 1, np.nan], dtype="O")
rizer = ht.ObjectFactorizer(len(key))
for na_sentinel in (-1, 20):
ids = rizer.factorize(key, na_sentinel=na_sentinel)
expected = np.array([0, 1, 0, na_sentinel], dtype=np.intp)
assert len(set(key)) == len(set(expected))
tm.assert_numpy_array_equal(pd.isna(key), expected == na_sentinel)
tm.assert_numpy_array_equal(ids, expected)
def test_factorizer_with_mask(self):
# GH#49549
data = np.array([1, 2, 3, 1, 1, 0], dtype="int64")
mask = np.array([False, False, False, False, False, True])
rizer = ht.Int64Factorizer(len(data))
result = rizer.factorize(data, mask=mask)
expected = np.array([0, 1, 2, 0, 0, -1], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
expected_uniques = np.array([1, 2, 3], dtype="int64")
tm.assert_numpy_array_equal(rizer.uniques.to_array(), expected_uniques)
def test_factorizer_object_with_nan(self):
# GH#49549
data = np.array([1, 2, 3, 1, np.nan])
rizer = ht.ObjectFactorizer(len(data))
result = rizer.factorize(data.astype(object))
expected = np.array([0, 1, 2, 0, -1], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
expected_uniques = np.array([1, 2, 3], dtype=object)
tm.assert_numpy_array_equal(rizer.uniques.to_array(), expected_uniques)
@pytest.mark.parametrize(
"data, expected_codes, expected_uniques",
[
(
[(1, 1), (1, 2), (0, 0), (1, 2), "nonsense"],
[0, 1, 2, 1, 3],
[(1, 1), (1, 2), (0, 0), "nonsense"],
),
(
[(1, 1), (1, 2), (0, 0), (1, 2), (1, 2, 3)],
[0, 1, 2, 1, 3],
[(1, 1), (1, 2), (0, 0), (1, 2, 3)],
),
([(1, 1), (1, 2), (0, 0), (1, 2)], [0, 1, 2, 1], [(1, 1), (1, 2), (0, 0)]),
],
)
def test_factorize_tuple_list(self, data, expected_codes, expected_uniques):
# GH9454
msg = "factorize with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
codes, uniques = pd.factorize(data)
tm.assert_numpy_array_equal(codes, np.array(expected_codes, dtype=np.intp))
expected_uniques_array = com.asarray_tuplesafe(expected_uniques, dtype=object)
tm.assert_numpy_array_equal(uniques, expected_uniques_array)
def test_complex_sorting(self):
# gh 12666 - check no segfault
x17 = np.array([complex(i) for i in range(17)], dtype=object)
msg = "'[<>]' not supported between instances of .*"
with pytest.raises(TypeError, match=msg):
algos.factorize(x17[::-1], sort=True)
def test_numeric_dtype_factorize(self, any_real_numpy_dtype):
# GH41132
dtype = any_real_numpy_dtype
data = np.array([1, 2, 2, 1], dtype=dtype)
expected_codes = np.array([0, 1, 1, 0], dtype=np.intp)
expected_uniques = np.array([1, 2], dtype=dtype)
codes, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
def test_float64_factorize(self, writable):
data = np.array([1.0, 1e8, 1.0, 1e-8, 1e8, 1.0], dtype=np.float64)
data.setflags(write=writable)
expected_codes = np.array([0, 1, 0, 2, 1, 0], dtype=np.intp)
expected_uniques = np.array([1.0, 1e8, 1e-8], dtype=np.float64)
codes, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
def test_uint64_factorize(self, writable):
data = np.array([2**64 - 1, 1, 2**64 - 1], dtype=np.uint64)
data.setflags(write=writable)
expected_codes = np.array([0, 1, 0], dtype=np.intp)
expected_uniques = np.array([2**64 - 1, 1], dtype=np.uint64)
codes, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
def test_int64_factorize(self, writable):
data = np.array([2**63 - 1, -(2**63), 2**63 - 1], dtype=np.int64)
data.setflags(write=writable)
expected_codes = np.array([0, 1, 0], dtype=np.intp)
expected_uniques = np.array([2**63 - 1, -(2**63)], dtype=np.int64)
codes, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
def test_string_factorize(self, writable):
data = np.array(["a", "c", "a", "b", "c"], dtype=object)
data.setflags(write=writable)
expected_codes = np.array([0, 1, 0, 2, 1], dtype=np.intp)
expected_uniques = np.array(["a", "c", "b"], dtype=object)
codes, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
def test_object_factorize(self, writable):
data = np.array(["a", "c", None, np.nan, "a", "b", NaT, "c"], dtype=object)
data.setflags(write=writable)
expected_codes = np.array([0, 1, -1, -1, 0, 2, -1, 1], dtype=np.intp)
expected_uniques = np.array(["a", "c", "b"], dtype=object)
codes, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
def test_datetime64_factorize(self, writable):
# GH35650 Verify whether read-only datetime64 array can be factorized
data = np.array([np.datetime64("2020-01-01T00:00:00.000")], dtype="M8[ns]")
data.setflags(write=writable)
expected_codes = np.array([0], dtype=np.intp)
expected_uniques = np.array(
["2020-01-01T00:00:00.000000000"], dtype="datetime64[ns]"
)
codes, uniques = pd.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
@pytest.mark.parametrize("sort", [True, False])
def test_factorize_rangeindex(self, sort):
