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Updated script that can be controled by Nodejs web app

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2024-11-25 12:24:18 +07:00
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lib/python3.13/site-packages/pandas/tests/series/methods/test_nlargest.py Normal file
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"""
Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo"
but are implicitly also testing nsmallest_foo.
"""
from itertools import product
import numpy as np
import pytest
import pandas as pd
from pandas import Series
import pandas._testing as tm
main_dtypes = [
"datetime",
"datetimetz",
"timedelta",
"int8",
"int16",
"int32",
"int64",
"float32",
"float64",
"uint8",
"uint16",
"uint32",
"uint64",
]
@pytest.fixture
def s_main_dtypes():
"""
A DataFrame with many dtypes
* datetime
* datetimetz
* timedelta
* [u]int{8,16,32,64}
* float{32,64}
The columns are the name of the dtype.
"""
df = pd.DataFrame(
{
"datetime": pd.to_datetime(["2003", "2002", "2001", "2002", "2005"]),
"datetimetz": pd.to_datetime(
["2003", "2002", "2001", "2002", "2005"]
).tz_localize("US/Eastern"),
"timedelta": pd.to_timedelta(["3d", "2d", "1d", "2d", "5d"]),
}
)
for dtype in [
"int8",
"int16",
"int32",
"int64",
"float32",
"float64",
"uint8",
"uint16",
"uint32",
"uint64",
]:
df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype)
return df
@pytest.fixture(params=main_dtypes)
def s_main_dtypes_split(request, s_main_dtypes):
"""Each series in s_main_dtypes."""
return s_main_dtypes[request.param]
def assert_check_nselect_boundary(vals, dtype, method):
# helper function for 'test_boundary_{dtype}' tests
ser = Series(vals, dtype=dtype)
result = getattr(ser, method)(3)
expected_idxr = [0, 1, 2] if method == "nsmallest" else [3, 2, 1]
expected = ser.loc[expected_idxr]
tm.assert_series_equal(result, expected)
class TestSeriesNLargestNSmallest:
@pytest.mark.parametrize(
"r",
[
Series([3.0, 2, 1, 2, "5"], dtype="object"),
Series([3.0, 2, 1, 2, 5], dtype="object"),
# not supported on some archs
# Series([3., 2, 1, 2, 5], dtype='complex256'),
Series([3.0, 2, 1, 2, 5], dtype="complex128"),
Series(list("abcde")),
Series(list("abcde"), dtype="category"),
],
)
def test_nlargest_error(self, r):
dt = r.dtype
msg = f"Cannot use method 'n(largest|smallest)' with dtype {dt}"
args = 2, len(r), 0, -1
methods = r.nlargest, r.nsmallest
for method, arg in product(methods, args):
with pytest.raises(TypeError, match=msg):
method(arg)
def test_nsmallest_nlargest(self, s_main_dtypes_split):
# float, int, datetime64 (use i8), timedelts64 (same),
# object that are numbers, object that are strings
ser = s_main_dtypes_split
tm.assert_series_equal(ser.nsmallest(2), ser.iloc[[2, 1]])
tm.assert_series_equal(ser.nsmallest(2, keep="last"), ser.iloc[[2, 3]])
empty = ser.iloc[0:0]
tm.assert_series_equal(ser.nsmallest(0), empty)
tm.assert_series_equal(ser.nsmallest(-1), empty)
tm.assert_series_equal(ser.nlargest(0), empty)
tm.assert_series_equal(ser.nlargest(-1), empty)
tm.assert_series_equal(ser.nsmallest(len(ser)), ser.sort_values())
tm.assert_series_equal(ser.nsmallest(len(ser) + 1), ser.sort_values())
tm.assert_series_equal(ser.nlargest(len(ser)), ser.iloc[[4, 0, 1, 3, 2]])
tm.assert_series_equal(ser.nlargest(len(ser) + 1), ser.iloc[[4, 0, 1, 3, 2]])
def test_nlargest_misc(self):
ser = Series([3.0, np.nan, 1, 2, 5])
result = ser.nlargest()
expected = ser.iloc[[4, 0, 3, 2, 1]]
tm.assert_series_equal(result, expected)
result = ser.nsmallest()
expected = ser.iloc[[2, 3, 0, 4, 1]]
tm.assert_series_equal(result, expected)
msg = 'keep must be either "first", "last"'
with pytest.raises(ValueError, match=msg):
