/
test_sort_values.py
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/
test_sort_values.py
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import numpy as np
import pytest
from pandas import (
Categorical,
DataFrame,
Series,
)
import pandas._testing as tm
class TestSeriesSortValues:
def test_sort_values(self, datetime_series):
# check indexes are reordered corresponding with the values
ser = Series([3, 2, 4, 1], ["A", "B", "C", "D"])
expected = Series([1, 2, 3, 4], ["D", "B", "A", "C"])
result = ser.sort_values()
tm.assert_series_equal(expected, result)
ts = datetime_series.copy()
ts[:5] = np.nan
vals = ts.values
result = ts.sort_values()
assert np.isnan(result[-5:]).all()
tm.assert_numpy_array_equal(result[:-5].values, np.sort(vals[5:]))
# na_position
result = ts.sort_values(na_position="first")
assert np.isnan(result[:5]).all()
tm.assert_numpy_array_equal(result[5:].values, np.sort(vals[5:]))
# something object-type
ser = Series(["A", "B"], [1, 2])
# no failure
ser.sort_values()
# ascending=False
ordered = ts.sort_values(ascending=False)
expected = np.sort(ts.dropna().values)[::-1]
tm.assert_almost_equal(expected, ordered.dropna().values)
ordered = ts.sort_values(ascending=False, na_position="first")
tm.assert_almost_equal(expected, ordered.dropna().values)
# ascending=[False] should behave the same as ascending=False
ordered = ts.sort_values(ascending=[False])
expected = ts.sort_values(ascending=False)
tm.assert_series_equal(expected, ordered)
ordered = ts.sort_values(ascending=[False], na_position="first")
expected = ts.sort_values(ascending=False, na_position="first")
tm.assert_series_equal(expected, ordered)
msg = 'For argument "ascending" expected type bool, received type NoneType.'
with pytest.raises(ValueError, match=msg):
ts.sort_values(ascending=None)
msg = r"Length of ascending \(0\) must be 1 for Series"
with pytest.raises(ValueError, match=msg):
ts.sort_values(ascending=[])
msg = r"Length of ascending \(3\) must be 1 for Series"
with pytest.raises(ValueError, match=msg):
ts.sort_values(ascending=[1, 2, 3])
msg = r"Length of ascending \(2\) must be 1 for Series"
with pytest.raises(ValueError, match=msg):
ts.sort_values(ascending=[False, False])
msg = 'For argument "ascending" expected type bool, received type str.'
with pytest.raises(ValueError, match=msg):
ts.sort_values(ascending="foobar")
# inplace=True
ts = datetime_series.copy()
return_value = ts.sort_values(ascending=False, inplace=True)
assert return_value is None
tm.assert_series_equal(ts, datetime_series.sort_values(ascending=False))
tm.assert_index_equal(
ts.index, datetime_series.sort_values(ascending=False).index
)
# GH#5856/5853
# Series.sort_values operating on a view
df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
s = df.iloc[:, 0]
s.sort_values(inplace=True)
tm.assert_series_equal(s, df.iloc[:, 0].sort_values())
def test_sort_values_categorical(self):
cat = Series(Categorical(["a", "b", "b", "a"], ordered=False))
# sort in the categories order
expected = Series(
Categorical(["a", "a", "b", "b"], ordered=False), index=[0, 3, 1, 2]
)
result = cat.sort_values()
tm.assert_series_equal(result, expected)
cat = Series(Categorical(["a", "c", "b", "d"], ordered=True))
res = cat.sort_values()
exp = np.array(["a", "b", "c", "d"], dtype=np.object_)
tm.assert_numpy_array_equal(res.__array__(), exp)
cat = Series(
Categorical(
["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True
)
)
res = cat.sort_values()
exp = np.array(["a", "b", "c", "d"], dtype=np.object_)
tm.assert_numpy_array_equal(res.__array__(), exp)
res = cat.sort_values(ascending=False)
exp = np.array(["d", "c", "b", "a"], dtype=np.object_)
tm.assert_numpy_array_equal(res.__array__(), exp)
raw_cat1 = Categorical(
