mirror of
https://github.com/PiBrewing/craftbeerpi4.git
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842 lines
27 KiB
Python
842 lines
27 KiB
Python
from datetime import datetime, timedelta
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import numpy as np
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import pytest
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import pytz
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from pandas._libs import iNaT
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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Index,
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IntervalIndex,
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NaT,
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Series,
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Timedelta,
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Timestamp,
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date_range,
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isna,
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)
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import pandas._testing as tm
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class TestSeriesMissingData:
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def test_timedelta_fillna(self):
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# GH 3371
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s = Series(
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[
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Timestamp("20130101"),
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Timestamp("20130101"),
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Timestamp("20130102"),
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Timestamp("20130103 9:01:01"),
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]
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)
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td = s.diff()
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# reg fillna
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result = td.fillna(Timedelta(seconds=0))
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expected = Series(
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[
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timedelta(0),
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timedelta(0),
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timedelta(1),
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timedelta(days=1, seconds=9 * 3600 + 60 + 1),
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]
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)
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tm.assert_series_equal(result, expected)
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# interpreted as seconds, deprecated
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with pytest.raises(TypeError, match="Passing integers to fillna"):
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td.fillna(1)
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result = td.fillna(Timedelta(seconds=1))
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expected = Series(
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[
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timedelta(seconds=1),
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timedelta(0),
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timedelta(1),
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timedelta(days=1, seconds=9 * 3600 + 60 + 1),
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]
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)
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tm.assert_series_equal(result, expected)
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result = td.fillna(timedelta(days=1, seconds=1))
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expected = Series(
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[
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timedelta(days=1, seconds=1),
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timedelta(0),
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timedelta(1),
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timedelta(days=1, seconds=9 * 3600 + 60 + 1),
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]
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)
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tm.assert_series_equal(result, expected)
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result = td.fillna(np.timedelta64(int(1e9)))
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expected = Series(
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[
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timedelta(seconds=1),
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timedelta(0),
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timedelta(1),
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timedelta(days=1, seconds=9 * 3600 + 60 + 1),
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]
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)
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tm.assert_series_equal(result, expected)
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result = td.fillna(NaT)
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expected = Series(
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[
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NaT,
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timedelta(0),
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timedelta(1),
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timedelta(days=1, seconds=9 * 3600 + 60 + 1),
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],
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dtype="m8[ns]",
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)
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tm.assert_series_equal(result, expected)
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# ffill