ser.nsmallest(keep="invalid")
with pytest.raises(ValueError, match=msg):
ser.nlargest(keep="invalid")
# GH#15297
ser = Series([1] * 5, index=[1, 2, 3, 4, 5])
expected_first = Series([1] * 3, index=[1, 2, 3])
expected_last = Series([1] * 3, index=[5, 4, 3])
result = ser.nsmallest(3)
tm.assert_series_equal(result, expected_first)
result = ser.nsmallest(3, keep="last")
tm.assert_series_equal(result, expected_last)
result = ser.nlargest(3)
tm.assert_series_equal(result, expected_first)
result = ser.nlargest(3, keep="last")
tm.assert_series_equal(result, expected_last)
@pytest.mark.parametrize("n", range(1, 5))
def test_nlargest_n(self, n):
# GH 13412
ser = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
result = ser.nlargest(n)
expected = ser.sort_values(ascending=False).head(n)
tm.assert_series_equal(result, expected)
result = ser.nsmallest(n)
expected = ser.sort_values().head(n)
tm.assert_series_equal(result, expected)
def test_nlargest_boundary_integer(self, nselect_method, any_int_numpy_dtype):
# GH#21426
dtype_info = np.iinfo(any_int_numpy_dtype)
min_val, max_val = dtype_info.min, dtype_info.max
vals = [min_val, min_val + 1, max_val - 1, max_val]
assert_check_nselect_boundary(vals, any_int_numpy_dtype, nselect_method)
def test_nlargest_boundary_float(self, nselect_method, float_numpy_dtype):
# GH#21426
dtype_info = np.finfo(float_numpy_dtype)
min_val, max_val = dtype_info.min, dtype_info.max
min_2nd, max_2nd = np.nextafter([min_val, max_val], 0, dtype=float_numpy_dtype)
vals = [min_val, min_2nd, max_2nd, max_val]
assert_check_nselect_boundary(vals, float_numpy_dtype, nselect_method)
@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
def test_nlargest_boundary_datetimelike(self, nselect_method, dtype):
# GH#21426
# use int64 bounds and +1 to min_val since true minimum is NaT
# (include min_val/NaT at end to maintain same expected_idxr)
dtype_info = np.iinfo("int64")
min_val, max_val = dtype_info.min, dtype_info.max
vals = [min_val + 1, min_val + 2, max_val - 1, max_val, min_val]
assert_check_nselect_boundary(vals, dtype, nselect_method)
def test_nlargest_duplicate_keep_all_ties(self):
# see GH#16818
ser = Series([10, 9, 8, 7, 7, 7, 7, 6])
result = ser.nlargest(4, keep="all")
expected = Series([10, 9, 8, 7, 7, 7, 7])
tm.assert_series_equal(result, expected)
result = ser.nsmallest(2, keep="all")
expected = Series([6, 7, 7, 7, 7], index=[7, 3, 4, 5, 6])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data,expected", [([True, False], [True]), ([True, False, True, True], [True])]
)
def test_nlargest_boolean(self, data, expected):
# GH#26154 : ensure True > False
ser = Series(data)
result = ser.nlargest(1)
expected = Series(expected)
tm.assert_series_equal(result, expected)
def test_nlargest_nullable(self, any_numeric_ea_dtype):
# GH#42816
dtype = any_numeric_ea_dtype
if dtype.startswith("UInt"):
# Can't cast from negative float to uint on some platforms
arr = np.random.default_rng(2).integers(1, 10, 10)
else:
arr = np.random.default_rng(2).standard_normal(10)
arr = arr.astype(dtype.lower(), copy=False)
ser = Series(arr.copy(), dtype=dtype)
ser[1] = pd.NA
result = ser.nlargest(5)
expected = (
Series(np.delete(arr, 1), index=ser.index.delete(1))
.nlargest(5)
.astype(dtype)
)
tm.assert_series_equal(result, expected)
def test_nsmallest_nan_when_keep_is_all(self):
# GH#46589
s = Series([1, 2, 3, 3, 3, None])
result = s.nsmallest(3, keep="all")
expected = Series([1.0, 2.0, 3.0, 3.0, 3.0])
tm.assert_series_equal(result, expected)
s = Series([1, 2, None, None, None])
result = s.nsmallest(3, keep="all")
expected = Series([1, 2, None, None, None])
tm.assert_series_equal(result, expected)