998 lines
34 KiB
Python
998 lines
34 KiB
Python
from datetime import (
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datetime,
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timedelta,
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timezone,
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)
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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 import (
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Categorical,
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DataFrame,
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DatetimeIndex,
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NaT,
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Period,
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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 TestSeriesFillNA:
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def test_fillna_nat(self):
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series = Series([0, 1, 2, NaT.value], 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([NaT.value, 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_fillna_value_or_method(self, datetime_series):
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msg = "Cannot specify both 'value' and 'method'"
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with pytest.raises(ValueError, match=msg):
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datetime_series.fillna(value=0, method="ffill")
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def test_fillna(self):
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ts = Series([0.0, 1.0, 2.0, 3.0, 4.0], index=tm.makeDateIndex(5))
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tm.assert_series_equal(ts, ts.fillna(method="ffill"))
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ts[2] = np.NaN
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exp = Series([0.0, 1.0, 1.0, 3.0, 4.0], index=ts.index)
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tm.assert_series_equal(ts.fillna(method="ffill"), exp)
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exp = Series([0.0, 1.0, 3.0, 3.0, 4.0], index=ts.index)
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tm.assert_series_equal(ts.fillna(method="backfill"), exp)
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exp = Series([0.0, 1.0, 5.0, 3.0, 4.0], index=ts.index)
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tm.assert_series_equal(ts.fillna(value=5), exp)
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msg = "Must specify a fill 'value' or 'method'"
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with pytest.raises(ValueError, match=msg):
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ts.fillna()
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def test_fillna_nonscalar(self):
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# GH#5703
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s1 = Series([np.nan])
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s2 = Series([1])
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result = s1.fillna(s2)
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expected = Series([1.0])
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tm.assert_series_equal(result, expected)
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result = s1.fillna({})
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tm.assert_series_equal(result, s1)
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result = s1.fillna(Series((), dtype=object))
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tm.assert_series_equal(result, s1)
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result = s2.fillna(s1)
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tm.assert_series_equal(result, s2)
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result = s1.fillna({0: 1})
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tm.assert_series_equal(result, expected)
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result = s1.fillna({1: 1})
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tm.assert_series_equal(result, Series([np.nan]))
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result = s1.fillna({0: 1, 1: 1})
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tm.assert_series_equal(result, expected)
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result = s1.fillna(Series({0: 1, 1: 1}))
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tm.assert_series_equal(result, expected)
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result = s1.fillna(Series({0: 1, 1: 1}, index=[4, 5]))
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tm.assert_series_equal(result, s1)
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def test_fillna_aligns(self):
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s1 = Series([0, 1, 2], list("abc"))
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s2 = Series([0, np.nan, 2], list("bac"))
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result = s2.fillna(s1)
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expected = Series([0, 0, 2.0], list("bac"))
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tm.assert_series_equal(result, expected)
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def test_fillna_limit(self):
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ser = Series(np.nan, index=[0, 1, 2])
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result = ser.fillna(999, limit=1)
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expected = Series([999, np.nan, np.nan], index=[0, 1, 2])
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tm.assert_series_equal(result, expected)
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result = ser.fillna(999, limit=2)
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expected = Series([999, 999, np.nan], index=[0, 1, 2])
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tm.assert_series_equal(result, expected)
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def test_fillna_dont_cast_strings(self):
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# GH#9043
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# make sure a string representation of int/float values can be filled
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# correctly without raising errors or being converted
