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craftbeerpi4-pione/venv3/lib/python3.7/site-packages/pandas/tests/groupby/test_rank.py
Manuel Fritsch 441e235d1f "fix cli main_dir error"
2021-03-03 23:49:41 +01:00

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import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series, concat
import pandas._testing as tm
from pandas.core.base import DataError
def test_rank_apply():
lev1 = tm.rands_array(10, 100)
lev2 = tm.rands_array(10, 130)
lab1 = np.random.randint(0, 100, size=500)
lab2 = np.random.randint(0, 130, size=500)
df = DataFrame(
{
"value": np.random.randn(500),
"key1": lev1.take(lab1),
"key2": lev2.take(lab2),
}
)
result = df.groupby(["key1", "key2"]).value.rank()
expected = [piece.value.rank() for key, piece in df.groupby(["key1", "key2"])]
expected = concat(expected, axis=0)
expected = expected.reindex(result.index)
tm.assert_series_equal(result, expected)
result = df.groupby(["key1", "key2"]).value.rank(pct=True)
expected = [
piece.value.rank(pct=True) for key, piece in df.groupby(["key1", "key2"])
]
expected = concat(expected, axis=0)
expected = expected.reindex(result.index)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
@pytest.mark.parametrize(
"vals",
[
[2, 2, 8, 2, 6],
[
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-08"),
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-06"),
],
],
)
@pytest.mark.parametrize(
"ties_method,ascending,pct,exp",
[
("average", True, False, [2.0, 2.0, 5.0, 2.0, 4.0]),
("average", True, True, [0.4, 0.4, 1.0, 0.4, 0.8]),
("average", False, False, [4.0, 4.0, 1.0, 4.0, 2.0]),
("average", False, True, [0.8, 0.8, 0.2, 0.8, 0.4]),
("min", True, False, [1.0, 1.0, 5.0, 1.0, 4.0]),
("min", True, True, [0.2, 0.2, 1.0, 0.2, 0.8]),
("min", False, False, [3.0, 3.0, 1.0, 3.0, 2.0]),
("min", False, True, [0.6, 0.6, 0.2, 0.6, 0.4]),
("max", True, False, [3.0, 3.0, 5.0, 3.0, 4.0]),
("max", True, True, [0.6, 0.6, 1.0, 0.6, 0.8]),
("max", False, False, [5.0, 5.0, 1.0, 5.0, 2.0]),
("max", False, True, [1.0, 1.0, 0.2, 1.0, 0.4]),
("first", True, False, [1.0, 2.0, 5.0, 3.0, 4.0]),
("first", True, True, [0.2, 0.4, 1.0, 0.6, 0.8]),
("first", False, False, [3.0, 4.0, 1.0, 5.0, 2.0]),
("first", False, True, [0.6, 0.8, 0.2, 1.0, 0.4]),
("dense", True, False, [1.0, 1.0, 3.0, 1.0, 2.0]),
("dense", True, True, [1.0 / 3.0, 1.0 / 3.0, 3.0 / 3.0, 1.0 / 3.0, 2.0 / 3.0]),
("dense", False, False, [3.0, 3.0, 1.0, 3.0, 2.0]),
("dense", False, True, [3.0 / 3.0, 3.0 / 3.0, 1.0 / 3.0, 3.0 / 3.0, 2.0 / 3.0]),
],
)
def test_rank_args(grps, vals, ties_method, ascending, pct, exp):
key = np.repeat(grps, len(vals))
vals = vals * len(grps)
df = DataFrame({"key": key, "val": vals})
result = df.groupby("key").rank(method=ties_method, ascending=ascending, pct=pct)
exp_df = DataFrame(exp * len(grps), columns=["val"])
tm.assert_frame_equal(result, exp_df)
@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
@pytest.mark.parametrize(
"vals", [[-np.inf, -np.inf, np.nan, 1.0, np.nan, np.inf, np.inf]]
)
@pytest.mark.parametrize(
"ties_method,ascending,na_option,exp",
[