# increasing -> sort doesn't matter
ri = pd.RangeIndex.from_range(range(10))
expected = np.arange(10, dtype=np.intp), ri
result = algos.factorize(ri, sort=sort)
tm.assert_numpy_array_equal(result[0], expected[0])
tm.assert_index_equal(result[1], expected[1], exact=True)
result = ri.factorize(sort=sort)
tm.assert_numpy_array_equal(result[0], expected[0])
tm.assert_index_equal(result[1], expected[1], exact=True)
@pytest.mark.parametrize("sort", [True, False])
def test_factorize_rangeindex_decreasing(self, sort):
# decreasing -> sort matters
ri = pd.RangeIndex.from_range(range(10))
expected = np.arange(10, dtype=np.intp), ri
ri2 = ri[::-1]
expected = expected[0], ri2
if sort:
expected = expected[0][::-1], expected[1][::-1]
result = algos.factorize(ri2, sort=sort)
tm.assert_numpy_array_equal(result[0], expected[0])
tm.assert_index_equal(result[1], expected[1], exact=True)
result = ri2.factorize(sort=sort)
tm.assert_numpy_array_equal(result[0], expected[0])
tm.assert_index_equal(result[1], expected[1], exact=True)
def test_deprecate_order(self):
# gh 19727 - check warning is raised for deprecated keyword, order.
# Test not valid once order keyword is removed.
data = np.array([2**63, 1, 2**63], dtype=np.uint64)
with pytest.raises(TypeError, match="got an unexpected keyword"):
algos.factorize(data, order=True)
with tm.assert_produces_warning(False):
algos.factorize(data)
@pytest.mark.parametrize(
"data",
[
np.array([0, 1, 0], dtype="u8"),
np.array([-(2**63), 1, -(2**63)], dtype="i8"),
np.array(["__nan__", "foo", "__nan__"], dtype="object"),
],
)
def test_parametrized_factorize_na_value_default(self, data):
# arrays that include the NA default for that type, but isn't used.
codes, uniques = algos.factorize(data)
expected_uniques = data[[0, 1]]
expected_codes = np.array([0, 1, 0], dtype=np.intp)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
@pytest.mark.parametrize(
"data, na_value",
[
(np.array([0, 1, 0, 2], dtype="u8"), 0),
(np.array([1, 0, 1, 2], dtype="u8"), 1),
(np.array([-(2**63), 1, -(2**63), 0], dtype="i8"), -(2**63)),
(np.array([1, -(2**63), 1, 0], dtype="i8"), 1),
(np.array(["a", "", "a", "b"], dtype=object), "a"),
(np.array([(), ("a", 1), (), ("a", 2)], dtype=object), ()),
(np.array([("a", 1), (), ("a", 1), ("a", 2)], dtype=object), ("a", 1)),
],
)
def test_parametrized_factorize_na_value(self, data, na_value):
codes, uniques = algos.factorize_array(data, na_value=na_value)
expected_uniques = data[[1, 3]]
expected_codes = np.array([-1, 0, -1, 1], dtype=np.intp)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_numpy_array_equal(uniques, expected_uniques)
@pytest.mark.parametrize("sort", [True, False])
@pytest.mark.parametrize(
"data, uniques",
[
(
np.array(["b", "a", None, "b"], dtype=object),
np.array(["b", "a"], dtype=object),
),
(
pd.array([2, 1, np.nan, 2], dtype="Int64"),
pd.array([2, 1], dtype="Int64"),
),
],
ids=["numpy_array", "extension_array"],
)
def test_factorize_use_na_sentinel(self, sort, data, uniques):
codes, uniques = algos.factorize(data, sort=sort, use_na_sentinel=True)
if sort:
expected_codes = np.array([1, 0, -1, 1], dtype=np.intp)
expected_uniques = algos.safe_sort(uniques)
else:
expected_codes = np.array([0, 1, -1, 0], dtype=np.intp)
expected_uniques = uniques
tm.assert_numpy_array_equal(codes, expected_codes)
if isinstance(data, np.ndarray):
tm.assert_numpy_array_equal(uniques, expected_uniques)
else:
tm.assert_extension_array_equal(uniques, expected_uniques)
@pytest.mark.parametrize(
"data, expected_codes, expected_uniques",
[
(
["a", None, "b", "a"],
np.array([0, 1, 2, 0], dtype=np.dtype("intp")),
np.array(["a", np.nan, "b"], dtype=object),
),
(
["a", np.nan, "b", "a"],
np.array([0, 1, 2, 0], dtype=np.dtype("intp")),
np.array(["a", np.nan, "b"], dtype=object),
),
],
)
def test_object_factorize_use_na_sentinel_false(
self, data, expected_codes, expected_uniques
):
codes, uniques = algos.factorize(
np.array(data, dtype=object), use_na_sentinel=False
)
tm.assert_numpy_array_equal(uniques, expected_uniques, strict_nan=True)
tm.assert_numpy_array_equal(codes, expected_codes, strict_nan=True)
@pytest.mark.parametrize(
"data, expected_codes, expected_uniques",
[
(
[1, None, 1, 2],
np.array([0, 1, 0, 2], dtype=np.dtype("intp")),
np.array([1, np.nan, 2], dtype="O"),
),
(
[1, np.nan, 1, 2],
np.array([0, 1, 0, 2], dtype=np.dtype("intp")),
np.array([1, np.nan, 2], dtype=np.float64),
),
],
)
def test_int_factorize_use_na_sentinel_false(
self, data, expected_codes, expected_uniques
):
msg = "factorize with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
codes, uniques = algos.factorize(data, use_na_sentinel=False)
tm.assert_numpy_array_equal(uniques, expected_uniques, strict_nan=True)
