211 lines
6.0 KiB
Python
211 lines
6.0 KiB
Python
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from pandas._libs.tslibs import IncompatibleFrequency
|
||
|
|
|
||
|
|
from pandas import (
|
||
|
|
DatetimeIndex,
|
||
|
|
Series,
|
||
|
|
Timestamp,
|
||
|
|
date_range,
|
||
|
|
isna,
|
||
|
|
notna,
|
||
|
|
offsets,
|
||
|
|
)
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
class TestSeriesAsof:
|
||
|
|
def test_asof_nanosecond_index_access(self):
|
||
|
|
ts = Timestamp("20130101").value
|
||
|
|
dti = DatetimeIndex([ts + 50 + i for i in range(100)])
|
||
|
|
ser = Series(np.random.randn(100), index=dti)
|
||
|
|
|
||
|
|
first_value = ser.asof(ser.index[0])
|
||
|
|
|
||
|
|
# GH#46903 previously incorrectly was "day"
|
||
|
|
assert dti.resolution == "nanosecond"
|
||
|
|
|
||
|
|
# this used to not work bc parsing was done by dateutil that didn't
|
||
|
|
# handle nanoseconds
|
||
|
|
assert first_value == ser["2013-01-01 00:00:00.000000050"]
|
||
|
|
|
||
|
|
expected_ts = np.datetime64("2013-01-01 00:00:00.000000050", "ns")
|
||
|
|
assert first_value == ser[Timestamp(expected_ts)]
|
||
|
|
|
||
|
|
def test_basic(self):
|
||
|
|
|
||
|
|
# array or list or dates
|
||
|
|
N = 50
|
||
|
|
rng = date_range("1/1/1990", periods=N, freq="53s")
|
||
|
|
ts = Series(np.random.randn(N), index=rng)
|
||
|
|
ts.iloc[15:30] = np.nan
|
||
|
|
dates = date_range("1/1/1990", periods=N * 3, freq="25s")
|
||
|
|
|
||
|
|
result = ts.asof(dates)
|
||
|
|
assert notna(result).all()
|
||
|
|
lb = ts.index[14]
|
||
|
|
ub = ts.index[30]
|
||
|
|
|
||
|
|
result = ts.asof(list(dates))
|
||
|
|
assert notna(result).all()
|
||
|
|
lb = ts.index[14]
|
||
|
|
ub = ts.index[30]
|
||
|
|
|
||
|
|
mask = (result.index >= lb) & (result.index < ub)
|
||
|
|
rs = result[mask]
|
||
|
|
assert (rs == ts[lb]).all()
|
||
|
|
|
||
|
|
val = result[result.index[result.index >= ub][0]]
|
||
|
|
assert ts[ub] == val
|
||
|
|
|
||
|
|
def test_scalar(self):
|
||
|
|
|
||
|
|
N = 30
|
||
|
|
rng = date_range("1/1/1990", periods=N, freq="53s")
|
||
|
|
ts = Series(np.arange(N), index=rng)
|
||
|
|
ts.iloc[5:10] = np.NaN
|
||
|
|
ts.iloc[15:20] = np.NaN
|
||
|
|
|
||
|
|
val1 = ts.asof(ts.index[7])
|
||
|
|
val2 = ts.asof(ts.index[19])
|
||
|
|
|
||
|
|
assert val1 == ts[4]
|
||
|
|
assert val2 == ts[14]
|
||
|
|
|
||
|
|
# accepts strings
|
||
|
|
val1 = ts.asof(str(ts.index[7]))
|
||
|
|
assert val1 == ts[4]
|
||
|
|
|
||
|
|
# in there
|
||
|
|
result = ts.asof(ts.index[3])
|
||
|
|
assert result == ts[3]
|
||
|
|
|
||
|
|
# no as of value
|
||
|
|
d = ts.index[0] - offsets.BDay()
|
||
|
|
assert np.isnan(ts.asof(d))
|
||
|
|
|
||
|
|
def test_with_nan(self):
|
||
|
|
# basic asof test
|
||
|
|
rng = date_range("1/1/2000", "1/2/2000", freq="4h")
|
||
|
|
s = Series(np.arange(len(rng)), index=rng)
|
||
|
|
r = s.resample("2h").mean()
|
||
|
|
|
||
|
|
result = r.asof(r.index)
|
||
|
|
expected = Series(
|
||
|
|
[0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6.0],
|
||
|
|
index=date_range("1/1/2000", "1/2/2000", freq="2h"),
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
r.iloc[3:5] = np.nan
|
||
|
|
result = r.asof(r.index)
|
||
|
|
expected = Series(
|
||
|
|
[0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 5, 5, 6.0],
|
||
|
|
index=date_range("1/1/2000", "1/2/2000", freq="2h"),