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td[2] = np.nan
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result = td.ffill()
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expected = td.fillna(Timedelta(seconds=0))
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expected[0] = np.nan
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tm.assert_series_equal(result, expected)
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# bfill
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td[2] = np.nan
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result = td.bfill()
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expected = td.fillna(Timedelta(seconds=0))
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expected[2] = timedelta(days=1, seconds=9 * 3600 + 60 + 1)
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tm.assert_series_equal(result, expected)
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def test_datetime64_fillna(self):
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s = Series(
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[
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Timestamp("20130101"),
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Timestamp("20130101"),
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Timestamp("20130102"),
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Timestamp("20130103 9:01:01"),
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]
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)
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s[2] = np.nan
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# ffill
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result = s.ffill()
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expected = Series(
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[
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Timestamp("20130101"),
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Timestamp("20130101"),
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Timestamp("20130101"),
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Timestamp("20130103 9:01:01"),
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]
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)
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tm.assert_series_equal(result, expected)
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# bfill
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result = s.bfill()
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expected = Series(
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[
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Timestamp("20130101"),
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Timestamp("20130101"),
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Timestamp("20130103 9:01:01"),
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Timestamp("20130103 9:01:01"),
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]
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)
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tm.assert_series_equal(result, expected)
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# GH 6587
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# make sure that we are treating as integer when filling
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# this also tests inference of a datetime-like with NaT's
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s = Series([pd.NaT, pd.NaT, "2013-08-05 15:30:00.000001"])
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expected = Series(
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[
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"2013-08-05 15:30:00.000001",
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"2013-08-05 15:30:00.000001",
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"2013-08-05 15:30:00.000001",
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],
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dtype="M8[ns]",
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)
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result = s.fillna(method="backfill")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("tz", ["US/Eastern", "Asia/Tokyo"])
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def test_datetime64_tz_fillna(self, tz):
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# DatetimeBlock
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s = Series(
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[
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Timestamp("2011-01-01 10:00"),
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pd.NaT,
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Timestamp("2011-01-03 10:00"),
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pd.NaT,
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]
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)
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null_loc = pd.Series([False, True, False, True])
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result = s.fillna(pd.Timestamp("2011-01-02 10:00"))
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expected = Series(
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[
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Timestamp("2011-01-01 10:00"),
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Timestamp("2011-01-02 10:00"),
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Timestamp("2011-01-03 10:00"),
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Timestamp("2011-01-02 10:00"),
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]
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)
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tm.assert_series_equal(expected, result)
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# check s is not changed
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(pd.Timestamp("2011-01-02 10:00", tz=tz))
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expected = Series(
