from datetime import datetime

import numpy as np

import pandas as pd
from pandas import (
    Period,
    Series,
    date_range,
    period_range,
    to_datetime,
)
import pandas._testing as tm


class TestCombineFirst:
    def test_combine_first_period_datetime(self):
        # GH#3367
        didx = date_range(start="1950-01-31", end="1950-07-31", freq="M")
        pidx = period_range(start=Period("1950-1"), end=Period("1950-7"), freq="M")
        # check to be consistent with DatetimeIndex
        for idx in [didx, pidx]:
            a = Series([1, np.nan, np.nan, 4, 5, np.nan, 7], index=idx)
            b = Series([9, 9, 9, 9, 9, 9, 9], index=idx)

            result = a.combine_first(b)
            expected = Series([1, 9, 9, 4, 5, 9, 7], index=idx, dtype=np.float64)
            tm.assert_series_equal(result, expected)

    def test_combine_first_name(self, datetime_series):
        result = datetime_series.combine_first(datetime_series[:5])
        assert result.name == datetime_series.name

    def test_combine_first(self):
        values = tm.makeIntIndex(20).values.astype(float)
        series = Series(values, index=tm.makeIntIndex(20))

        series_copy = series * 2
        series_copy[::2] = np.NaN

        # nothing used from the input
        combined = series.combine_first(series_copy)

        tm.assert_series_equal(combined, series)

        # Holes filled from input
        combined = series_copy.combine_first(series)
        assert np.isfinite(combined).all()

        tm.assert_series_equal(combined[::2], series[::2])
        tm.assert_series_equal(combined[1::2], series_copy[1::2])

        # mixed types
        index = tm.makeStringIndex(20)
        floats = Series(np.random.randn(20), index=index)
        strings = Series(tm.makeStringIndex(10), index=index[::2])

        combined = strings.combine_first(floats)

        tm.assert_series_equal(strings, combined.loc[index[::2]])
        tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]])

        # corner case
        ser = Series([1.0, 2, 3], index=[0, 1, 2])
        empty = Series([], index=[], dtype=object)
        result = ser.combine_first(empty)
        ser.index = ser.index.astype("O")
        tm.assert_series_equal(ser, result)

    def test_combine_first_dt64(self):

        s0 = to_datetime(Series(["2010", np.NaN]))
        s1 = to_datetime(Series([np.NaN, "2011"]))
        rs = s0.combine_first(s1)
        xp = to_datetime(Series(["2010", "2011"]))
        tm.assert_series_equal(rs, xp)

        s0 = to_datetime(Series(["2010", np.NaN]))
        s1 = Series([np.NaN, "2011"])
        rs = s0.combine_first(s1)

        msg = "containing strings is deprecated"
        with tm.assert_produces_warning(FutureWarning, match=msg):
            xp = Series([datetime(2010, 1, 1), "2011"])

        tm.assert_series_equal(rs, xp)

    def test_combine_first_dt_tz_values(self, tz_naive_fixture):
        ser1 = Series(
            pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture),
            name="ser1",
        )
        ser2 = Series(
            pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture),
            index=[2, 3, 4],
            name="ser2",
        )
        result = ser1.combine_first(ser2)
        exp_vals = pd.DatetimeIndex(
            ["20150101", "20150102", "20150103", "20160515", "20160516"],
            tz=tz_naive_fixture,
        )
        exp = Series(exp_vals, name="ser1")
        tm.assert_series_equal(exp, result)