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
r.iloc[-3:] = np.nan
|
||
|
|
result = r.asof(r.index)
|
||
|
|
expected = Series(
|
||
|
|
[0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 4, 4, 4.0],
|
||
|
|
index=date_range("1/1/2000", "1/2/2000", freq="2h"),
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_periodindex(self):
|
||
|
|
from pandas import (
|
||
|
|
PeriodIndex,
|
||
|
|
period_range,
|
||
|
|
)
|
||
|
|
|
||
|
|
# array or list or dates
|
||
|
|
N = 50
|
||
|
|
rng = period_range("1/1/1990", periods=N, freq="H")
|
||
|
|
ts = Series(np.random.randn(N), index=rng)
|
||
|
|
ts.iloc[15:30] = np.nan
|
||
|
|
dates = date_range("1/1/1990", periods=N * 3, freq="37min")
|
||
|
|
|
||
|
|
result = ts.asof(dates)
|
||
|
|
assert notna(result).all()
|
||
|
|
lb = ts.index[14]
|
||
|
|
ub = ts.index[30]
|
||
|
|
|
||
|
|
result = ts.asof(list(dates))
|
||
|
|
assert notna(result).all()
|
||
|
|
lb = ts.index[14]
|
||
|
|
ub = ts.index[30]
|
||
|
|
|
||
|
|
pix = PeriodIndex(result.index.values, freq="H")
|
||
|
|
mask = (pix >= lb) & (pix < ub)
|
||
|
|
rs = result[mask]
|
||
|
|
assert (rs == ts[lb]).all()
|
||
|
|
|
||
|
|
ts.iloc[5:10] = np.nan
|
||
|
|
ts.iloc[15:20] = np.nan
|
||
|
|
|
||
|
|
val1 = ts.asof(ts.index[7])
|
||
|
|
val2 = ts.asof(ts.index[19])
|
||
|
|
|
||
|
|
assert val1 == ts[4]
|
||
|
|
assert val2 == ts[14]
|
||
|
|
|
||
|
|
# accepts strings
|
||
|
|
val1 = ts.asof(str(ts.index[7]))
|
||
|
|
assert val1 == ts[4]
|
||
|
|
|
||
|
|
# in there
|
||
|
|
assert ts.asof(ts.index[3]) == ts[3]
|
||
|
|
|
||
|
|
# no as of value
|
||
|
|
d = ts.index[0].to_timestamp() - offsets.BDay()
|
||
|
|
assert isna(ts.asof(d))
|
||
|
|
|
||
|
|
# Mismatched freq
|
||
|
|
msg = "Input has different freq"
|
||
|
|
with pytest.raises(IncompatibleFrequency, match=msg):
|
||
|
|
ts.asof(rng.asfreq("D"))
|
||
|
|
|
||
|
|
def test_errors(self):
|
||
|
|
|
||
|
|
s = Series(
|
||
|
|
[1, 2, 3],
|
||
|
|
index=[Timestamp("20130101"), Timestamp("20130103"), Timestamp("20130102")],
|
||
|
|
)
|
||
|
|
|
||
|
|
# non-monotonic
|
||
|
|
assert not s.index.is_monotonic_increasing
|
||
|
|
with pytest.raises(ValueError, match="requires a sorted index"):
|
||
|
|
s.asof(s.index[0])
|
||
|
|
|
||
|
|
# subset with Series
|
||
|
|
N = 10
|
||
|
|
rng = date_range("1/1/1990", periods=N, freq="53s")
|
||
|
|
s = Series(np.random.randn(N), index=rng)
|
||
|
|
with pytest.raises(ValueError, match="not valid for Series"):
|
||
|
|
s.asof(s.index[0], subset="foo")
|
||
|
|
|
||
|
|
def test_all_nans(self):
|
||
|
|
# GH 15713
|
||
|
|
# series is all nans
|
||
|
|
|
||
|
|
# testing non-default indexes
|
||
|
|
N = 50
|
||
|
|
rng = date_range("1/1/1990", periods=N, freq="53s")
|
||
|
|
|
||
|
|
dates = date_range("1/1/1990", periods=N * 3, freq="25s")
|
||
|
|
result = Series(np.nan, index=rng).asof(dates)
|
||
|
|
expected = Series(np.nan, index=dates)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# testing scalar input
|
||
|
|
date = date_range("1/1/1990", periods=N * 3, freq="25s")[0]
|
||
|
|
result = Series(np.nan, index=rng).asof(date)
|
||
|
|
assert isna(result)
|
||
|
|
|
||
|
|
# test name is propagated
|
||
|
|
result = Series(np.nan, index=[1, 2, 3, 4], name="test").asof([4, 5])
|
||
|
|
expected = Series(np.nan, index=[4, 5], name="test")
|
||
|
|
tm.assert_series_equal(result, expected)
|