tm.assert_numpy_array_equal(codes, expected_codes, strict_nan=True)
@pytest.mark.parametrize(
"data, expected_codes, expected_uniques",
[
(
Index(Categorical(["a", "a", "b"])),
np.array([0, 0, 1], dtype=np.intp),
CategoricalIndex(["a", "b"], categories=["a", "b"], dtype="category"),
),
(
Series(Categorical(["a", "a", "b"])),
np.array([0, 0, 1], dtype=np.intp),
CategoricalIndex(["a", "b"], categories=["a", "b"], dtype="category"),
),
(
Series(DatetimeIndex(["2017", "2017"], tz="US/Eastern")),
np.array([0, 0], dtype=np.intp),
DatetimeIndex(["2017"], tz="US/Eastern"),
),
],
)
def test_factorize_mixed_values(self, data, expected_codes, expected_uniques):
# GH 19721
codes, uniques = algos.factorize(data)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_index_equal(uniques, expected_uniques)
def test_factorize_interval_non_nano(self, unit):
# GH#56099
left = DatetimeIndex(["2016-01-01", np.nan, "2015-10-11"]).as_unit(unit)
right = DatetimeIndex(["2016-01-02", np.nan, "2015-10-15"]).as_unit(unit)
idx = IntervalIndex.from_arrays(left, right)
codes, cats = idx.factorize()
assert cats.dtype == f"interval[datetime64[{unit}], right]"
ts = Timestamp(0).as_unit(unit)
idx2 = IntervalIndex.from_arrays(left - ts, right - ts)
codes2, cats2 = idx2.factorize()
assert cats2.dtype == f"interval[timedelta64[{unit}], right]"
idx3 = IntervalIndex.from_arrays(
left.tz_localize("US/Pacific"), right.tz_localize("US/Pacific")
)
codes3, cats3 = idx3.factorize()
assert cats3.dtype == f"interval[datetime64[{unit}, US/Pacific], right]"
class TestUnique:
def test_ints(self):
arr = np.random.default_rng(2).integers(0, 100, size=50)
result = algos.unique(arr)
assert isinstance(result, np.ndarray)
def test_objects(self):
arr = np.random.default_rng(2).integers(0, 100, size=50).astype("O")
result = algos.unique(arr)
assert isinstance(result, np.ndarray)
def test_object_refcount_bug(self):
lst = np.array(["A", "B", "C", "D", "E"], dtype=object)
for i in range(1000):
len(algos.unique(lst))
def test_on_index_object(self):
mindex = MultiIndex.from_arrays(
[np.arange(5).repeat(5), np.tile(np.arange(5), 5)]
)
expected = mindex.values
expected.sort()
mindex = mindex.repeat(2)
result = pd.unique(mindex)
result.sort()
tm.assert_almost_equal(result, expected)
def test_dtype_preservation(self, any_numpy_dtype):
# GH 15442
if any_numpy_dtype in (tm.BYTES_DTYPES + tm.STRING_DTYPES):
data = [1, 2, 2]
uniques = [1, 2]
elif is_integer_dtype(any_numpy_dtype):
data = [1, 2, 2]
uniques = [1, 2]
elif is_float_dtype(any_numpy_dtype):
data = [1, 2, 2]
uniques = [1.0, 2.0]
elif is_complex_dtype(any_numpy_dtype):
data = [complex(1, 0), complex(2, 0), complex(2, 0)]
uniques = [complex(1, 0), complex(2, 0)]
elif is_bool_dtype(any_numpy_dtype):
data = [True, True, False]
uniques = [True, False]
elif is_object_dtype(any_numpy_dtype):
data = ["A", "B", "B"]
uniques = ["A", "B"]
else:
# datetime64[ns]/M8[ns]/timedelta64[ns]/m8[ns] tested elsewhere
data = [1, 2, 2]
uniques = [1, 2]
result = Series(data, dtype=any_numpy_dtype).unique()
expected = np.array(uniques, dtype=any_numpy_dtype)
if any_numpy_dtype in tm.STRING_DTYPES:
expected = expected.astype(object)
if expected.dtype.kind in ["m", "M"]:
# We get TimedeltaArray/DatetimeArray
assert isinstance(result, (DatetimeArray, TimedeltaArray))
result = np.array(result)
tm.assert_numpy_array_equal(result, expected)
def test_datetime64_dtype_array_returned(self):
# GH 9431
expected = np.array(
[
"2015-01-03T00:00:00.000000000",
"2015-01-01T00:00:00.000000000",
],
dtype="M8[ns]",
)
dt_index = to_datetime(
[
"2015-01-03T00:00:00.000000000",
"2015-01-01T00:00:00.000000000",
"2015-01-01T00:00:00.000000000",
]
)
result = algos.unique(dt_index)
tm.assert_numpy_array_equal(result, expected)
assert result.dtype == expected.dtype
s = Series(dt_index)
result = algos.unique(s)
tm.assert_numpy_array_equal(result, expected)
assert result.dtype == expected.dtype
arr = s.values
result = algos.unique(arr)
tm.assert_numpy_array_equal(result, expected)
assert result.dtype == expected.dtype
def test_datetime_non_ns(self):
a = np.array(["2000", "2000", "2001"], dtype="datetime64[s]")
result = pd.unique(a)
expected = np.array(["2000", "2001"], dtype="datetime64[s]")
tm.assert_numpy_array_equal(result, expected)
def test_timedelta_non_ns(self):
a = np.array(["2000", "2000", "2001"], dtype="timedelta64[s]")
result = pd.unique(a)
expected = np.array([2000, 2001], dtype="timedelta64[s]")
tm.assert_numpy_array_equal(result, expected)
def test_timedelta64_dtype_array_returned(self):
# GH 9431
expected = np.array([31200, 45678, 10000], dtype="m8[ns]")
td_index = 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(np.array(duplicated_items, dtype=object))
