mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-10 09:17:48 +01:00
242 lines
8 KiB
Python
242 lines
8 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Index
|
|
import pandas._testing as tm
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"interpolation", ["linear", "lower", "higher", "nearest", "midpoint"]
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"a_vals,b_vals",
|
|
[
|
|
# Ints
|
|
([1, 2, 3, 4, 5], [5, 4, 3, 2, 1]),
|
|
([1, 2, 3, 4], [4, 3, 2, 1]),
|
|
([1, 2, 3, 4, 5], [4, 3, 2, 1]),
|
|
# Floats
|
|
([1.0, 2.0, 3.0, 4.0, 5.0], [5.0, 4.0, 3.0, 2.0, 1.0]),
|
|
# Missing data
|
|
([1.0, np.nan, 3.0, np.nan, 5.0], [5.0, np.nan, 3.0, np.nan, 1.0]),
|
|
([np.nan, 4.0, np.nan, 2.0, np.nan], [np.nan, 4.0, np.nan, 2.0, np.nan]),
|
|
# Timestamps
|
|
(
|
|
list(pd.date_range("1/1/18", freq="D", periods=5)),
|
|
list(pd.date_range("1/1/18", freq="D", periods=5))[::-1],
|
|
),
|
|
# All NA
|
|
([np.nan] * 5, [np.nan] * 5),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("q", [0, 0.25, 0.5, 0.75, 1])
|
|
def test_quantile(interpolation, a_vals, b_vals, q):
|
|
if interpolation == "nearest" and q == 0.5 and b_vals == [4, 3, 2, 1]:
|
|
pytest.skip(
|
|
"Unclear numpy expectation for nearest result with equidistant data"
|
|
)
|
|
|
|
a_expected = pd.Series(a_vals).quantile(q, interpolation=interpolation)
|
|
b_expected = pd.Series(b_vals).quantile(q, interpolation=interpolation)
|
|
|
|
df = DataFrame(
|
|
{"key": ["a"] * len(a_vals) + ["b"] * len(b_vals), "val": a_vals + b_vals}
|
|
)
|
|
|
|
expected = DataFrame(
|
|
[a_expected, b_expected], columns=["val"], index=Index(["a", "b"], name="key")
|
|
)
|
|
result = df.groupby("key").quantile(q, interpolation=interpolation)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_quantile_array():
|
|
# https://github.com/pandas-dev/pandas/issues/27526
|
|
df = pd.DataFrame({"A": [0, 1, 2, 3, 4]})
|
|
result = df.groupby([0, 0, 1, 1, 1]).quantile([0.25])
|
|
|
|
index = pd.MultiIndex.from_product([[0, 1], [0.25]])
|
|
expected = pd.DataFrame({"A": [0.25, 2.50]}, index=index)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
df = pd.DataFrame({"A": [0, 1, 2, 3], "B": [4, 5, 6, 7]})
|
|
index = pd.MultiIndex.from_product([[0, 1], [0.25, 0.75]])
|
|
|
|
result = df.groupby([0, 0, 1, 1]).quantile([0.25, 0.75])
|
|
expected = pd.DataFrame(
|
|
{"A": [0.25, 0.75, 2.25, 2.75], "B": [4.25, 4.75, 6.25, 6.75]}, index=index
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_quantile_array2():
|
|
# https://github.com/pandas-dev/pandas/pull/28085#issuecomment-524066959
|
|
df = pd.DataFrame(
|
|
np.random.RandomState(0).randint(0, 5, size=(10, 3)), columns=list("ABC")
|
|
)
|
|
result = df.groupby("A").quantile([0.3, 0.7])
|
|
expected = pd.DataFrame(
|
|
{
|
|
"B": [0.9, 2.1, 2.2, 3.4, 1.6, 2.4, 2.3, 2.7, 0.0, 0.0],
|
|
"C": [1.2, 2.8, 1.8, 3.0, 0.0, 0.0, 1.9, 3.1, 3.0, 3.0],
|
|
},
|
|
index=pd.MultiIndex.from_product(
|
|
[[0, 1, 2, 3, 4], [0.3, 0.7]], names=["A", None]
|
|
),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_quantile_array_no_sort():
|
|
df = pd.DataFrame({"A": [0, 1, 2], "B": [3, 4, 5]})
|
|
result = df.groupby([1, 0, 1], sort=False).quantile([0.25, 0.5, 0.75])
|
|
expected = pd.DataFrame(
|
|
{"A": [0.5, 1.0, 1.5, 1.0, 1.0, 1.0], "B": [3.5, 4.0, 4.5, 4.0, 4.0, 4.0]},
|
|
index=pd.MultiIndex.from_product([[1, 0], [0.25, 0.5, 0.75]]),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.groupby([1, 0, 1], sort=False).quantile([0.75, 0.25])
|
|
expected = pd.DataFrame(
|
|
{"A": [1.5, 0.5, 1.0, 1.0], "B": [4.5, 3.5, 4.0, 4.0]},
|
|
index=pd.MultiIndex.from_product([[1, 0], [0.75, 0.25]]),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_quantile_array_multiple_levels():
|
|
df = pd.DataFrame(
|
|
{"A": [0, 1, 2], "B": [3, 4, 5], "c": ["a", "a", "a"], "d": ["a", "a", "b"]}
|
|
)
|
|
result = df.groupby(["c", "d"]).quantile([0.25, 0.75])
|
|
index = pd.MultiIndex.from_tuples(
|
|
[("a", "a", 0.25), ("a", "a", 0.75), ("a", "b", 0.25), ("a", "b", 0.75)],
|
|
names=["c", "d", None],
|
|
)
|
|
expected = pd.DataFrame(
