mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-26 07:24:56 +01:00
342 lines
10 KiB
Python
342 lines
10 KiB
Python
from collections import OrderedDict
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import pandas.util._test_decorators as td
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Index, Series, Timestamp, concat
|
|
import pandas._testing as tm
|
|
from pandas.core.base import SpecificationError
|
|
|
|
|
|
def test_getitem(frame):
|
|
r = frame.rolling(window=5)
|
|
tm.assert_index_equal(r._selected_obj.columns, frame.columns)
|
|
|
|
r = frame.rolling(window=5)[1]
|
|
assert r._selected_obj.name == frame.columns[1]
|
|
|
|
# technically this is allowed
|
|
r = frame.rolling(window=5)[1, 3]
|
|
tm.assert_index_equal(r._selected_obj.columns, frame.columns[[1, 3]])
|
|
|
|
r = frame.rolling(window=5)[[1, 3]]
|
|
tm.assert_index_equal(r._selected_obj.columns, frame.columns[[1, 3]])
|
|
|
|
|
|
def test_select_bad_cols():
|
|
df = DataFrame([[1, 2]], columns=["A", "B"])
|
|
g = df.rolling(window=5)
|
|
with pytest.raises(KeyError, match="Columns not found: 'C'"):
|
|
g[["C"]]
|
|
with pytest.raises(KeyError, match="^[^A]+$"):
|
|
# A should not be referenced as a bad column...
|
|
# will have to rethink regex if you change message!
|
|
g[["A", "C"]]
|
|
|
|
|
|
def test_attribute_access():
|
|
|
|
df = DataFrame([[1, 2]], columns=["A", "B"])
|
|
r = df.rolling(window=5)
|
|
tm.assert_series_equal(r.A.sum(), r["A"].sum())
|
|
msg = "'Rolling' object has no attribute 'F'"
|
|
with pytest.raises(AttributeError, match=msg):
|
|
r.F
|
|
|
|
|
|
def tests_skip_nuisance():
|
|
|
|
df = DataFrame({"A": range(5), "B": range(5, 10), "C": "foo"})
|
|
r = df.rolling(window=3)
|
|
result = r[["A", "B"]].sum()
|
|
expected = DataFrame(
|
|
{"A": [np.nan, np.nan, 3, 6, 9], "B": [np.nan, np.nan, 18, 21, 24]},
|
|
columns=list("AB"),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_skip_sum_object_raises():
|
|
df = DataFrame({"A": range(5), "B": range(5, 10), "C": "foo"})
|
|
r = df.rolling(window=3)
|
|
result = r.sum()
|
|
expected = DataFrame(
|
|
{"A": [np.nan, np.nan, 3, 6, 9], "B": [np.nan, np.nan, 18, 21, 24]},
|
|
columns=list("AB"),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_agg():
|
|
df = DataFrame({"A": range(5), "B": range(0, 10, 2)})
|
|
|
|
r = df.rolling(window=3)
|
|
a_mean = r["A"].mean()
|
|
a_std = r["A"].std()
|
|
a_sum = r["A"].sum()
|
|
b_mean = r["B"].mean()
|
|
b_std = r["B"].std()
|
|
|
|
result = r.aggregate([np.mean, np.std])
|
|
expected = concat([a_mean, a_std, b_mean, b_std], axis=1)
|
|
expected.columns = pd.MultiIndex.from_product([["A", "B"], ["mean", "std"]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = r.aggregate({"A": np.mean, "B": np.std})
|
|
|
|
expected = concat([a_mean, b_std], axis=1)
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
|
|
|
result = r.aggregate({"A": ["mean", "std"]})
|
|
expected = concat([a_mean, a_std], axis=1)
|
|
expected.columns = pd.MultiIndex.from_tuples([("A", "mean"), ("A", "std")])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = r["A"].aggregate(["mean", "sum"])
|
|
expected = concat([a_mean, a_sum], axis=1)
|
|
expected.columns = ["mean", "sum"]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
msg = "nested renamer is not supported"