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"))
# 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("abc")))
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, unit):
# GH 15939
dti = Index(
[
Timestamp("20160101", tz="US/Eastern"),
Timestamp("20160101", tz="US/Eastern"),
]
).as_unit(unit)
ser = Series(dti)
result = ser.unique()
expected = dti[:1]._data
tm.assert_extension_array_equal(result, expected)
result = dti.unique()
expected = dti[:1]
tm.assert_index_equal(result, expected)
result = pd.unique(ser)
expected = dti[:1]._data
tm.assert_extension_array_equal(result, expected)
result = pd.unique(dti)
expected = dti[:1]
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"))
msg = "unique with argument that is not not a Series, Index,"
with tm.assert_produces_warning(FutureWarning, match=msg):
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)
def test_order_of_appearance_dt64(self, unit):
ser = Series([Timestamp("20160101"), Timestamp("20160101")]).dt.as_unit(unit)
result = pd.unique(ser)
expected = np.array(["2016-01-01T00:00:00.000000000"], dtype=f"M8[{unit}]")
tm.assert_numpy_array_equal(result, expected)
def test_order_of_appearance_dt64tz(self, unit):
dti = DatetimeIndex(
[
Timestamp("20160101", tz="US/Eastern"),
Timestamp("20160101", tz="US/Eastern"),
]
).as_unit(unit)
result = pd.unique(dti)
expected = DatetimeIndex(
["2016-01-01 00:00:00"], dtype=f"datetime64[{unit}, US/Eastern]", freq=None
)
tm.assert_index_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
msg = "unique with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
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)
@pytest.mark.parametrize("el_type", [np.float64, object])
def test_first_nan_kept(self, el_type):
# 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
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=object)
result = pd.unique(a)
assert result.size == 2
assert a[0] is unique_nulls_fixture
assert a[1] is unique_nulls_fixture2
def test_unique_masked(self, any_numeric_ea_dtype):
# GH#48019
ser = Series([1, pd.NA, 2] * 3, dtype=any_numeric_ea_dtype)
result = pd.unique(ser)
expected = pd.array([1, pd.NA, 2], dtype=any_numeric_ea_dtype)
tm.assert_extension_array_equal(result, expected)
def test_nunique_ints(index_or_series_or_array):
# GH#36327
values = index_or_series_or_array(np.random.default_rng(2).integers(0, 20, 30))
result = algos.nunique_ints(values)
expected = len(algos.unique(values))
assert result == expected
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):
msg = "isin with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
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)
with tm.assert_produces_warning(FutureWarning, match=msg):
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)
with tm.assert_produces_warning(FutureWarning, match=msg):
result = algos.isin(["a", "b"], [1])
expected = np.array([False, False])
tm.assert_numpy_array_equal(result, expected)
def test_i8(self):
arr = 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 = 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)
@pytest.mark.parametrize("dtype1", ["m8[ns]", "M8[ns]", "M8[ns, UTC]", "period[D]"])
@pytest.mark.parametrize("dtype", ["i8", "f8", "u8"])
def test_isin_datetimelike_values_numeric_comps(self, dtype, dtype1):
# Anything but object and we get all-False shortcut
dta = date_range("2013-01-01", periods=3)._values
arr = Series(dta.view("i8")).array.view(dtype1)
comps = arr.view("i8").astype(dtype)
result = algos.isin(comps, arr)
expected = np.zeros(comps.shape, dtype=bool)
tm.assert_numpy_array_equal(result, expected)
def test_large(self):
s = 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)
@pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]", "M8[ns, UTC]", "period[D]"])
def test_isin_datetimelike_all_nat(self, dtype):
# GH#56427
dta = date_range("2013-01-01", periods=3)._values
arr = Series(dta.view("i8")).array.view(dtype)
arr[0] = NaT
result = algos.isin(arr, [NaT])
expected = np.array([True, False, False], dtype=bool)
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]", "M8[ns, UTC]"])
def test_isin_datetimelike_strings_deprecated(self, dtype):
# GH#53111
dta = date_range("2013-01-01", periods=3)._values
arr = Series(dta.view("i8")).array.view(dtype)
vals = [str(x) for x in arr]
msg = "The behavior of 'isin' with dtype=.* is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
res = algos.isin(arr, vals)
assert res.all()
vals2 = np.array(vals, dtype=str)
with tm.assert_produces_warning(FutureWarning, match=msg):
res2 = algos.isin(arr, vals2)
assert res2.all()
def test_isin_dt64tz_with_nat(self):
# the all-NaT values used to get inferred to tznaive, which was evaluated
# as non-matching GH#56427
dti = date_range("2016-01-01", periods=3, tz="UTC")