["a", "b", "c", "d"], categories=["a", "b", "c", "d"], ordered=False
)
raw_cat2 = Categorical(
["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True
)
s = ["a", "b", "c", "d"]
df = DataFrame(
{"unsort": raw_cat1, "sort": raw_cat2, "string": s, "values": [1, 2, 3, 4]}
)
# Cats must be sorted in a dataframe
res = df.sort_values(by=["string"], ascending=False)
exp = np.array(["d", "c", "b", "a"], dtype=np.object_)
tm.assert_numpy_array_equal(res["sort"].values.__array__(), exp)
assert res["sort"].dtype == "category"
res = df.sort_values(by=["sort"], ascending=False)
exp = df.sort_values(by=["string"], ascending=True)
tm.assert_series_equal(res["values"], exp["values"])
assert res["sort"].dtype == "category"
assert res["unsort"].dtype == "category"
# unordered cat, but we allow this
df.sort_values(by=["unsort"], ascending=False)
# multi-columns sort
# GH#7848
df = DataFrame(
{"id": [6, 5, 4, 3, 2, 1], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
)
df["grade"] = Categorical(df["raw_grade"], ordered=True)
df["grade"] = df["grade"].cat.set_categories(["b", "e", "a"])
# sorts 'grade' according to the order of the categories
result = df.sort_values(by=["grade"])
expected = df.iloc[[1, 2, 5, 0, 3, 4]]
tm.assert_frame_equal(result, expected)
# multi
result = df.sort_values(by=["grade", "id"])
expected = df.iloc[[2, 1, 5, 4, 3, 0]]
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("inplace", [True, False])
@pytest.mark.parametrize(
"original_list, sorted_list, ignore_index, output_index",
[
([2, 3, 6, 1], [6, 3, 2, 1], True, [0, 1, 2, 3]),
([2, 3, 6, 1], [6, 3, 2, 1], False, [2, 1, 0, 3]),
],
)
def test_sort_values_ignore_index(
self, inplace, original_list, sorted_list, ignore_index, output_index
):
# GH 30114
ser = Series(original_list)
expected = Series(sorted_list, index=output_index)
kwargs = {"ignore_index": ignore_index, "inplace": inplace}
if inplace:
result_ser = ser.copy()
result_ser.sort_values(ascending=False, **kwargs)
else:
result_ser = ser.sort_values(ascending=False, **kwargs)
tm.assert_series_equal(result_ser, expected)
tm.assert_series_equal(ser, Series(original_list))
def test_mergesort_descending_stability(self):
# GH 28697
s = Series([1, 2, 1, 3], ["first", "b", "second", "c"])
result = s.sort_values(ascending=False, kind="mergesort")
expected = Series([3, 2, 1, 1], ["c", "b", "first", "second"])
tm.assert_series_equal(result, expected)
def test_sort_values_validate_ascending_for_value_error(self):
# GH41634
ser = Series([23, 7, 21])
msg = 'For argument "ascending" expected type bool, received type str.'
with pytest.raises(ValueError, match=msg):
ser.sort_values(ascending="False")
def test_sort_values_validate_ascending_functional(self, ascending):
# GH41634
ser = Series([23, 7, 21])
expected = np.sort(ser.values)
sorted_ser = ser.sort_values(ascending=ascending)
if not ascending:
expected = expected[::-1]
result = sorted_ser.values
tm.assert_numpy_array_equal(result, expected)
class TestSeriesSortingKey:
def test_sort_values_key(self):
series = Series(np.array(["Hello", "goodbye"]))
result = series.sort_values(axis=0)
expected = series
tm.assert_series_equal(result, expected)
result = series.sort_values(axis=0, key=lambda x: x.str.lower())
expected = series[::-1]
tm.assert_series_equal(result, expected)
def test_sort_values_key_nan(self):
series = Series(np.array([0, 5, np.nan, 3, 2, np.nan]))
result = series.sort_values(axis=0)
expected = series.iloc[[0, 4, 3, 1, 2, 5]]
tm.assert_series_equal(result, expected)
result = series.sort_values(axis=0, key=lambda x: x + 5)
expected = series.iloc[[0, 4, 3, 1, 2, 5]]
tm.assert_series_equal(result, expected)
result = series.sort_values(axis=0, key=lambda x: -x, ascending=False)
expected = series.iloc[[0, 4, 3, 1, 2, 5]]
tm.assert_series_equal(result, expected)