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[
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Timestamp("2011-01-01 10:00"),
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Timestamp("2011-01-02 10:00", tz=tz),
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Timestamp("2011-01-03 10:00"),
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Timestamp("2011-01-02 10:00", tz=tz),
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]
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna("AAA")
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expected = Series(
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[
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Timestamp("2011-01-01 10:00"),
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"AAA",
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Timestamp("2011-01-03 10:00"),
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"AAA",
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],
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dtype=object,
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(
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{
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1: pd.Timestamp("2011-01-02 10:00", tz=tz),
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3: pd.Timestamp("2011-01-04 10:00"),
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}
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)
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expected = Series(
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[
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Timestamp("2011-01-01 10:00"),
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Timestamp("2011-01-02 10:00", tz=tz),
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Timestamp("2011-01-03 10:00"),
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Timestamp("2011-01-04 10:00"),
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]
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(
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{1: pd.Timestamp("2011-01-02 10:00"), 3: pd.Timestamp("2011-01-04 10:00")}
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)
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expected = Series(
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[
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Timestamp("2011-01-01 10:00"),
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Timestamp("2011-01-02 10:00"),
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Timestamp("2011-01-03 10:00"),
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Timestamp("2011-01-04 10:00"),
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]
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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# DatetimeBlockTZ
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idx = pd.DatetimeIndex(
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["2011-01-01 10:00", pd.NaT, "2011-01-03 10:00", pd.NaT], tz=tz
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)
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s = pd.Series(idx)
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assert s.dtype == f"datetime64[ns, {tz}]"
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(pd.Timestamp("2011-01-02 10:00"))
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expected = Series(
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[
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Timestamp("2011-01-01 10:00", tz=tz),
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Timestamp("2011-01-02 10:00"),
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Timestamp("2011-01-03 10:00", tz=tz),
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Timestamp("2011-01-02 10:00"),
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]
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(pd.Timestamp("2011-01-02 10:00", tz=tz))
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idx = pd.DatetimeIndex(
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[
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"2011-01-01 10:00",
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"2011-01-02 10:00",
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"2011-01-03 10:00",
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"2011-01-02 10:00",
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],
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tz=tz,
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)
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expected = Series(idx)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(pd.Timestamp("2011-01-02 10:00", tz=tz).to_pydatetime())
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idx = pd.DatetimeIndex(
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[
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"2011-01-01 10:00",
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"2011-01-02 10:00",
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"2011-01-03 10:00",
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"2011-01-02 10:00",
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],
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tz=tz,
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)
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expected = Series(idx)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna("AAA")
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expected = Series(
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[
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Timestamp("2011-01-01 10:00", tz=tz),
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"AAA",
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Timestamp("2011-01-03 10:00", tz=tz),
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"AAA",
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],
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dtype=object,