ser = Series(dti)
ser[0] = NaT
res = algos.isin(ser._values, [NaT])
exp = np.array([True, False, False], dtype=bool)
tm.assert_numpy_array_equal(res, exp)
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_categorical_isin(self):
vals = np.array([0, 1, 2, 0])
cats = ["a", "b", "c"]
cat = Categorical([1]).from_codes(vals, cats)
other = Categorical([1]).from_codes(np.array([0, 1]), cats)
expected = np.array([True, True, False, True])
result = algos.isin(cat, other)
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 jeopardize this behavior
comps = [np.nan] # could be casted to float64
values = [np.nan]
expected = np.array([True])
msg = "isin with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = algos.isin(comps, values)
tm.assert_numpy_array_equal(expected, result)
def test_same_nan_is_in_large(self):
# https://github.com/pandas-dev/pandas/issues/22205
s = np.tile(1.0, 1_000_001)
s[0] = np.nan
result = algos.isin(s, np.array([np.nan, 1]))
expected = np.ones(len(s), dtype=bool)
tm.assert_numpy_array_equal(result, expected)
def test_same_nan_is_in_large_series(self):
# https://github.com/pandas-dev/pandas/issues/22205
s = np.tile(1.0, 1_000_001)
series = Series(s)
s[0] = np.nan
result = series.isin(np.array([np.nan, 1]))
expected = Series(np.ones(len(s), dtype=bool))
tm.assert_series_equal(result, expected)
def test_same_object_is_in(self):
# GH 22160
# there could be special treatment for nans
# the user however could define a custom class
# with similar behavior, then we at least should
# fall back to usual python's behavior: "a in [a] == True"
class LikeNan:
def __eq__(self, other) -> bool:
return False
def __hash__(self):
return 0
a, b = LikeNan(), LikeNan()
msg = "isin with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
# 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(np.array(comps), values)
tm.assert_numpy_array_equal(np.array([True]), result)
# as object-array:
result = algos.isin(
np.asarray(comps, dtype=object), np.asarray(values, dtype=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])
msg = "isin with argument that is not not a Series, Index"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = algos.isin(comps, values)
tm.assert_numpy_array_equal(expected, result)
@pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
def test_empty(self, empty):
# see gh-16991
vals = Index(["a", "b"])
expected = np.array([False, False])
result = algos.isin(vals, empty)
tm.assert_numpy_array_equal(expected, result)
def test_different_nan_objects(self):
# GH 22119
comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object)
vals = np.array([float("nan")], dtype=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)
def test_isin_int_df_string_search(self):
"""Comparing df with int`s (1,2) with a string at isin() ("1")
-> should not match values because int 1 is not equal str 1"""
df = DataFrame({"values": [1, 2]})
result = df.isin(["1"])
expected_false = DataFrame({"values": [False, False]})
tm.assert_frame_equal(result, expected_false)
def test_isin_nan_df_string_search(self):
"""Comparing df with nan value (np.nan,2) with a string at isin() ("NaN")
-> should not match values because np.nan is not equal str NaN"""
df = DataFrame({"values": [np.nan, 2]})
result = df.isin(np.array(["NaN"], dtype=object))
expected_false = DataFrame({"values": [False, False]})
tm.assert_frame_equal(result, expected_false)
def test_isin_float_df_string_search(self):
"""Comparing df with floats (1.4245,2.32441) with a string at isin() ("1.4245")
-> should not match values because float 1.4245 is not equal str 1.4245"""
df = DataFrame({"values": [1.4245, 2.32441]})
result = df.isin(np.array(["1.4245"], dtype=object))
expected_false = DataFrame({"values": [False, False]})
tm.assert_frame_equal(result, expected_false)
def test_isin_unsigned_dtype(self):
# GH#46485
ser = Series([1378774140726870442], dtype=np.uint64)
result = ser.isin([1378774140726870528])
expected = Series(False)
tm.assert_series_equal(result, expected)
class TestValueCounts:
def test_value_counts(self):
arr = np.random.default_rng(1234).standard_normal(4)
factor = cut(arr, 4)
# assert isinstance(factor, n)
msg = "pandas.value_counts is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = algos.value_counts(factor)
breaks = [-1.606, -1.018, -0.431, 0.155, 0.741]
index = IntervalIndex.from_breaks(breaks).astype(CategoricalDtype(ordered=True))
expected = Series([1, 0, 2, 1], index=index, name="count")
tm.assert_series_equal(result.sort_index(), expected.sort_index())
def test_value_counts_bins(self):
s = [1, 2, 3, 4]
msg = "pandas.value_counts is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = algos.value_counts(s, bins=1)