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vals = ["0", "1.5", "-0.3"]
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for val in vals:
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ser = Series([0, 1, np.nan, np.nan, 4], dtype="float64")
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result = ser.fillna(val)
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expected = Series([0, 1, val, val, 4], dtype="object")
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tm.assert_series_equal(result, expected)
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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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ser = Series([Timestamp("20130101"), NaT])
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result = ser.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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with tm.assert_produces_warning(FutureWarning, match="the 'errors' keyword"):
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result = ser.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 tm.assert_produces_warning(FutureWarning, match="the 'errors' keyword"):
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result = ser.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 = ser.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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ser2 = ser.copy()
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ser2[1] = "foo"
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tm.assert_series_equal(ser2, 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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ser = Series([1.0, np.nan])
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result = ser.fillna(0, downcast="infer")
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expected = 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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ser = Series([1.0, np.nan])
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result = ser.fillna({1: 0}, downcast="infer")
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expected = Series([1, 0])
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tm.assert_series_equal(result, expected)
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def test_fillna_downcast_infer_objects_to_numeric(self):
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# GH#44241 if we have object-dtype, 'downcast="infer"' should
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# _actually_ infer
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arr = np.arange(5).astype(object)
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arr[3] = np.nan
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ser = Series(arr)
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res = ser.fillna(3, downcast="infer")
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expected = Series(np.arange(5), dtype=np.int64)
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tm.assert_series_equal(res, expected)
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res = ser.ffill(downcast="infer")
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expected = Series([0, 1, 2, 2, 4], dtype=np.int64)
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tm.assert_series_equal(res, expected)
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res = ser.bfill(downcast="infer")
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expected = Series([0, 1, 2, 4, 4], dtype=np.int64)
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tm.assert_series_equal(res, expected)
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# with a non-round float present, we will downcast to float64
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ser[2] = 2.5
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expected = Series([0, 1, 2.5, 3, 4], dtype=np.float64)
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res = ser.fillna(3, downcast="infer")
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tm.assert_series_equal(res, expected)
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res = ser.ffill(downcast="infer")
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expected = Series([0, 1, 2.5, 2.5, 4], dtype=np.float64)
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tm.assert_series_equal(res, expected)
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res = ser.bfill(downcast="infer")
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expected = Series([0, 1, 2.5, 4, 4], dtype=np.float64)
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tm.assert_series_equal(res, expected)
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def test_timedelta_fillna(self, frame_or_series):
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# GH#3371
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ser = 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 = ser.diff()
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obj = frame_or_series(td)
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# reg fillna
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result = obj.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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expected = frame_or_series(expected)
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tm.assert_equal(result, expected)
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# interpreted as seconds, no longer supported
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msg = "value should be a 'Timedelta', 'NaT', or array of those. Got 'int'"
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wmsg = "In a future version, this will cast to a common dtype"
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with pytest.raises(TypeError, match=msg):
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with tm.assert_produces_warning(FutureWarning, match=wmsg):
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# GH#45746
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obj.fillna(1)
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result = obj.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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expected = frame_or_series(expected)
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tm.assert_equal(result, expected)