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(
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{
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1: pd.Timestamp("2011-01-02 10:00", tz=tz),
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3: pd.Timestamp("2011-01-04 10:00"),
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}
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)
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expected = Series(
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[
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Timestamp("2011-01-01 10:00", tz=tz),
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Timestamp("2011-01-02 10:00", tz=tz),
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Timestamp("2011-01-03 10:00", tz=tz),
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Timestamp("2011-01-04 10:00"),
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]
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(
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{
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1: pd.Timestamp("2011-01-02 10:00", tz=tz),
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3: pd.Timestamp("2011-01-04 10:00", tz=tz),
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}
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)
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expected = Series(
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[
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Timestamp("2011-01-01 10:00", tz=tz),
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Timestamp("2011-01-02 10:00", tz=tz),
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Timestamp("2011-01-03 10:00", tz=tz),
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Timestamp("2011-01-04 10:00", tz=tz),
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]
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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# filling with a naive/other zone, coerce to object
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result = s.fillna(Timestamp("20130101"))
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expected = Series(
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[
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Timestamp("2011-01-01 10:00", tz=tz),
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Timestamp("2013-01-01"),
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Timestamp("2011-01-03 10:00", tz=tz),
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Timestamp("2013-01-01"),
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]
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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result = s.fillna(Timestamp("20130101", tz="US/Pacific"))
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expected = Series(
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[
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Timestamp("2011-01-01 10:00", tz=tz),
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Timestamp("2013-01-01", tz="US/Pacific"),
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Timestamp("2011-01-03 10:00", tz=tz),
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Timestamp("2013-01-01", tz="US/Pacific"),
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]
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)
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(pd.isna(s), null_loc)
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def test_fillna_dt64tz_with_method(self):
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# with timezone
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# GH 15855
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ser = pd.Series([pd.Timestamp("2012-11-11 00:00:00+01:00"), pd.NaT])
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exp = pd.Series(
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[
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pd.Timestamp("2012-11-11 00:00:00+01:00"),
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pd.Timestamp("2012-11-11 00:00:00+01:00"),
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]
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)
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tm.assert_series_equal(ser.fillna(method="pad"), exp)
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ser = pd.Series([pd.NaT, pd.Timestamp("2012-11-11 00:00:00+01:00")])
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exp = pd.Series(
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[
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pd.Timestamp("2012-11-11 00:00:00+01:00"),
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pd.Timestamp("2012-11-11 00:00:00+01:00"),
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]
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)
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tm.assert_series_equal(ser.fillna(method="bfill"), exp)
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def test_fillna_consistency(self):
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# GH 16402
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# fillna with a tz aware to a tz-naive, should result in object
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s = Series([Timestamp("20130101"), pd.NaT])
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result = s.fillna(Timestamp("20130101", tz="US/Eastern"))
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expected = Series(
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[Timestamp("20130101"), Timestamp("2013-01-01", tz="US/Eastern")],
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dtype="object",
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)
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tm.assert_series_equal(result, expected)
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# where (we ignore the errors=)
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result = s.where(
