from datetime import datetime
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import numpy as np
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import pandas as pd
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from pandas import (
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Period,
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Series,
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date_range,
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period_range,
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to_datetime,
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)
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import pandas._testing as tm
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class TestCombineFirst:
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def test_combine_first_period_datetime(self):
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# GH#3367
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didx = date_range(start="1950-01-31", end="1950-07-31", freq="ME")
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pidx = period_range(start=Period("1950-1"), end=Period("1950-7"), freq="M")
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# check to be consistent with DatetimeIndex
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for idx in [didx, pidx]:
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a = Series([1, np.nan, np.nan, 4, 5, np.nan, 7], index=idx)
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b = Series([9, 9, 9, 9, 9, 9, 9], index=idx)
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result = a.combine_first(b)
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expected = Series([1, 9, 9, 4, 5, 9, 7], index=idx, dtype=np.float64)
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tm.assert_series_equal(result, expected)
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def test_combine_first_name(self, datetime_series):
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result = datetime_series.combine_first(datetime_series[:5])
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assert result.name == datetime_series.name
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def test_combine_first(self, using_infer_string):
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values = np.arange(20, dtype=np.float64)
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series = Series(values, index=np.arange(20, dtype=np.int64))
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series_copy = series * 2
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series_copy[::2] = np.nan
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# nothing used from the input
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combined = series.combine_first(series_copy)
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tm.assert_series_equal(combined, series)
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# Holes filled from input
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combined = series_copy.combine_first(series)
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assert np.isfinite(combined).all()
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tm.assert_series_equal(combined[::2], series[::2])
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tm.assert_series_equal(combined[1::2], series_copy[1::2])
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# mixed types
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index = pd.Index([str(i) for i in range(20)])
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floats = Series(np.random.default_rng(2).standard_normal(20), index=index)
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strings = Series([str(i) for i in range(10)], index=index[::2], dtype=object)
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combined = strings.combine_first(floats)
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tm.assert_series_equal(strings, combined.loc[index[::2]])
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tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]])
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# corner case
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ser = Series([1.0, 2, 3], index=[0, 1, 2])
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empty = Series([], index=[], dtype=object)
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msg = "The behavior of array concatenation with empty entries is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = ser.combine_first(empty)
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if not using_infer_string:
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ser.index = ser.index.astype("O")
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tm.assert_series_equal(ser, result)
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def test_combine_first_dt64(self, unit):
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s0 = to_datetime(Series(["2010", np.nan])).dt.as_unit(unit)
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s1 = to_datetime(Series([np.nan, "2011"])).dt.as_unit(unit)
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rs = s0.combine_first(s1)
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xp = to_datetime(Series(["2010", "2011"])).dt.as_unit(unit)
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tm.assert_series_equal(rs, xp)
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s0 = to_datetime(Series(["2010", np.nan])).dt.as_unit(unit)
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s1 = Series([np.nan, "2011"])
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rs = s0.combine_first(s1)
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xp = Series([datetime(2010, 1, 1), "2011"], dtype="datetime64[ns]")
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tm.assert_series_equal(rs, xp)
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def test_combine_first_dt_tz_values(self, tz_naive_fixture):
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ser1 = Series(
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pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture),
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name="ser1",
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)
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ser2 = Series(
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pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture),
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index=[2, 3, 4],
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name="ser2",
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)
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result = ser1.combine_first(ser2)
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exp_vals = pd.DatetimeIndex(
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["20150101", "20150102", "20150103", "20160515", "20160516"],
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tz=tz_naive_fixture,
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)
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exp = Series(exp_vals, name="ser1")
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tm.assert_series_equal(exp, result)
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def test_combine_first_timezone_series_with_empty_series(self):
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# GH 41800
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time_index = date_range(
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datetime(2021, 1, 1, 1),
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datetime(2021, 1, 1, 10),
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freq="h",
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tz="Europe/Rome",
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)
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s1 = Series(range(10), index=time_index)
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s2 = Series(index=time_index)
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msg = "The behavior of array concatenation with empty entries is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = s1.combine_first(s2)
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tm.assert_series_equal(result, s1)
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def test_combine_first_preserves_dtype(self):
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# GH51764
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s1 = Series([1666880195890293744, 1666880195890293837])
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s2 = Series([1, 2, 3])
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result = s1.combine_first(s2)
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expected = Series([1666880195890293744, 1666880195890293837, 3])
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tm.assert_series_equal(result, expected)
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def test_combine_mixed_timezone(self):
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# GH 26283
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uniform_tz = Series({pd.Timestamp("2019-05-01", tz="UTC"): 1.0})
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multi_tz = Series(
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{
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pd.Timestamp("2019-05-01 01:00:00+0100", tz="Europe/London"): 2.0,
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pd.Timestamp("2019-05-02", tz="UTC"): 3.0,
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}
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)
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result = uniform_tz.combine_first(multi_tz)
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expected = Series(
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[1.0, 3.0],
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index=pd.Index(
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[
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pd.Timestamp("2019-05-01 00:00:00+00:00", tz="UTC"),
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pd.Timestamp("2019-05-02 00:00:00+00:00", tz="UTC"),
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],
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dtype="object",
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),
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)
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tm.assert_series_equal(result, expected)
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