|
|
{"A": [0.25, 0.75, 2.0, 2.0], "B": [3.25, 3.75, 5.0, 5.0]}, index=index
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("frame_size", [(2, 3), (100, 10)])
|
|
@pytest.mark.parametrize("groupby", [[0], [0, 1]])
|
|
@pytest.mark.parametrize("q", [[0.5, 0.6]])
|
|
def test_groupby_quantile_with_arraylike_q_and_int_columns(frame_size, groupby, q):
|
|
# GH30289
|
|
nrow, ncol = frame_size
|
|
df = pd.DataFrame(
|
|
np.array([ncol * [_ % 4] for _ in range(nrow)]), columns=range(ncol)
|
|
)
|
|
|
|
idx_levels = [list(range(min(nrow, 4)))] * len(groupby) + [q]
|
|
idx_codes = [[x for x in range(min(nrow, 4)) for _ in q]] * len(groupby) + [
|
|
list(range(len(q))) * min(nrow, 4)
|
|
]
|
|
expected_index = pd.MultiIndex(
|
|
levels=idx_levels, codes=idx_codes, names=groupby + [None]
|
|
)
|
|
expected_values = [
|
|
[float(x)] * (ncol - len(groupby)) for x in range(min(nrow, 4)) for _ in q
|
|
]
|
|
expected_columns = [x for x in range(ncol) if x not in groupby]
|
|
expected = pd.DataFrame(
|
|
expected_values, index=expected_index, columns=expected_columns
|
|
)
|
|
result = df.groupby(groupby).quantile(q)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_quantile_raises():
|
|
df = pd.DataFrame(
|
|
[["foo", "a"], ["foo", "b"], ["foo", "c"]], columns=["key", "val"]
|
|
)
|
|
|
|
with pytest.raises(TypeError, match="cannot be performed against 'object' dtypes"):
|
|
df.groupby("key").quantile()
|
|
|
|
|
|
def test_quantile_out_of_bounds_q_raises():
|
|
# https://github.com/pandas-dev/pandas/issues/27470
|
|
df = pd.DataFrame(dict(a=[0, 0, 0, 1, 1, 1], b=range(6)))
|
|
g = df.groupby([0, 0, 0, 1, 1, 1])
|
|
with pytest.raises(ValueError, match="Got '50.0' instead"):
|
|
g.quantile(50)
|
|
|
|
with pytest.raises(ValueError, match="Got '-1.0' instead"):
|
|
g.quantile(-1)
|
|
|
|
|
|
def test_quantile_missing_group_values_no_segfaults():
|
|
# GH 28662
|
|
data = np.array([1.0, np.nan, 1.0])
|
|
df = pd.DataFrame(dict(key=data, val=range(3)))
|
|
|
|
# Random segfaults; would have been guaranteed in loop
|
|
grp = df.groupby("key")
|
|
for _ in range(100):
|
|
grp.quantile()
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"key, val, expected_key, expected_val",
|
|
[
|
|
([1.0, np.nan, 3.0, np.nan], range(4), [1.0, 3.0], [0.0, 2.0]),
|
|
([1.0, np.nan, 2.0, 2.0], range(4), [1.0, 2.0], [0.0, 2.5]),
|
|
(["a", "b", "b", np.nan], range(4), ["a", "b"], [0, 1.5]),
|
|
([0], [42], [0], [42.0]),
|
|
([], [], np.array([], dtype="float64"), np.array([], dtype="float64")),
|
|
],
|
|
)
|
|
def test_quantile_missing_group_values_correct_results(
|
|
key, val, expected_key, expected_val
|
|
):
|
|
# GH 28662, GH 33200, GH 33569
|
|
df = pd.DataFrame({"key": key, "val": val})
|
|
|
|
expected = pd.DataFrame(
|
|
expected_val, index=pd.Index(expected_key, name="key"), columns=["val"]
|
|
)
|
|
|
|
grp = df.groupby("key")
|
|
|
|
result = grp.quantile(0.5)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = grp.quantile()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"values",
|
|
[
|
|
pd.array([1, 0, None] * 2, dtype="Int64"),
|
|
pd.array([True, False, None] * 2, dtype="boolean"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("q", [0.5, [0.0, 0.5, 1.0]])
|
|
def test_groupby_quantile_nullable_array(values, q):
|
|
# https://github.com/pandas-dev/pandas/issues/33136
|
|
df = pd.DataFrame({"a": ["x"] * 3 + ["y"] * 3, "b": values})
|
|
result = df.groupby("a")["b"].quantile(q)
|
|
|
|
if isinstance(q, list):
|
|
idx = pd.MultiIndex.from_product((["x", "y"], q), names=["a", None])
|
|
true_quantiles = [0.0, 0.5, 1.0]
|
|
else:
|
|
idx = pd.Index(["x", "y"], name="a")
|
|
true_quantiles = [0.5]
|
|
|
|
expected = pd.Series(true_quantiles * 2, index=idx, name="b")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("q", [0.5, [0.0, 0.5, 1.0]])
|
|
def test_groupby_quantile_skips_invalid_dtype(q):
|
|
df = pd.DataFrame({"a": [1], "b": [2.0], "c": ["x"]})
|
|
result = df.groupby("a").quantile(q)
|
|
expected = df.groupby("a")[["b"]].quantile(q)
|
|
tm.assert_frame_equal(result, expected)
|