expected = Series(
[4], index=IntervalIndex.from_tuples([(0.996, 4.0)]), name="count"
)
tm.assert_series_equal(result, expected)
with tm.assert_produces_warning(FutureWarning, match=msg):
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)]),
name="count",
)
tm.assert_series_equal(result, expected)
def test_value_counts_dtypes(self):
msg2 = "pandas.value_counts is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg2):
result = algos.value_counts(np.array([1, 1.0]))
assert len(result) == 1
with tm.assert_produces_warning(FutureWarning, match=msg2):
result = algos.value_counts(np.array([1, 1.0]), bins=1)
assert len(result) == 1
with tm.assert_produces_warning(FutureWarning, match=msg2):
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):
with tm.assert_produces_warning(FutureWarning, match=msg2):
algos.value_counts(np.array(["1", 1], dtype=object), bins=1)
def test_value_counts_nat(self):
td = Series([np.timedelta64(10000), NaT], dtype="timedelta64[ns]")
dt = to_datetime(["NaT", "2014-01-01"])
msg = "pandas.value_counts is deprecated"
for ser in [td, dt]:
with tm.assert_produces_warning(FutureWarning, match=msg):
vc = algos.value_counts(ser)
vc_with_na = algos.value_counts(ser, dropna=False)
assert len(vc) == 1
assert len(vc_with_na) == 2
exp_dt = Series({Timestamp("2014-01-01 00:00:00"): 1}, name="count")
with tm.assert_produces_warning(FutureWarning, match=msg):
result_dt = algos.value_counts(dt)
tm.assert_series_equal(result_dt, exp_dt)
exp_td = Series({np.timedelta64(10000): 1}, name="count")
with tm.assert_produces_warning(FutureWarning, match=msg):
result_td = algos.value_counts(td)
tm.assert_series_equal(result_td, exp_td)
@pytest.mark.parametrize("dtype", [object, "M8[us]"])
def test_value_counts_datetime_outofbounds(self, dtype):
# GH 13663
ser = 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),
],
dtype=dtype,
)
res = ser.value_counts()
exp_index = Index(
[datetime(3000, 1, 1), datetime(5000, 1, 1), datetime(6000, 1, 1)],
dtype=dtype,
)
exp = Series([3, 2, 1], index=exp_index, name="count")
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"]), name="count"
)
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"]),
name="count",
)
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]), name="count"
)
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,
),
name="count",
)
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
),
name="count",
)
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
),
name="count",
)
tm.assert_series_equal(result, expected, check_index_type=True)
def test_value_counts_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], name="count"),
)
tm.assert_series_equal(
Series([True, True, False]).value_counts(dropna=False),
Series([2, 1], index=[True, False], name="count"),
)
tm.assert_series_equal(
Series([True] * 3 + [False] * 2 + [None] * 5).value_counts(dropna=True),
Series([3, 2], index=Index([True, False], dtype=object), name="count"),
)
tm.assert_series_equal(
Series([True] * 5 + [False] * 3 + [None] * 2).value_counts(dropna=False),
Series([5, 3, 2], index=[True, False, None], name="count"),
)
tm.assert_series_equal(
Series([10.3, 5.0, 5.0]).value_counts(dropna=True),
Series([2, 1], index=[5.0, 10.3], name="count"),
)
tm.assert_series_equal(
Series([10.3, 5.0, 5.0]).value_counts(dropna=False),
Series([2, 1], index=[5.0, 10.3], name="count"),
)
tm.assert_series_equal(
Series([10.3, 5.0, 5.0, None]).value_counts(dropna=True),
Series([2, 1], index=[5.0, 10.3], name="count"),
)
result = Series([10.3, 10.3, 5.0, 5.0, 5.0, None]).value_counts(dropna=False)
expected = Series([3, 2, 1], index=[5.0, 10.3, None], name="count")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("dtype", (np.float64, object, "M8[ns]"))
def test_value_counts_normalized(self, dtype):
# GH12558
s = Series([1] * 2 + [2] * 3 + [np.nan] * 5)
s_typed = s.astype(dtype)
result = s_typed.value_counts(normalize=True, dropna=False)
expected = Series(
[0.5, 0.3, 0.2],
index=Series([np.nan, 2.0, 1.0], dtype=dtype),
name="proportion",
)
tm.assert_series_equal(result, expected)
result = s_typed.value_counts(normalize=True, dropna=True)
expected = Series(
[0.6, 0.4], index=Series([2.0, 1.0], dtype=dtype), name="proportion"
)
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], name="count")
msg = "pandas.value_counts is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
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], name="count")
with tm.assert_produces_warning(FutureWarning, match=msg):
result = algos.value_counts(arr)
tm.assert_series_equal(result, expected)
def test_value_counts_series(self):
# GH#54857
values = np.array([3, 1, 2, 3, 4, np.nan])