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[True, False], Timestamp("20130101", tz="US/Eastern"), errors="ignore"
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)
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tm.assert_series_equal(result, expected)
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result = s.where(
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[True, False], Timestamp("20130101", tz="US/Eastern"), errors="ignore"
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)
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tm.assert_series_equal(result, expected)
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# with a non-datetime
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result = s.fillna("foo")
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expected = Series([Timestamp("20130101"), "foo"])
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tm.assert_series_equal(result, expected)
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# assignment
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s2 = s.copy()
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s2[1] = "foo"
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tm.assert_series_equal(s2, expected)
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def test_datetime64tz_fillna_round_issue(self):
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# GH 14872
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data = pd.Series(
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[pd.NaT, pd.NaT, datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc)]
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)
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filled = data.fillna(method="bfill")
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expected = pd.Series(
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[
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datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
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datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
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datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
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]
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)
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tm.assert_series_equal(filled, expected)
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def test_fillna_downcast(self):
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# GH 15277
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# infer int64 from float64
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s = pd.Series([1.0, np.nan])
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result = s.fillna(0, downcast="infer")
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expected = pd.Series([1, 0])
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tm.assert_series_equal(result, expected)
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# infer int64 from float64 when fillna value is a dict
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s = pd.Series([1.0, np.nan])
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result = s.fillna({1: 0}, downcast="infer")
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expected = pd.Series([1, 0])
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tm.assert_series_equal(result, expected)
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def test_fillna_int(self):
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s = Series(np.random.randint(-100, 100, 50))
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return_value = s.fillna(method="ffill", inplace=True)
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assert return_value is None
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tm.assert_series_equal(s.fillna(method="ffill", inplace=False), s)
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def test_categorical_nan_equality(self):
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cat = Series(Categorical(["a", "b", "c", np.nan]))
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exp = Series([True, True, True, False])
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res = cat == cat
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tm.assert_series_equal(res, exp)
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def test_categorical_nan_handling(self):
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# NaNs are represented as -1 in labels
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s = Series(Categorical(["a", "b", np.nan, "a"]))
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tm.assert_index_equal(s.cat.categories, Index(["a", "b"]))
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tm.assert_numpy_array_equal(
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s.values.codes, np.array([0, 1, -1, 0], dtype=np.int8)
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)
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def test_fillna_nat(self):
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series = Series([0, 1, 2, iNaT], dtype="M8[ns]")
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filled = series.fillna(method="pad")
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filled2 = series.fillna(value=series.values[2])
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expected = series.copy()
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expected.values[3] = expected.values[2]
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tm.assert_series_equal(filled, expected)
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tm.assert_series_equal(filled2, expected)
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df = DataFrame({"A": series})
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filled = df.fillna(method="pad")
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filled2 = df.fillna(value=series.values[2])
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expected = DataFrame({"A": expected})