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result = obj.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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expected = frame_or_series(expected)
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tm.assert_equal(result, expected)
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result = obj.fillna(np.timedelta64(10**9))
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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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expected = frame_or_series(expected)
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tm.assert_equal(result, expected)
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result = obj.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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expected = frame_or_series(expected)
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tm.assert_equal(result, expected)
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# ffill
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td[2] = np.nan
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obj = frame_or_series(td)
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result = obj.ffill()
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expected = td.fillna(Timedelta(seconds=0))
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expected[0] = np.nan
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expected = frame_or_series(expected)
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tm.assert_equal(result, expected)
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# bfill
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td[2] = np.nan
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obj = frame_or_series(td)
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result = obj.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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expected = frame_or_series(expected)
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tm.assert_equal(result, expected)
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def test_datetime64_fillna(self):
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ser = 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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ser[2] = np.nan
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# ffill
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result = ser.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 = ser.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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def test_datetime64_fillna_backfill(self):
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# GH#6587
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# make sure that we are treating as integer when filling
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msg = "containing strings is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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# this also tests inference of a datetime-like with NaT's
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ser = Series([NaT, 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 = ser.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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# DatetimeLikeBlock
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ser = Series(
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[
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Timestamp("2011-01-01 10:00"),
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NaT,
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Timestamp("2011-01-03 10:00"),
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NaT,
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]
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)
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null_loc = Series([False, True, False, True])
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result = ser.fillna(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(isna(ser), null_loc)
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result = ser.fillna(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(isna(ser), null_loc)
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result = ser.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(isna(ser), null_loc)
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result = ser.fillna(
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{
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1: Timestamp("2011-01-02 10:00", tz=tz),
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3: 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(isna(ser), null_loc)
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result = ser.fillna(
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{1: Timestamp("2011-01-02 10:00"), 3: 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(isna(ser), null_loc)
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# DatetimeTZBlock
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idx = DatetimeIndex(["2011-01-01 10:00", NaT, "2011-01-03 10:00", NaT], tz=tz)
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ser = Series(idx)
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assert ser.dtype == f"datetime64[ns, {tz}]"
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tm.assert_series_equal(isna(ser), null_loc)
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result = ser.fillna(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(isna(ser), null_loc)
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result = ser.fillna(Timestamp("2011-01-02 10:00", tz=tz))
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idx = 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(isna(ser), null_loc)
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result = ser.fillna(Timestamp("2011-01-02 10:00", tz=tz).to_pydatetime())
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idx = 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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)
|
|
expected = Series(idx)
|
|
tm.assert_series_equal(expected, result)
|
|
tm.assert_series_equal(isna(ser), null_loc)
|
|
|
|
result = ser.fillna("AAA")
|
|
expected = Series(
|
|
[
|
|
Timestamp("2011-01-01 10:00", tz=tz),
|
|
"AAA",
|
|
Timestamp("2011-01-03 10:00", tz=tz),