("average", True, "keep", [1.5, 1.5, np.nan, 3, np.nan, 4.5, 4.5]),
("average", True, "top", [3.5, 3.5, 1.5, 5.0, 1.5, 6.5, 6.5]),
("average", True, "bottom", [1.5, 1.5, 6.5, 3.0, 6.5, 4.5, 4.5]),
("average", False, "keep", [4.5, 4.5, np.nan, 3, np.nan, 1.5, 1.5]),
("average", False, "top", [6.5, 6.5, 1.5, 5.0, 1.5, 3.5, 3.5]),
("average", False, "bottom", [4.5, 4.5, 6.5, 3.0, 6.5, 1.5, 1.5]),
("min", True, "keep", [1.0, 1.0, np.nan, 3.0, np.nan, 4.0, 4.0]),
("min", True, "top", [3.0, 3.0, 1.0, 5.0, 1.0, 6.0, 6.0]),
("min", True, "bottom", [1.0, 1.0, 6.0, 3.0, 6.0, 4.0, 4.0]),
("min", False, "keep", [4.0, 4.0, np.nan, 3.0, np.nan, 1.0, 1.0]),
("min", False, "top", [6.0, 6.0, 1.0, 5.0, 1.0, 3.0, 3.0]),
("min", False, "bottom", [4.0, 4.0, 6.0, 3.0, 6.0, 1.0, 1.0]),
("max", True, "keep", [2.0, 2.0, np.nan, 3.0, np.nan, 5.0, 5.0]),
("max", True, "top", [4.0, 4.0, 2.0, 5.0, 2.0, 7.0, 7.0]),
("max", True, "bottom", [2.0, 2.0, 7.0, 3.0, 7.0, 5.0, 5.0]),
("max", False, "keep", [5.0, 5.0, np.nan, 3.0, np.nan, 2.0, 2.0]),
("max", False, "top", [7.0, 7.0, 2.0, 5.0, 2.0, 4.0, 4.0]),
("max", False, "bottom", [5.0, 5.0, 7.0, 3.0, 7.0, 2.0, 2.0]),
("first", True, "keep", [1.0, 2.0, np.nan, 3.0, np.nan, 4.0, 5.0]),
("first", True, "top", [3.0, 4.0, 1.0, 5.0, 2.0, 6.0, 7.0]),
("first", True, "bottom", [1.0, 2.0, 6.0, 3.0, 7.0, 4.0, 5.0]),
("first", False, "keep", [4.0, 5.0, np.nan, 3.0, np.nan, 1.0, 2.0]),
("first", False, "top", [6.0, 7.0, 1.0, 5.0, 2.0, 3.0, 4.0]),
("first", False, "bottom", [4.0, 5.0, 6.0, 3.0, 7.0, 1.0, 2.0]),
("dense", True, "keep", [1.0, 1.0, np.nan, 2.0, np.nan, 3.0, 3.0]),
("dense", True, "top", [2.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0]),
("dense", True, "bottom", [1.0, 1.0, 4.0, 2.0, 4.0, 3.0, 3.0]),
("dense", False, "keep", [3.0, 3.0, np.nan, 2.0, np.nan, 1.0, 1.0]),
("dense", False, "top", [4.0, 4.0, 1.0, 3.0, 1.0, 2.0, 2.0]),
("dense", False, "bottom", [3.0, 3.0, 4.0, 2.0, 4.0, 1.0, 1.0]),
],
)
def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
# GH 20561
key = np.repeat(grps, len(vals))
vals = vals * len(grps)
df = DataFrame({"key": key, "val": vals})
result = df.groupby("key").rank(
method=ties_method, ascending=ascending, na_option=na_option
)
exp_df = DataFrame(exp * len(grps), columns=["val"])
tm.assert_frame_equal(result, exp_df)
@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
@pytest.mark.parametrize(
"vals",
[
[2, 2, np.nan, 8, 2, 6, np.nan, np.nan],
[
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-02"),
np.nan,
pd.Timestamp("2018-01-08"),
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-06"),
np.nan,
np.nan,
],
],
)
@pytest.mark.parametrize(
"ties_method,ascending,na_option,pct,exp",
[
(
"average",
True,
"keep",
False,
[2.0, 2.0, np.nan, 5.0, 2.0, 4.0, np.nan, np.nan],
),
(
"average",
True,
"keep",
True,
[0.4, 0.4, np.nan, 1.0, 0.4, 0.8, np.nan, np.nan],
),
(
"average",
False,
"keep",
False,
[4.0, 4.0, np.nan, 1.0, 4.0, 2.0, np.nan, np.nan],
),
(
"average",
False,
"keep",
True,
[0.8, 0.8, np.nan, 0.2, 0.8, 0.4, np.nan, np.nan],
),
("min", True, "keep", False, [1.0, 1.0, np.nan, 5.0, 1.0, 4.0, np.nan, np.nan]),