result = Series(values).value_counts(bins=3)
expected = Series(
[2, 2, 1],
index=IntervalIndex.from_tuples(
[(0.996, 2.0), (2.0, 3.0), (3.0, 4.0)], dtype="interval[float64, right]"
),
name="count",
)
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([Period(d, freq="D") for d in dt]),
np.array([np.datetime64(d) for d in dt]),
np.array([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))
@pytest.mark.parametrize("case", [Index([1, 2, 3]), pd.RangeIndex(0, 3)])
def test_unique_index(self, case):
assert case.is_unique is True
tm.assert_numpy_array_equal(case.duplicated(), np.array([False, False, False]))
@pytest.mark.parametrize(
"arr, uniques",
[
(
[(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, uniques):
# https://github.com/pandas-dev/pandas/issues/16519
expected = np.empty(len(uniques), dtype=object)
expected[:] = uniques
msg = "unique with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = pd.unique(arr)
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize(
"array,expected",
[
(
[1 + 1j, 0, 1, 1j, 1 + 2j, 1 + 2j],
# Should return a complex dtype in the future
np.array([(1 + 1j), 0j, (1 + 0j), 1j, (1 + 2j)], dtype=object),
)
],
)
def test_unique_complex_numbers(self, array, expected):
# GH 17927
msg = "unique with argument that is not not a Series"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = pd.unique(array)
tm.assert_numpy_array_equal(result, expected)
class TestHashTable:
@pytest.mark.parametrize(
"htable, data",
[
(ht.PyObjectHashTable, [f"foo_{i}" for i in range(1000)]),
(ht.StringHashTable, [f"foo_{i}" for i in range(1000)]),
(ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
(ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
(ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
],
)
def test_hashtable_unique(self, htable, data, writable):
# output of maker has guaranteed unique elements
s = Series(data)
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, 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, data",
[
(ht.PyObjectHashTable, [f"foo_{i}" for i in range(1000)]),
(ht.StringHashTable, [f"foo_{i}" for i in range(1000)]),
(ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
(ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
(ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
],
)
def test_hashtable_factorize(self, htable, writable, data):
# output of maker has guaranteed unique elements
s = Series(data)
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, 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)
class TestRank:
@pytest.mark.parametrize(
"arr",
[
[np.nan, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 3, np.nan],
[4.0, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 4.0, np.nan],
],
)
def test_scipy_compat(self, arr):
sp_stats = pytest.importorskip("scipy.stats")
arr = np.array(arr)
mask = ~np.isfinite(arr)
arr = arr.copy()
result = libalgos.rank_1d(arr)
arr[mask] = np.inf
exp = sp_stats.rankdata(arr)
exp[mask] = np.nan
tm.assert_almost_equal(result, exp)
@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
def test_basic(self, writable, dtype):
exp = np.array([1, 2], dtype=np.float64)
data = np.array([1, 100], dtype=dtype)
data.setflags(write=writable)
ser = Series(data)
result = algos.rank(ser)
tm.assert_numpy_array_equal(result, exp)
@pytest.mark.parametrize("dtype", [np.float64, np.uint64])
def test_uint64_overflow(self, dtype):
exp = np.array([1, 2], dtype=np.float64)
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_cpu
def test_pct_max_many_rows(self):
# GH 18271
values = np.arange(2**24 + 1)
result = algos.rank(values, pct=True).max()
assert result == 1
values = np.arange(2**25 + 2).reshape(2**24 + 1, 2)
result = algos.rank(values, pct=True).max()
assert result == 1
class TestMode:
def test_no_mode(self):
exp = Series([], dtype=np.float64, index=Index([], dtype=int))
tm.assert_numpy_array_equal(algos.mode(np.array([])), exp.values)
@pytest.mark.parametrize("dt", np.typecodes["AllInteger"] + np.typecodes["Float"])
def test_mode_single(self, dt):
# GH 15714
exp_single = [1]
data_single = [1]
exp_multi = [1]
data_multi = [1, 1]
ser = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
ser = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_mode_obj_int(self):
exp = Series([1], dtype=int)
tm.assert_numpy_array_equal(algos.mode(exp.values), exp.values)
exp = Series(["a", "b", "c"], dtype=object)
tm.assert_numpy_array_equal(algos.mode(exp.values), exp.values)
@pytest.mark.parametrize("dt", np.typecodes["AllInteger"] + np.typecodes["Float"])
def test_number_mode(self, dt):
exp_single = [1]
data_single = [1] * 5 + [2] * 3
exp_multi = [1, 3]
data_multi = [1] * 5 + [2] * 3 + [3] * 5
