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test_asof.py
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test_asof.py
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# coding=utf-8
import pytest
import numpy as np
from pandas import (offsets, Series, notna,
isna, date_range, Timestamp)
import pandas.util.testing as tm
from .common import TestData
class TestSeriesAsof(TestData):
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[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[5:10] = np.NaN
ts[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.],
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.],
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.],
index=date_range('1/1/2000', '1/2/2000', freq='2h'))
tm.assert_series_equal(result, expected)
def test_periodindex(self):
from pandas import period_range, PeriodIndex
# 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[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[5:10] = np.nan
ts[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))
def test_errors(self):
s = Series([1, 2, 3],
index=[Timestamp('20130101'),
Timestamp('20130103'),
Timestamp('20130102')])
# non-monotonic
assert not s.index.is_monotonic
with pytest.raises(ValueError):
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):
s.asof(s.index[0], subset='foo')
def test_all_nans(self):
# GH 15713
# series is all nans
result = Series([np.nan]).asof([0])
expected = Series([np.nan])
tm.assert_series_equal(result, expected)
# 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)