("min", True, "keep", True, [0.2, 0.2, np.nan, 1.0, 0.2, 0.8, np.nan, np.nan]),
(
"min",
False,
"keep",
False,
[3.0, 3.0, np.nan, 1.0, 3.0, 2.0, np.nan, np.nan],
),
("min", False, "keep", True, [0.6, 0.6, np.nan, 0.2, 0.6, 0.4, np.nan, np.nan]),
("max", True, "keep", False, [3.0, 3.0, np.nan, 5.0, 3.0, 4.0, np.nan, np.nan]),
("max", True, "keep", True, [0.6, 0.6, np.nan, 1.0, 0.6, 0.8, np.nan, np.nan]),
(
"max",
False,
"keep",
False,
[5.0, 5.0, np.nan, 1.0, 5.0, 2.0, np.nan, np.nan],
),
("max", False, "keep", True, [1.0, 1.0, np.nan, 0.2, 1.0, 0.4, np.nan, np.nan]),
(
"first",
True,
"keep",
False,
[1.0, 2.0, np.nan, 5.0, 3.0, 4.0, np.nan, np.nan],
),
(
"first",
True,
"keep",
True,
[0.2, 0.4, np.nan, 1.0, 0.6, 0.8, np.nan, np.nan],
),
(
"first",
False,
"keep",
False,
[3.0, 4.0, np.nan, 1.0, 5.0, 2.0, np.nan, np.nan],
),
(
"first",
False,
"keep",
True,
[0.6, 0.8, np.nan, 0.2, 1.0, 0.4, np.nan, np.nan],
),
(
"dense",
True,
"keep",
False,
[1.0, 1.0, np.nan, 3.0, 1.0, 2.0, np.nan, np.nan],
),
(
"dense",
True,
"keep",
True,
[
1.0 / 3.0,
1.0 / 3.0,
np.nan,
3.0 / 3.0,
1.0 / 3.0,
2.0 / 3.0,
np.nan,
np.nan,
],
),
(
"dense",
False,
"keep",
False,
[3.0, 3.0, np.nan, 1.0, 3.0, 2.0, np.nan, np.nan],
),
(
"dense",
False,
"keep",
True,
[
3.0 / 3.0,
3.0 / 3.0,
np.nan,
1.0 / 3.0,
3.0 / 3.0,
2.0 / 3.0,
np.nan,
np.nan,
],
),
("average", True, "bottom", False, [2.0, 2.0, 7.0, 5.0, 2.0, 4.0, 7.0, 7.0]),
(
"average",
True,
"bottom",
True,
[0.25, 0.25, 0.875, 0.625, 0.25, 0.5, 0.875, 0.875],
),
("average", False, "bottom", False, [4.0, 4.0, 7.0, 1.0, 4.0, 2.0, 7.0, 7.0]),
(
"average",
False,
"bottom",
True,
[0.5, 0.5, 0.875, 0.125, 0.5, 0.25, 0.875, 0.875],
),
("min", True, "bottom", False, [1.0, 1.0, 6.0, 5.0, 1.0, 4.0, 6.0, 6.0]),
(
"min",
True,
"bottom",
True,
[0.125, 0.125, 0.75, 0.625, 0.125, 0.5, 0.75, 0.75],
),
("min", False, "bottom", False, [3.0, 3.0, 6.0, 1.0, 3.0, 2.0, 6.0, 6.0]),
(
"min",
False,
"bottom",
True,
[0.375, 0.375, 0.75, 0.125, 0.375, 0.25, 0.75, 0.75],
),
("max", True, "bottom", False, [3.0, 3.0, 8.0, 5.0, 3.0, 4.0, 8.0, 8.0]),
("max", True, "bottom", True, [0.375, 0.375, 1.0, 0.625, 0.375, 0.5, 1.0, 1.0]),
("max", False, "bottom", False, [5.0, 5.0, 8.0, 1.0, 5.0, 2.0, 8.0, 8.0]),
(
"max",
False,
"bottom",
True,
[0.625, 0.625, 1.0, 0.125, 0.625, 0.25, 1.0, 1.0],
),
("first", True, "bottom", False, [1.0, 2.0, 6.0, 5.0, 3.0, 4.0, 7.0, 8.0]),
(
"first",
True,
"bottom",
True,
[0.125, 0.25, 0.75, 0.625, 0.375, 0.5, 0.875, 1.0],
),
("first", False, "bottom", False, [3.0, 4.0, 6.0, 1.0, 5.0, 2.0, 7.0, 8.0]),
(
"first",
False,
"bottom",
True,
[0.375, 0.5, 0.75, 0.125, 0.625, 0.25, 0.875, 1.0],
),
("dense", True, "bottom", False, [1.0, 1.0, 4.0, 3.0, 1.0, 2.0, 4.0, 4.0]),
("dense", True, "bottom", True, [0.25, 0.25, 1.0, 0.75, 0.25, 0.5, 1.0, 1.0]),
("dense", False, "bottom", False, [3.0, 3.0, 4.0, 1.0, 3.0, 2.0, 4.0, 4.0]),
("dense", False, "bottom", True, [0.75, 0.75, 1.0, 0.25, 0.75, 0.5, 1.0, 1.0]),
],
)
def test_rank_args_missing(grps, vals, ties_method, ascending, na_option, pct, exp):
key = np.repeat(grps, len(vals))
vals = vals * len(grps)
df = DataFrame({"key": key, "val": vals})