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tm.assert_frame_equal(filled, expected)
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tm.assert_frame_equal(filled2, expected)
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series = Series([iNaT, 0, 1, 2], dtype="M8[ns]")
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filled = series.fillna(method="bfill")
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filled2 = series.fillna(value=series[1])
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expected = series.copy()
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expected[0] = expected[1]
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tm.assert_series_equal(filled, expected)
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tm.assert_series_equal(filled2, expected)
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df = DataFrame({"A": series})
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filled = df.fillna(method="bfill")
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filled2 = df.fillna(value=series[1])
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expected = DataFrame({"A": expected})
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tm.assert_frame_equal(filled, expected)
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tm.assert_frame_equal(filled2, expected)
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def test_isna_for_inf(self):
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s = Series(["a", np.inf, np.nan, pd.NA, 1.0])
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with pd.option_context("mode.use_inf_as_na", True):
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r = s.isna()
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dr = s.dropna()
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e = Series([False, True, True, True, False])
|
|
de = Series(["a", 1.0], index=[0, 4])
|
|
tm.assert_series_equal(r, e)
|
|
tm.assert_series_equal(dr, de)
|
|
|
|
def test_isnull_for_inf_deprecated(self):
|
|
# gh-17115
|
|
s = Series(["a", np.inf, np.nan, 1.0])
|
|
with pd.option_context("mode.use_inf_as_null", True):
|
|
r = s.isna()
|
|
dr = s.dropna()
|
|
|
|
e = Series([False, True, True, False])
|
|
de = Series(["a", 1.0], index=[0, 3])
|
|
tm.assert_series_equal(r, e)
|
|
tm.assert_series_equal(dr, de)
|
|
|
|
def test_fillna(self, datetime_series):
|
|
ts = Series([0.0, 1.0, 2.0, 3.0, 4.0], index=tm.makeDateIndex(5))
|
|
|
|
tm.assert_series_equal(ts, ts.fillna(method="ffill"))
|
|
|
|
ts[2] = np.NaN
|
|
|
|
exp = Series([0.0, 1.0, 1.0, 3.0, 4.0], index=ts.index)
|
|
tm.assert_series_equal(ts.fillna(method="ffill"), exp)
|
|
|
|
exp = Series([0.0, 1.0, 3.0, 3.0, 4.0], index=ts.index)
|
|
tm.assert_series_equal(ts.fillna(method="backfill"), exp)
|
|
|
|
exp = Series([0.0, 1.0, 5.0, 3.0, 4.0], index=ts.index)
|
|
tm.assert_series_equal(ts.fillna(value=5), exp)
|
|
|
|
msg = "Must specify a fill 'value' or 'method'"
|
|
with pytest.raises(ValueError, match=msg):
|
|
ts.fillna()
|
|
|
|
msg = "Cannot specify both 'value' and 'method'"
|
|
with pytest.raises(ValueError, match=msg):
|
|
datetime_series.fillna(value=0, method="ffill")
|
|
|
|
# GH 5703
|
|
s1 = Series([np.nan])
|
|
s2 = Series([1])
|
|
result = s1.fillna(s2)
|
|
expected = Series([1.0])
|
|
tm.assert_series_equal(result, expected)
|
|
result = s1.fillna({})
|
|
tm.assert_series_equal(result, s1)
|
|
result = s1.fillna(Series((), dtype=object))
|
|
tm.assert_series_equal(result, s1)
|
|
result = s2.fillna(s1)
|
|
tm.assert_series_equal(result, s2)
|
|
result = s1.fillna({0: 1})
|
|
tm.assert_series_equal(result, expected)
|
|
result = s1.fillna({1: 1})
|
|
tm.assert_series_equal(result, Series([np.nan]))
|
|
result = s1.fillna({0: 1, 1: 1})
|
|
tm.assert_series_equal(result, expected)
|
|
result = s1.fillna(Series({0: 1, 1: 1}))
|
|
tm.assert_series_equal(result, expected)
|
|
result = s1.fillna(Series({0: 1, 1: 1}, index=[4, 5]))
|
|
tm.assert_series_equal(result, s1)
|
|
|
|
s1 = Series([0, 1, 2], list("abc"))
|
|
s2 = Series([0, np.nan, 2], list("bac"))
|
|
result = s2.fillna(s1)
|
|
expected = Series([0, 0, 2.0], list("bac"))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# limit
|
|
s = Series(np.nan, index=[0, 1, 2])
|
|
result = s.fillna(999, limit=1)
|
|
expected = Series([999, np.nan, np.nan], index=[0, 1, 2])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.fillna(999, limit=2)
|
|
expected = Series([999, 999, np.nan], index=[0, 1, 2])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# GH 9043
|
|
# make sure a string representation of int/float values can be filled
|
|
# correctly without raising errors or being converted
|
|
vals = ["0", "1.5", "-0.3"]
|
|
for val in vals:
|
|
s = Series([0, 1, np.nan, np.nan, 4], dtype="float64")
|
|
result = s.fillna(val)
|
|
expected = Series([0, 1, val, val, 4], dtype="object")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_fillna_bug(self):
|
|
x = Series([np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"])
|
|
filled = x.fillna(method="ffill")
|
|
expected = Series([np.nan, 1.0, 1.0, 3.0, 3.0], x.index)
|
|
tm.assert_series_equal(filled, expected)
|
|
|
|
filled = x.fillna(method="bfill")
|
|
expected = Series([1.0, 1.0, 3.0, 3.0, np.nan], x.index)
|
|
tm.assert_series_equal(filled, expected)
|
|
|
|
def test_fillna_invalid_method(self, datetime_series):
|
|
try:
|
|
datetime_series.fillna(method="ffil")
|
|
except ValueError as inst:
|
|
assert "ffil" in str(inst)
|
|
|
|
def test_ffill(self):
|
|
ts = Series([0.0, 1.0, 2.0, 3.0, 4.0], index=tm.makeDateIndex(5))
|
|
ts[2] = np.NaN
|
|
tm.assert_series_equal(ts.ffill(), ts.fillna(method="ffill"))
|
|
|
|
def test_ffill_mixed_dtypes_without_missing_data(self):
|
|
# GH14956
|
|
series = pd.Series([datetime(2015, 1, 1, tzinfo=pytz.utc), 1])
|
|
result = series.ffill()
|
|
tm.assert_series_equal(series, result)
|
|
|
|
def test_bfill(self):
|
|
ts = Series([0.0, 1.0, 2.0, 3.0, 4.0], index=tm.makeDateIndex(5))
|
|
ts[2] = np.NaN
|
|
tm.assert_series_equal(ts.bfill(), ts.fillna(method="bfill"))