|
|
"AAA",
|
|
],
|
|
dtype=object,
|
|
)
|
|
tm.assert_series_equal(expected, result)
|
|
tm.assert_series_equal(isna(ser), null_loc)
|
|
|
|
result = ser.fillna(
|
|
{
|
|
1: Timestamp("2011-01-02 10:00", tz=tz),
|
|
3: Timestamp("2011-01-04 10:00"),
|
|
}
|
|
)
|
|
expected = Series(
|
|
[
|
|
Timestamp("2011-01-01 10:00", tz=tz),
|
|
Timestamp("2011-01-02 10:00", tz=tz),
|
|
Timestamp("2011-01-03 10:00", tz=tz),
|
|
Timestamp("2011-01-04 10:00"),
|
|
]
|
|
)
|
|
tm.assert_series_equal(expected, result)
|
|
tm.assert_series_equal(isna(ser), null_loc)
|
|
|
|
result = ser.fillna(
|
|
{
|
|
1: Timestamp("2011-01-02 10:00", tz=tz),
|
|
3: Timestamp("2011-01-04 10:00", tz=tz),
|
|
}
|
|
)
|
|
expected = Series(
|
|
[
|
|
Timestamp("2011-01-01 10:00", tz=tz),
|
|
Timestamp("2011-01-02 10:00", tz=tz),
|
|
Timestamp("2011-01-03 10:00", tz=tz),
|
|
Timestamp("2011-01-04 10:00", tz=tz),
|
|
]
|
|
)
|
|
tm.assert_series_equal(expected, result)
|
|
tm.assert_series_equal(isna(ser), null_loc)
|
|
|
|
# filling with a naive/other zone, coerce to object
|
|
result = ser.fillna(Timestamp("20130101"))
|
|
expected = Series(
|
|
[
|
|
Timestamp("2011-01-01 10:00", tz=tz),
|
|
Timestamp("2013-01-01"),
|
|
Timestamp("2011-01-03 10:00", tz=tz),
|
|
Timestamp("2013-01-01"),
|
|
]
|
|
)
|
|
tm.assert_series_equal(expected, result)
|
|
tm.assert_series_equal(isna(ser), null_loc)
|
|
|
|
with tm.assert_produces_warning(FutureWarning, match="mismatched timezone"):
|
|
result = ser.fillna(Timestamp("20130101", tz="US/Pacific"))
|
|
expected = Series(
|
|
[
|
|
Timestamp("2011-01-01 10:00", tz=tz),
|
|
Timestamp("2013-01-01", tz="US/Pacific"),
|
|
Timestamp("2011-01-03 10:00", tz=tz),
|
|
Timestamp("2013-01-01", tz="US/Pacific"),
|
|
]
|
|
)
|
|
tm.assert_series_equal(expected, result)
|
|
tm.assert_series_equal(isna(ser), null_loc)
|
|
|
|
def test_fillna_dt64tz_with_method(self):
|
|
# with timezone
|
|
# GH#15855
|
|
ser = Series([Timestamp("2012-11-11 00:00:00+01:00"), NaT])
|
|
exp = Series(
|
|
[
|
|
Timestamp("2012-11-11 00:00:00+01:00"),
|
|
Timestamp("2012-11-11 00:00:00+01:00"),
|
|
]
|
|
)
|
|
tm.assert_series_equal(ser.fillna(method="pad"), exp)
|
|
|
|
ser = Series([NaT, Timestamp("2012-11-11 00:00:00+01:00")])
|
|
exp = Series(
|
|
[
|
|
Timestamp("2012-11-11 00:00:00+01:00"),
|
|
Timestamp("2012-11-11 00:00:00+01:00"),
|
|
]
|
|
)
|
|
tm.assert_series_equal(ser.fillna(method="bfill"), exp)
|
|
|
|
def test_fillna_pytimedelta(self):
|
|
# GH#8209
|
|
ser = Series([np.nan, Timedelta("1 days")], index=["A", "B"])
|
|
|
|
result = ser.fillna(timedelta(1))
|
|
expected = Series(Timedelta("1 days"), index=["A", "B"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_fillna_period(self):
|
|
# GH#13737
|
|
ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")])
|
|
|
|
res = ser.fillna(Period("2012-01", freq="M"))
|
|
exp = Series([Period("2011-01", freq="M"), Period("2012-01", freq="M")])
|
|
tm.assert_series_equal(res, exp)
|
|
assert res.dtype == "Period[M]"
|
|
|
|
def test_fillna_dt64_timestamp(self, frame_or_series):
|
|
ser = Series(
|
|
[
|
|
Timestamp("20130101"),
|
|
Timestamp("20130101"),
|
|
Timestamp("20130102"),
|
|
Timestamp("20130103 9:01:01"),
|
|
]
|
|
)
|
|
ser[2] = np.nan
|
|
obj = frame_or_series(ser)
|
|
|
|
# reg fillna
|
|
result = obj.fillna(Timestamp("20130104"))
|
|
expected = Series(
|
|
[
|
|
Timestamp("20130101"),
|
|
Timestamp("20130101"),
|
|
Timestamp("20130104"),
|
|
Timestamp("20130103 9:01:01"),
|
|
]
|
|
)
|
|
expected = frame_or_series(expected)
|
|
tm.assert_equal(result, expected)
|
|
|
|
result = obj.fillna(NaT)
|
|
expected = obj
|
|
tm.assert_equal(result, expected)
|
|
|
|
def test_fillna_dt64_non_nao(self):
|
|
# GH#27419
|
|
ser = Series([Timestamp("2010-01-01"), NaT, Timestamp("2000-01-01")])
|
|
val = np.datetime64("1975-04-05", "ms")
|
|
|
|
result = ser.fillna(val)
|
|
expected = Series(
|
|
[Timestamp("2010-01-01"), Timestamp("1975-04-05"), Timestamp("2000-01-01")]
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_fillna_numeric_inplace(self):
|
|
x = Series([np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"])
|
|
y = x.copy()
|
|
|
|
return_value = y.fillna(value=0, inplace=True)
|
|
assert return_value is None
|
|
|
|
expected = x.fillna(value=0)
|
|
tm.assert_series_equal(y, expected)
|
|
|
|
# ---------------------------------------------------------------
|
|
# CategoricalDtype
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_value, expected_output",
|
|
[
|
|
("a", ["a", "a", "b", "a", "a"]),
|
|
({1: "a", 3: "b", 4: "b"}, ["a", "a", "b", "b", "b"]),
|
|
({1: "a"}, ["a", "a", "b", np.nan, np.nan]),
|
|
({1: "a", 3: "b"}, ["a", "a", "b", "b", np.nan]),
|
|
(Series("a"), ["a", np.nan, "b", np.nan, np.nan]),
|
|
(Series("a", index=[1]), ["a", "a", "b", np.nan, np.nan]),
|
|
(Series({1: "a", 3: "b"}), ["a", "a", "b", "b", np.nan]),
|
|
(Series(["a", "b"], index=[3, 4]), ["a", np.nan, "b", "a", "b"]),
|
|
],
|
|
)
|
|
def test_fillna_categorical(self, fill_value, expected_output):
|
|
# GH#17033
|
|
# Test fillna for a Categorical series
|
|
data = ["a", np.nan, "b", np.nan, np.nan]
|
|
ser = Series(Categorical(data, categories=["a", "b"]))
|
|
exp = Series(Categorical(expected_output, categories=["a", "b"]))
|
|
result = ser.fillna(fill_value)
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_value, expected_output",
|
|
[
|
|
(Series(["a", "b", "c", "d", "e"]), ["a", "b", "b", "d", "e"]),
|
|
(Series(["b", "d", "a", "d", "a"]), ["a", "d", "b", "d", "a"]),
|
|
(
|
|
Series(
|
|
Categorical(
|
|
["b", "d", "a", "d", "a"], categories=["b", "c", "d", "e", "a"]
|
|
)
|
|
),
|
|
["a", "d", "b", "d", "a"],
|
|
),
|
|
],
|
|
)
|
|
def test_fillna_categorical_with_new_categories(self, fill_value, expected_output):
|
|
# GH#26215
|
|
data = ["a", np.nan, "b", np.nan, np.nan]
|
|
ser = Series(Categorical(data, categories=["a", "b", "c", "d", "e"]))
|
|
exp = Series(Categorical(expected_output, categories=["a", "b", "c", "d", "e"]))
|
|
result = ser.fillna(fill_value)
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
def test_fillna_categorical_raises(self):
|
|
data = ["a", np.nan, "b", np.nan, np.nan]
|
|
ser = Series(Categorical(data, categories=["a", "b"]))
|
|
cat = ser._values
|
|
|
|
msg = "Cannot setitem on a Categorical with a new category"
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.fillna("d")
|
|
|
|
msg2 = "Length of 'value' does not match."