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
# using a dict with renaming
|
|
r.aggregate({"A": {"mean": "mean", "sum": "sum"}})
|
|
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
r.aggregate(
|
|
{"A": {"mean": "mean", "sum": "sum"}, "B": {"mean2": "mean", "sum2": "sum"}}
|
|
)
|
|
|
|
result = r.aggregate({"A": ["mean", "std"], "B": ["mean", "std"]})
|
|
expected = concat([a_mean, a_std, b_mean, b_std], axis=1)
|
|
|
|
exp_cols = [("A", "mean"), ("A", "std"), ("B", "mean"), ("B", "std")]
|
|
expected.columns = pd.MultiIndex.from_tuples(exp_cols)
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
|
|
|
|
|
def test_agg_apply(raw):
|
|
|
|
# passed lambda
|
|
df = DataFrame({"A": range(5), "B": range(0, 10, 2)})
|
|
|
|
r = df.rolling(window=3)
|
|
a_sum = r["A"].sum()
|
|
|
|
result = r.agg({"A": np.sum, "B": lambda x: np.std(x, ddof=1)})
|
|
rcustom = r["B"].apply(lambda x: np.std(x, ddof=1), raw=raw)
|
|
expected = concat([a_sum, rcustom], axis=1)
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
|
|
|
|
|
def test_agg_consistency():
|
|
|
|
df = DataFrame({"A": range(5), "B": range(0, 10, 2)})
|
|
r = df.rolling(window=3)
|
|
|
|
result = r.agg([np.sum, np.mean]).columns
|
|
expected = pd.MultiIndex.from_product([list("AB"), ["sum", "mean"]])
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
result = r["A"].agg([np.sum, np.mean]).columns
|
|
expected = Index(["sum", "mean"])
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
result = r.agg({"A": [np.sum, np.mean]}).columns
|
|
expected = pd.MultiIndex.from_tuples([("A", "sum"), ("A", "mean")])
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
|
|
def test_agg_nested_dicts():
|
|
|
|
# API change for disallowing these types of nested dicts
|
|
df = DataFrame({"A": range(5), "B": range(0, 10, 2)})
|
|
r = df.rolling(window=3)
|
|
|
|
msg = "nested renamer is not supported"
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
r.aggregate({"r1": {"A": ["mean", "sum"]}, "r2": {"B": ["mean", "sum"]}})
|
|
|
|
expected = concat(
|
|
[r["A"].mean(), r["A"].std(), r["B"].mean(), r["B"].std()], axis=1
|
|
)
|
|
expected.columns = pd.MultiIndex.from_tuples(
|
|
[("ra", "mean"), ("ra", "std"), ("rb", "mean"), ("rb", "std")]
|
|
)
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
r[["A", "B"]].agg({"A": {"ra": ["mean", "std"]}, "B": {"rb": ["mean", "std"]}})
|
|
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
r.agg({"A": {"ra": ["mean", "std"]}, "B": {"rb": ["mean", "std"]}})
|
|
|
|
|
|
def test_count_nonnumeric_types():
|
|
# GH12541
|
|
cols = [
|
|
"int",
|
|
"float",
|
|
"string",
|
|
"datetime",
|
|
"timedelta",
|
|
"periods",
|
|
"fl_inf",
|
|
"fl_nan",
|
|
"str_nan",
|
|
"dt_nat",
|
|
"periods_nat",
|
|
]
|
|
dt_nat_col = [Timestamp("20170101"), Timestamp("20170203"), Timestamp(None)]
|
|
|
|
df = DataFrame(
|
|
{
|
|
"int": [1, 2, 3],
|
|
"float": [4.0, 5.0, 6.0],
|
|
"string": list("abc"),
|
|
"datetime": pd.date_range("20170101", periods=3),
|
|
"timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
|
|
"periods": [
|
|
pd.Period("2012-01"),
|
|
pd.Period("2012-02"),
|
|
pd.Period("2012-03"),
|
|
],
|
|
"fl_inf": [1.0, 2.0, np.Inf],
|
|
"fl_nan": [1.0, 2.0, np.NaN],
|
|
"str_nan": ["aa", "bb", np.NaN],
|
|
"dt_nat": dt_nat_col,
|
|
"periods_nat": [
|
|
pd.Period("2012-01"),