result = df.groupby("key").rank(
method=ties_method, ascending=ascending, na_option=na_option, pct=pct
)
exp_df = DataFrame(exp * len(grps), columns=["val"])
tm.assert_frame_equal(result, exp_df)
@pytest.mark.parametrize(
"pct,exp", [(False, [3.0, 3.0, 3.0, 3.0, 3.0]), (True, [0.6, 0.6, 0.6, 0.6, 0.6])]
)
def test_rank_resets_each_group(pct, exp):
df = DataFrame(
{"key": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"], "val": [1] * 10}
)
result = df.groupby("key").rank(pct=pct)
exp_df = DataFrame(exp * 2, columns=["val"])
tm.assert_frame_equal(result, exp_df)
def test_rank_avg_even_vals():
df = DataFrame({"key": ["a"] * 4, "val": [1] * 4})
result = df.groupby("key").rank()
exp_df = DataFrame([2.5, 2.5, 2.5, 2.5], columns=["val"])
tm.assert_frame_equal(result, exp_df)
@pytest.mark.parametrize("ties_method", ["average", "min", "max", "first", "dense"])
@pytest.mark.parametrize("ascending", [True, False])
@pytest.mark.parametrize("na_option", ["keep", "top", "bottom"])
@pytest.mark.parametrize("pct", [True, False])
@pytest.mark.parametrize(
"vals", [["bar", "bar", "foo", "bar", "baz"], ["bar", np.nan, "foo", np.nan, "baz"]]
)
def test_rank_object_raises(ties_method, ascending, na_option, pct, vals):
df = DataFrame({"key": ["foo"] * 5, "val": vals})
with pytest.raises(DataError, match="No numeric types to aggregate"):
df.groupby("key").rank(
method=ties_method, ascending=ascending, na_option=na_option, pct=pct
)
@pytest.mark.parametrize("na_option", [True, "bad", 1])
@pytest.mark.parametrize("ties_method", ["average", "min", "max", "first", "dense"])
@pytest.mark.parametrize("ascending", [True, False])
@pytest.mark.parametrize("pct", [True, False])
@pytest.mark.parametrize(
"vals",
[
["bar", "bar", "foo", "bar", "baz"],
["bar", np.nan, "foo", np.nan, "baz"],
[1, np.nan, 2, np.nan, 3],
],
)
def test_rank_naoption_raises(ties_method, ascending, na_option, pct, vals):
df = DataFrame({"key": ["foo"] * 5, "val": vals})
msg = "na_option must be one of 'keep', 'top', or 'bottom'"
with pytest.raises(ValueError, match=msg):
df.groupby("key").rank(
method=ties_method, ascending=ascending, na_option=na_option, pct=pct
)
def test_rank_empty_group():
# see gh-22519
column = "A"
df = DataFrame({"A": [0, 1, 0], "B": [1.0, np.nan, 2.0]})
result = df.groupby(column).B.rank(pct=True)
expected = Series([0.5, np.nan, 1.0], name="B")
tm.assert_series_equal(result, expected)
result = df.groupby(column).rank(pct=True)
expected = DataFrame({"B": [0.5, np.nan, 1.0]})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"input_key,input_value,output_value",
[
([1, 2], [1, 1], [1.0, 1.0]),
([1, 1, 2, 2], [1, 2, 1, 2], [0.5, 1.0, 0.5, 1.0]),
([1, 1, 2, 2], [1, 2, 1, np.nan], [0.5, 1.0, 1.0, np.nan]),
([1, 1, 2], [1, 2, np.nan], [0.5, 1.0, np.nan]),
],
)
def test_rank_zero_div(input_key, input_value, output_value):
# GH 23666
df = DataFrame({"A": input_key, "B": input_value})
result = df.groupby("A").rank(method="dense", pct=True)
expected = DataFrame({"B": output_value})
tm.assert_frame_equal(result, expected)
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