|
|
with pytest.raises(ValueError, match=msg2):
|
|
cat.fillna(Series("d"))
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.fillna({1: "d", 3: "a"})
|
|
|
|
msg = '"value" parameter must be a scalar or dict, but you passed a "list"'
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.fillna(["a", "b"])
|
|
|
|
msg = '"value" parameter must be a scalar or dict, but you passed a "tuple"'
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.fillna(("a", "b"))
|
|
|
|
msg = (
|
|
'"value" parameter must be a scalar, dict '
|
|
'or Series, but you passed a "DataFrame"'
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.fillna(DataFrame({1: ["a"], 3: ["b"]}))
|
|
|
|
@pytest.mark.parametrize("dtype", [float, "float32", "float64"])
|
|
@pytest.mark.parametrize("fill_type", tm.ALL_REAL_NUMPY_DTYPES)
|
|
@pytest.mark.parametrize("scalar", [True, False])
|
|
def test_fillna_float_casting(self, dtype, fill_type, scalar):
|
|
# GH-43424
|
|
ser = Series([np.nan, 1.2], dtype=dtype)
|
|
fill_values = Series([2, 2], dtype=fill_type)
|
|
if scalar:
|
|
fill_values = fill_values.dtype.type(2)
|
|
|
|
result = ser.fillna(fill_values)
|
|
expected = Series([2.0, 1.2], dtype=dtype)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
ser = Series([np.nan, 1.2], dtype=dtype)
|
|
mask = ser.isna().to_numpy()
|
|
ser[mask] = fill_values
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
ser = Series([np.nan, 1.2], dtype=dtype)
|
|
ser.mask(mask, fill_values, inplace=True)
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
ser = Series([np.nan, 1.2], dtype=dtype)
|
|
res = ser.where(~mask, fill_values)
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
def test_fillna_f32_upcast_with_dict(self):
|
|
# GH-43424
|
|
ser = Series([np.nan, 1.2], dtype=np.float32)
|
|
result = ser.fillna({0: 1})
|
|
expected = Series([1.0, 1.2], dtype=np.float32)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# ---------------------------------------------------------------
|
|
# Invalid Usages
|
|
|
|
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_fillna_listlike_invalid(self):
|
|
ser = Series(np.random.randint(-100, 100, 50))
|
|
msg = '"value" parameter must be a scalar or dict, but you passed a "list"'
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.fillna([1, 2])
|
|
|
|
msg = '"value" parameter must be a scalar or dict, but you passed a "tuple"'
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.fillna((1, 2))
|
|
|
|
def test_fillna_method_and_limit_invalid(self):
|
|
|
|
# related GH#9217, make sure limit is an int and greater than 0
|
|
ser = Series([1, 2, 3, None])
|
|
msg = "|".join(
|
|
[
|
|
r"Cannot specify both 'value' and 'method'\.",
|
|
"Limit must be greater than 0",
|
|
"Limit must be an integer",
|
|
]
|
|
)
|
|
for limit in [-1, 0, 1.0, 2.0]:
|
|
for method in ["backfill", "bfill", "pad", "ffill", None]:
|
|
with pytest.raises(ValueError, match=msg):
|
|
ser.fillna(1, limit=limit, method=method)
|
|
|
|
def test_fillna_datetime64_with_timezone_tzinfo(self):
|
|
# https://github.com/pandas-dev/pandas/issues/38851
|
|
# different tzinfos representing UTC treated as equal
|
|
ser = Series(date_range("2020", periods=3, tz="UTC"))
|
|
expected = ser.copy()
|
|
ser[1] = NaT
|
|
result = ser.fillna(datetime(2020, 1, 2, tzinfo=timezone.utc))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# but we dont (yet) consider distinct tzinfos for non-UTC tz equivalent
|
|
ts = Timestamp("2000-01-01", tz="US/Pacific")
|
|
ser2 = Series(ser._values.tz_convert("dateutil/US/Pacific"))
|
|
assert ser2.dtype.kind == "M"
|
|
with tm.assert_produces_warning(FutureWarning, match="mismatched timezone"):
|
|
result = ser2.fillna(ts)
|
|
expected = Series([ser[0], ts, ser[2]], dtype=object)
|
|
# TODO(2.0): once deprecation is enforced
|