ser = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
ser = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_strobj_mode(self):
exp = ["b"]
data = ["a"] * 2 + ["b"] * 3
ser = Series(data, dtype="c")
exp = Series(exp, dtype="c")
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
@pytest.mark.parametrize("dt", [str, object])
def test_strobj_multi_char(self, dt):
exp = ["bar"]
data = ["foo"] * 2 + ["bar"] * 3
ser = Series(data, dtype=dt)
exp = Series(exp, dtype=dt)
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_datelike_mode(self):
exp = Series(["1900-05-03", "2011-01-03", "2013-01-02"], dtype="M8[ns]")
ser = Series(["2011-01-03", "2013-01-02", "1900-05-03"], dtype="M8[ns]")
tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
tm.assert_series_equal(ser.mode(), exp)
exp = Series(["2011-01-03", "2013-01-02"], dtype="M8[ns]")
ser = Series(
["2011-01-03", "2013-01-02", "1900-05-03", "2011-01-03", "2013-01-02"],
dtype="M8[ns]",
)
tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
tm.assert_series_equal(ser.mode(), exp)
def test_timedelta_mode(self):
exp = Series(["-1 days", "0 days", "1 days"], dtype="timedelta64[ns]")
ser = Series(["1 days", "-1 days", "0 days"], dtype="timedelta64[ns]")
tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
tm.assert_series_equal(ser.mode(), exp)
exp = Series(["2 min", "1 day"], dtype="timedelta64[ns]")
ser = Series(
["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"],
dtype="timedelta64[ns]",
)
tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
tm.assert_series_equal(ser.mode(), exp)
def test_mixed_dtype(self):
exp = Series(["foo"], dtype=object)
ser = Series([1, "foo", "foo"])
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_uint64_overflow(self):
exp = Series([2**63], dtype=np.uint64)
ser = Series([1, 2**63, 2**63], dtype=np.uint64)
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
exp = Series([1, 2**63], dtype=np.uint64)
ser = Series([1, 2**63], dtype=np.uint64)
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_categorical(self):
c = Categorical([1, 2])
exp = c
res = Series(c).mode()._values
tm.assert_categorical_equal(res, exp)
c = Categorical([1, "a", "a"])
exp = Categorical(["a"], categories=[1, "a"])
res = Series(c).mode()._values
tm.assert_categorical_equal(res, exp)
c = Categorical([1, 1, 2, 3, 3])
exp = Categorical([1, 3], categories=[1, 2, 3])
res = Series(c).mode()._values
tm.assert_categorical_equal(res, exp)
def test_index(self):
idx = Index([1, 2, 3])
exp = Series([1, 2, 3], dtype=np.int64)
tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
idx = Index([1, "a", "a"])
exp = Series(["a"], dtype=object)
tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
idx = Index([1, 1, 2, 3, 3])
exp = Series([1, 3], dtype=np.int64)
tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
idx = Index(
["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"],
dtype="timedelta64[ns]",
)
with pytest.raises(AttributeError, match="TimedeltaIndex"):
# algos.mode expects Arraylike, does *not* unwrap TimedeltaIndex
algos.mode(idx)
def test_ser_mode_with_name(self):
# GH 46737
ser = Series([1, 1, 3], name="foo")
result = ser.mode()
expected = Series([1], name="foo")
tm.assert_series_equal(result, expected)
class TestDiff:
@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
def test_diff_datetimelike_nat(self, dtype):
# NaT - NaT is NaT, not 0
arr = np.arange(12).astype(np.int64).view(dtype).reshape(3, 4)
arr[:, 2] = arr.dtype.type("NaT", "ns")
result = algos.diff(arr, 1, axis=0)
expected = np.ones(arr.shape, dtype="timedelta64[ns]") * 4
expected[:, 2] = np.timedelta64("NaT", "ns")
expected[0, :] = np.timedelta64("NaT", "ns")
tm.assert_numpy_array_equal(result, expected)
result = algos.diff(arr.T, 1, axis=1)
tm.assert_numpy_array_equal(result, expected.T)
def test_diff_ea_axis(self):
dta = date_range("2016-01-01", periods=3, tz="US/Pacific")._data
msg = "cannot diff DatetimeArray on axis=1"
with pytest.raises(ValueError, match=msg):
algos.diff(dta, 1, axis=1)
@pytest.mark.parametrize("dtype", ["int8", "int16"])
def test_diff_low_precision_int(self, dtype):
arr = np.array([0, 1, 1, 0, 0], dtype=dtype)
result = algos.diff(arr, 1)
expected = np.array([np.nan, 1, 0, -1, 0], dtype="float32")
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("op", [np.array, pd.array])
def test_union_with_duplicates(op):
# GH#36289
lvals = op([3, 1, 3, 4])
rvals = op([2, 3, 1, 1])
expected = op([3, 3, 1, 1, 4, 2])
if isinstance(expected, np.ndarray):
result = algos.union_with_duplicates(lvals, rvals)
tm.assert_numpy_array_equal(result, expected)
else:
result = algos.union_with_duplicates(lvals, rvals)
tm.assert_extension_array_equal(result, expected)