183 lines
5.7 KiB
Python
183 lines
5.7 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
from pandas._libs.tslibs import IncompatibleFrequency
|
|
|
|
from pandas import (
|
|
DataFrame,
|
|
Period,
|
|
Series,
|
|
Timestamp,
|
|
date_range,
|
|
period_range,
|
|
to_datetime,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
|
|
@pytest.fixture
|
|
def date_range_frame():
|
|
"""
|
|
Fixture for DataFrame of ints with date_range index
|
|
|
|
Columns are ['A', 'B'].
|
|
"""
|
|
N = 50
|
|
rng = date_range("1/1/1990", periods=N, freq="53s")
|
|
return DataFrame({"A": np.arange(N), "B": np.arange(N)}, index=rng)
|
|
|
|
|
|
class TestFrameAsof:
|
|
def test_basic(self, date_range_frame):
|
|
df = date_range_frame
|
|
N = 50
|
|
df.loc[df.index[15:30], "A"] = np.nan
|
|
dates = date_range("1/1/1990", periods=N * 3, freq="25s")
|
|
|
|
result = df.asof(dates)
|
|
assert result.notna().all(1).all()
|
|
lb = df.index[14]
|
|
ub = df.index[30]
|
|
|
|
dates = list(dates)
|
|
|
|
result = df.asof(dates)
|
|
assert result.notna().all(1).all()
|
|
|
|
mask = (result.index >= lb) & (result.index < ub)
|
|
rs = result[mask]
|
|
assert (rs == 14).all(1).all()
|
|
|
|
def test_subset(self, date_range_frame):
|
|
N = 10
|
|
df = date_range_frame.iloc[:N].copy()
|
|
df.loc[df.index[4:8], "A"] = np.nan
|
|
dates = date_range("1/1/1990", periods=N * 3, freq="25s")
|
|
|
|
# with a subset of A should be the same
|
|
result = df.asof(dates, subset="A")
|
|
expected = df.asof(dates)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# same with A/B
|
|
result = df.asof(dates, subset=["A", "B"])
|
|
expected = df.asof(dates)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# B gives df.asof
|
|
result = df.asof(dates, subset="B")
|
|
expected = df.resample("25s", closed="right").ffill().reindex(dates)
|
|
expected.iloc[20:] = 9
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_missing(self, date_range_frame):
|
|
# GH 15118
|
|
# no match found - `where` value before earliest date in index
|
|
N = 10
|
|
df = date_range_frame.iloc[:N].copy()
|
|
|
|
result = df.asof("1989-12-31")
|
|
|
|
expected = Series(
|
|
index=["A", "B"], name=Timestamp("1989-12-31"), dtype=np.float64
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.asof(to_datetime(["1989-12-31"]))
|
|
expected = DataFrame(
|
|
index=to_datetime(["1989-12-31"]), columns=["A", "B"], dtype="float64"
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# Check that we handle PeriodIndex correctly, dont end up with
|
|
# period.ordinal for series name
|
|
df = df.to_period("D")
|
|
result = df.asof("1989-12-31")
|
|
assert isinstance(result.name, Period)
|
|
|
|
def test_asof_all_nans(self, frame_or_series):
|
|
# GH 15713
|
|
# DataFrame/Series is all nans
|
|
result = frame_or_series([np.nan]).asof([0])
|
|
expected = frame_or_series([np.nan])
|
|
tm.assert_equal(result, expected)
|
|
|
|
def test_all_nans(self, date_range_frame):
|
|
# GH 15713
|
|
# DataFrame is all nans
|
|
|
|
# testing non-default indexes, multiple inputs
|
|
N = 150
|
|
rng = date_range_frame.index
|
|
dates = date_range("1/1/1990", periods=N, freq="25s")
|
|
result = DataFrame(np.nan, index=rng, columns=["A"]).asof(dates)
|
|
expected = DataFrame(np.nan, index=dates, columns=["A"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# testing multiple columns
|
|
dates = date_range("1/1/1990", periods=N, freq="25s")
|
|
result = DataFrame(np.nan, index=rng, columns=["A", "B", "C"]).asof(dates)
|
|
expected = DataFrame(np.nan, index=dates, columns=["A", "B", "C"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# testing scalar input
|
|
result = DataFrame(np.nan, index=[1, 2], columns=["A", "B"]).asof([3])
|
|
expected = DataFrame(np.nan, index=[3], columns=["A", "B"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = DataFrame(np.nan, index=[1, 2], columns=["A", "B"]).asof(3)
|
|
expected = Series(np.nan, index=["A", "B"], name=3)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"stamp,expected",
|
|
[
|
|
(
|
|
Timestamp("2018-01-01 23:22:43.325+00:00"),
|
|
Series(2.0, name=Timestamp("2018-01-01 23:22:43.325+00:00")),
|
|
),
|
|
(
|
|
Timestamp("2018-01-01 22:33:20.682+01:00"),
|
|
Series(1.0, name=Timestamp("2018-01-01 22:33:20.682+01:00")),
|
|
),
|
|
],
|
|
)
|
|
def test_time_zone_aware_index(self, stamp, expected):
|
|
# GH21194
|
|
# Testing awareness of DataFrame index considering different
|
|
# UTC and timezone
|
|
df = DataFrame(
|
|
data=[1, 2],
|
|
index=[
|
|
Timestamp("2018-01-01 21:00:05.001+00:00"),
|
|
Timestamp("2018-01-01 22:35:10.550+00:00"),
|
|
],
|
|
)
|
|
|
|
result = df.asof(stamp)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_is_copy(self, date_range_frame):
|
|
# GH-27357, GH-30784: ensure the result of asof is an actual copy and
|
|
# doesn't track the parent dataframe / doesn't give SettingWithCopy warnings
|
|
df = date_range_frame
|
|
N = 50
|
|
df.loc[df.index[15:30], "A"] = np.nan
|
|
dates = date_range("1/1/1990", periods=N * 3, freq="25s")
|
|
|
|
result = df.asof(dates)
|
|
|
|
with tm.assert_produces_warning(None):
|
|
result["C"] = 1
|
|
|
|
def test_asof_periodindex_mismatched_freq(self):
|
|
N = 50
|
|
rng = period_range("1/1/1990", periods=N, freq="H")
|
|
df = DataFrame(np.random.randn(N), index=rng)
|
|
|
|
# Mismatched freq
|
|
msg = "Input has different freq"
|
|
with pytest.raises(IncompatibleFrequency, match=msg):
|
|
df.asof(rng.asfreq("D"))
|