|
# expected = Series(
|
|
# [ser2[0], ts.tz_convert(ser2.dtype.tz), ser2[2]],
|
|
# dtype=ser2.dtype,
|
|
# )
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_fillna_pos_args_deprecation(self):
|
|
# https://github.com/pandas-dev/pandas/issues/41485
|
|
srs = Series([1, 2, 3, np.nan], dtype=float)
|
|
msg = (
|
|
r"In a future version of pandas all arguments of Series.fillna "
|
|
r"except for the argument 'value' will be keyword-only"
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
result = srs.fillna(0, None, None)
|
|
expected = Series([1, 2, 3, 0], dtype=float)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"input, input_fillna, expected_data, expected_categories",
|
|
[
|
|
(["A", "B", None, "A"], "B", ["A", "B", "B", "A"], ["A", "B"]),
|
|
(["A", "B", np.nan, "A"], "B", ["A", "B", "B", "A"], ["A", "B"]),
|
|
],
|
|
)
|
|
def test_fillna_categorical_accept_same_type(
|
|
self, input, input_fillna, expected_data, expected_categories
|
|
):
|
|
# GH32414
|
|
cat = Categorical(input)
|
|
ser = Series(cat).fillna(input_fillna)
|
|
filled = cat.fillna(ser)
|
|
result = cat.fillna(filled)
|
|
expected = Categorical(expected_data, categories=expected_categories)
|
|
tm.assert_categorical_equal(result, expected)
|
|
|
|
|
|
class TestFillnaPad:
|
|
def test_fillna_bug(self):
|
|
ser = Series([np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"])
|
|
filled = ser.fillna(method="ffill")
|
|
expected = Series([np.nan, 1.0, 1.0, 3.0, 3.0], ser.index)
|
|
tm.assert_series_equal(filled, expected)
|
|
|
|
filled = ser.fillna(method="bfill")
|
|
expected = Series([1.0, 1.0, 3.0, 3.0, np.nan], ser.index)
|
|
tm.assert_series_equal(filled, expected)
|
|
|
|
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_pos_args_deprecation(self):
|
|
# https://github.com/pandas-dev/pandas/issues/41485
|
|
ser = Series([1, 2, 3])
|
|
msg = (
|
|
r"In a future version of pandas all arguments of Series.ffill "
|
|
r"will be keyword-only"
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
result = ser.ffill(0)
|
|
expected = Series([1, 2, 3])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_ffill_mixed_dtypes_without_missing_data(self):
|
|
# GH#14956
|
|
series = 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_bfill_pos_args_deprecation(self):
|
|
# https://github.com/pandas-dev/pandas/issues/41485
|
|
ser = Series([1, 2, 3])
|
|
msg = (
|
|
r"In a future version of pandas all arguments of Series.bfill "
|
|
r"will be keyword-only"
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
result = ser.bfill(0)
|
|
expected = Series([1, 2, 3])
|
|
tm.assert_series_equal(result, 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_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):
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index = np.arange(10)
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|
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
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|
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)
|
|
|
|
def test_fillna_int(self):
|
|
ser = Series(np.random.randint(-100, 100, 50))
|
|
return_value = ser.fillna(method="ffill", inplace=True)
|
|
assert return_value is None
|
|
tm.assert_series_equal(ser.fillna(method="ffill", inplace=False), ser)
|
|
|
|
def test_datetime64tz_fillna_round_issue(self):
|
|
# GH#14872
|
|
|
|
data = Series(
|
|
[NaT, NaT, datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc)]
|
|
)
|
|
|
|
filled = data.fillna(method="bfill")
|
|
|
|
expected = Series(
|
|
[
|
|
datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
|
|
datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
|
|
datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
|
|
]
|
|
)
|
|
|
|
tm.assert_series_equal(filled, expected)
|