|
|
pd.Period("2012-02"),
|
|
pd.Period(None),
|
|
],
|
|
},
|
|
columns=cols,
|
|
)
|
|
|
|
expected = DataFrame(
|
|
{
|
|
"int": [1.0, 2.0, 2.0],
|
|
"float": [1.0, 2.0, 2.0],
|
|
"string": [1.0, 2.0, 2.0],
|
|
"datetime": [1.0, 2.0, 2.0],
|
|
"timedelta": [1.0, 2.0, 2.0],
|
|
"periods": [1.0, 2.0, 2.0],
|
|
"fl_inf": [1.0, 2.0, 2.0],
|
|
"fl_nan": [1.0, 2.0, 1.0],
|
|
"str_nan": [1.0, 2.0, 1.0],
|
|
"dt_nat": [1.0, 2.0, 1.0],
|
|
"periods_nat": [1.0, 2.0, 1.0],
|
|
},
|
|
columns=cols,
|
|
)
|
|
|
|
result = df.rolling(window=2, min_periods=0).count()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.rolling(1, min_periods=0).count()
|
|
expected = df.notna().astype(float)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@td.skip_if_no_scipy
|
|
@pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning")
|
|
def test_window_with_args():
|
|
# make sure that we are aggregating window functions correctly with arg
|
|
r = Series(np.random.randn(100)).rolling(
|
|
window=10, min_periods=1, win_type="gaussian"
|
|
)
|
|
expected = concat([r.mean(std=10), r.mean(std=0.01)], axis=1)
|
|
expected.columns = ["<lambda>", "<lambda>"]
|
|
result = r.aggregate([lambda x: x.mean(std=10), lambda x: x.mean(std=0.01)])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def a(x):
|
|
return x.mean(std=10)
|
|
|
|
def b(x):
|
|
return x.mean(std=0.01)
|
|
|
|
expected = concat([r.mean(std=10), r.mean(std=0.01)], axis=1)
|
|
expected.columns = ["a", "b"]
|
|
result = r.aggregate([a, b])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_preserve_metadata():
|
|
# GH 10565
|
|
s = Series(np.arange(100), name="foo")
|
|
|
|
s2 = s.rolling(30).sum()
|
|
s3 = s.rolling(20).sum()
|
|
assert s2.name == "foo"
|
|
assert s3.name == "foo"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"func,window_size,expected_vals",
|
|
[
|
|
(
|
|
"rolling",
|
|
2,
|
|
[
|
|
[np.nan, np.nan, np.nan, np.nan],
|
|
[15.0, 20.0, 25.0, 20.0],
|
|
[25.0, 30.0, 35.0, 30.0],
|
|
[np.nan, np.nan, np.nan, np.nan],
|
|
[20.0, 30.0, 35.0, 30.0],
|
|
[35.0, 40.0, 60.0, 40.0],
|
|
[60.0, 80.0, 85.0, 80],
|
|
],
|
|
),
|
|
(
|
|
"expanding",
|
|
None,
|
|
[
|
|
[10.0, 10.0, 20.0, 20.0],
|
|
[15.0, 20.0, 25.0, 20.0],
|
|
[20.0, 30.0, 30.0, 20.0],
|
|
[10.0, 10.0, 30.0, 30.0],
|
|
[20.0, 30.0, 35.0, 30.0],
|
|
[26.666667, 40.0, 50.0, 30.0],
|
|
[40.0, 80.0, 60.0, 30.0],
|
|
],
|
|
),
|
|
],
|
|
)
|
|
def test_multiple_agg_funcs(func, window_size, expected_vals):
|
|
# GH 15072
|
|
df = pd.DataFrame(
|
|
[
|
|
["A", 10, 20],
|
|
["A", 20, 30],
|
|
["A", 30, 40],
|
|
["B", 10, 30],
|
|
["B", 30, 40],
|
|
["B", 40, 80],
|
|
["B", 80, 90],
|
|
],
|
|
columns=["stock", "low", "high"],
|
|
)
|
|
|
|
f = getattr(df.groupby("stock"), func)
|
|
if window_size:
|
|
window = f(window_size)
|
|
else:
|
|
window = f()
|
|
|
|
index = pd.MultiIndex.from_tuples(
|
|
[("A", 0), ("A", 1), ("A", 2), ("B", 3), ("B", 4), ("B", 5), ("B", 6)],
|
|
names=["stock", None],
|
|
)
|
|
columns = pd.MultiIndex.from_tuples(
|
|
[("low", "mean"), ("low", "max"), ("high", "mean"), ("high", "min")]
|
|
)
|
|
expected = pd.DataFrame(expected_vals, index=index, columns=columns)
|
|
|
|
result = window.agg(
|
|
OrderedDict((("low", ["mean", "max"]), ("high", ["mean", "min"])))
|
|
)
|
|
|
|
tm.assert_frame_equal(result, expected)
|