|
|
|
|
def test_timedelta64_nan(self):
|
|
|
|
td = Series([timedelta(days=i) for i in range(10)])
|
|
|
|
# nan ops on timedeltas
|
|
td1 = td.copy()
|
|
td1[0] = np.nan
|
|
assert isna(td1[0])
|
|
assert td1[0].value == iNaT
|
|
td1[0] = td[0]
|
|
assert not isna(td1[0])
|
|
|
|
# GH#16674 iNaT is treated as an integer when given by the user
|
|
td1[1] = iNaT
|
|
assert not isna(td1[1])
|
|
assert td1.dtype == np.object_
|
|
assert td1[1] == iNaT
|
|
td1[1] = td[1]
|
|
assert not isna(td1[1])
|
|
|
|
td1[2] = NaT
|
|
assert isna(td1[2])
|
|
assert td1[2].value == iNaT
|
|
td1[2] = td[2]
|
|
assert not isna(td1[2])
|
|
|
|
# FIXME: don't leave commented-out
|
|
# boolean setting
|
|
# this doesn't work, not sure numpy even supports it
|
|
# result = td[(td>np.timedelta64(timedelta(days=3))) &
|
|
# td<np.timedelta64(timedelta(days=7)))] = np.nan
|
|
# assert isna(result).sum() == 7
|
|
|
|
# NumPy limitation =(
|
|
|
|
# def test_logical_range_select(self):
|
|
# np.random.seed(12345)
|
|
# selector = -0.5 <= datetime_series <= 0.5
|
|
# expected = (datetime_series >= -0.5) & (datetime_series <= 0.5)
|
|
# tm.assert_series_equal(selector, expected)
|
|
|
|
def test_dropna_empty(self):
|
|
s = Series([], dtype=object)
|
|
|
|
assert len(s.dropna()) == 0
|
|
return_value = s.dropna(inplace=True)
|
|
assert return_value is None
|
|
assert len(s) == 0
|
|
|
|
# invalid axis
|
|
msg = "No axis named 1 for object type Series"
|
|
with pytest.raises(ValueError, match=msg):
|
|
s.dropna(axis=1)
|
|
|
|
def test_datetime64_tz_dropna(self):
|
|
# DatetimeBlock
|
|
s = Series(
|
|
[
|
|
Timestamp("2011-01-01 10:00"),
|
|
pd.NaT,
|
|
Timestamp("2011-01-03 10:00"),
|
|
pd.NaT,
|
|
]
|
|
)
|
|
result = s.dropna()
|
|
expected = Series(
|
|
[Timestamp("2011-01-01 10:00"), Timestamp("2011-01-03 10:00")], index=[0, 2]
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# DatetimeBlockTZ
|
|
idx = pd.DatetimeIndex(
|
|
["2011-01-01 10:00", pd.NaT, "2011-01-03 10:00", pd.NaT], tz="Asia/Tokyo"
|
|
)
|
|
s = pd.Series(idx)
|
|
assert s.dtype == "datetime64[ns, Asia/Tokyo]"
|
|
result = s.dropna()
|
|
expected = Series(
|
|
[
|
|
Timestamp("2011-01-01 10:00", tz="Asia/Tokyo"),
|
|
Timestamp("2011-01-03 10:00", tz="Asia/Tokyo"),
|
|
],
|
|
index=[0, 2],
|
|
)
|
|
assert result.dtype == "datetime64[ns, Asia/Tokyo]"
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_dropna_no_nan(self):
|
|
for s in [Series([1, 2, 3], name="x"), Series([False, True, False], name="x")]:
|
|
|
|
result = s.dropna()
|
|
tm.assert_series_equal(result, s)
|
|
assert result is not s
|
|
|
|
s2 = s.copy()
|
|
return_value = s2.dropna(inplace=True)
|
|
assert return_value is None
|
|
tm.assert_series_equal(s2, s)
|
|
|
|
def test_dropna_intervals(self):
|
|
s = Series(
|
|
[np.nan, 1, 2, 3],
|
|
IntervalIndex.from_arrays([np.nan, 0, 1, 2], [np.nan, 1, 2, 3]),
|
|
)
|
|
|
|
result = s.dropna()
|
|
expected = s.iloc[1:]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_valid(self, datetime_series):
|
|
ts = datetime_series.copy()
|
|
ts.index = ts.index._with_freq(None)
|
|
ts[::2] = np.NaN
|
|
|
|
result = ts.dropna()
|
|
assert len(result) == ts.count()
|
|
tm.assert_series_equal(result, ts[1::2])
|
|
tm.assert_series_equal(result, ts[pd.notna(ts)])
|
|
|
|
def test_isna(self):
|
|
ser = Series([0, 5.4, 3, np.nan, -0.001])
|
|
expected = Series([False, False, False, True, False])
|
|
tm.assert_series_equal(ser.isna(), expected)
|
|
|
|
ser = Series(["hi", "", np.nan])
|
|
expected = Series([False, False, True])
|
|
tm.assert_series_equal(ser.isna(), expected)
|
|
|
|
def test_notna(self):
|
|
ser = Series([0, 5.4, 3, np.nan, -0.001])
|
|
expected = Series([True, True, True, False, True])
|
|
tm.assert_series_equal(ser.notna(), expected)
|
|
|
|
ser = Series(["hi", "", np.nan])
|
|
expected = Series([True, True, False])
|
|
tm.assert_series_equal(ser.notna(), expected)
|
|
|
|
def test_pad_nan(self):
|
|
x = Series(
|
|
[np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"], dtype=float
|
|
)
|
|
|
|
return_value = x.fillna(method="pad", inplace=True)
|
|
assert return_value is None
|
|
|
|
expected = Series(
|
|
[np.nan, 1.0, 1.0, 3.0, 3.0], ["z", "a", "b", "c", "d"], dtype=float
|
|
)
|
|
tm.assert_series_equal(x[1:], expected[1:])
|
|
assert np.isnan(x[0]), np.isnan(expected[0])
|
|
|
|
def test_pad_require_monotonicity(self):
|
|
rng = date_range("1/1/2000", "3/1/2000", freq="B")
|
|
|
|
# neither monotonic increasing or decreasing
|
|
rng2 = rng[[1, 0, 2]]
|
|
|
|
msg = "index must be monotonic increasing or decreasing"
|
|
with pytest.raises(ValueError, match=msg):
|
|
rng2.get_indexer(rng, method="pad")
|
|
|
|
def test_dropna_preserve_name(self, datetime_series):
|
|
datetime_series[:5] = np.nan
|
|
result = datetime_series.dropna()
|
|
assert result.name == datetime_series.name
|
|
name = datetime_series.name
|
|
ts = datetime_series.copy()
|
|
return_value = ts.dropna(inplace=True)
|
|
assert return_value is None
|
|
assert ts.name == name
|
|
|
|
def test_series_fillna_limit(self):
|
|
index = np.arange(10)
|
|
s = Series(np.random.randn(10), index=index)
|
|
|
|
result = s[:2].reindex(index)
|
|
result = result.fillna(method="pad", limit=5)
|
|
|
|
expected = s[:2].reindex(index).fillna(method="pad")
|
|
expected[-3:] = np.nan
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s[-2:].reindex(index)
|
|
result = result.fillna(method="bfill", limit=5)
|
|
|
|
expected = s[-2:].reindex(index).fillna(method="backfill")
|
|
expected[:3] = np.nan
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_series_pad_backfill_limit(self):
|
|
index = np.arange(10)
|
|
s = Series(np.random.randn(10), index=index)
|
|
|
|
result = s[:2].reindex(index, method="pad", limit=5)
|
|
|
|
expected = s[:2].reindex(index).fillna(method="pad")
|
|
expected[-3:] = np.nan
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s[-2:].reindex(index, method="backfill", limit=5)
|
|
|
|
expected = s[-2:].reindex(index).fillna(method="backfill")
|
|
expected[:3] = np.nan
|
|
tm.assert_series_equal(result, expected)
|