from datetime import datetime

import numpy as np
import pytest

import pandas as pd
from pandas import Categorical, DataFrame, Index, MultiIndex, Series, date_range, isna
import pandas._testing as tm


class TestDataFrameSelectReindex:
    # These are specific reindex-based tests; other indexing tests should go in
    # test_indexing

    def test_merge_join_different_levels(self):
        # GH 9455

        # first dataframe
        df1 = DataFrame(columns=["a", "b"], data=[[1, 11], [0, 22]])

        # second dataframe
        columns = MultiIndex.from_tuples([("a", ""), ("c", "c1")])
        df2 = DataFrame(columns=columns, data=[[1, 33], [0, 44]])

        # merge
        columns = ["a", "b", ("c", "c1")]
        expected = DataFrame(columns=columns, data=[[1, 11, 33], [0, 22, 44]])
        with tm.assert_produces_warning(UserWarning):
            result = pd.merge(df1, df2, on="a")
        tm.assert_frame_equal(result, expected)

        # join, see discussion in GH 12219
        columns = ["a", "b", ("a", ""), ("c", "c1")]
        expected = DataFrame(columns=columns, data=[[1, 11, 0, 44], [0, 22, 1, 33]])
        with tm.assert_produces_warning(UserWarning):
            result = df1.join(df2, on="a")
        tm.assert_frame_equal(result, expected)

    def test_reindex(self, float_frame):
        datetime_series = tm.makeTimeSeries(nper=30)

        newFrame = float_frame.reindex(datetime_series.index)

        for col in newFrame.columns:
            for idx, val in newFrame[col].items():
                if idx in float_frame.index:
                    if np.isnan(val):
                        assert np.isnan(float_frame[col][idx])
                    else:
                        assert val == float_frame[col][idx]
                else:
                    assert np.isnan(val)

        for col, series in newFrame.items():
            assert tm.equalContents(series.index, newFrame.index)
        emptyFrame = float_frame.reindex(Index([]))
        assert len(emptyFrame.index) == 0

        # Cython code should be unit-tested directly
        nonContigFrame = float_frame.reindex(datetime_series.index[::2])

        for col in nonContigFrame.columns:
            for idx, val in nonContigFrame[col].items():
                if idx in float_frame.index:
                    if np.isnan(val):
                        assert np.isnan(float_frame[col][idx])
                    else:
                        assert val == float_frame[col][idx]
                else:
                    assert np.isnan(val)

        for col, series in nonContigFrame.items():
            assert tm.equalContents(series.index, nonContigFrame.index)

        # corner cases

        # Same index, copies values but not index if copy=False
        newFrame = float_frame.reindex(float_frame.index, copy=False)
        assert newFrame.index is float_frame.index

        # length zero
        newFrame = float_frame.reindex([])
        assert newFrame.empty
        assert len(newFrame.columns) == len(float_frame.columns)

        # length zero with columns reindexed with non-empty index
        newFrame = float_frame.reindex([])
        newFrame = newFrame.reindex(float_frame.index)
        assert len(newFrame.index) == len(float_frame.index)
        assert len(newFrame.columns) == len(float_frame.columns)

        # pass non-Index
        newFrame = float_frame.reindex(list(datetime_series.index))
        expected = datetime_series.index._with_freq(None)
        tm.assert_index_equal(newFrame.index, expected)

        # copy with no axes
        result = float_frame.reindex()
        tm.assert_frame_equal(result, float_frame)
        assert result is not float_frame

    def test_reindex_nan(self):
        df = pd.DataFrame(
            [[1, 2], [3, 5], [7, 11], [9, 23]],
            index=[2, np.nan, 1, 5],
            columns=["joe", "jim"],
        )

        i, j = [np.nan, 5, 5, np.nan, 1, 2, np.nan], [1, 3, 3, 1, 2, 0, 1]
        tm.assert_frame_equal(df.reindex(i), df.iloc[j])

        df.index = df.index.astype("object")
        tm.assert_frame_equal(df.reindex(i), df.iloc[j], check_index_type=False)

        # GH10388
        df = pd.DataFrame(
            {
                "other": ["a", "b", np.nan, "c"],
                "date": ["2015-03-22", np.nan, "2012-01-08", np.nan],
                "amount": [2, 3, 4, 5],
            }
        )

        df["date"] = pd.to_datetime(df.date)
        df["delta"] = (pd.to_datetime("2015-06-18") - df["date"]).shift(1)

        left = df.set_index(["delta", "other", "date"]).reset_index()
        right = df.reindex(columns=["delta", "other", "date", "amount"])
        tm.assert_frame_equal(left, right)

    def test_reindex_name_remains(self):
        s = Series(np.random.rand(10))
        df = DataFrame(s, index=np.arange(len(s)))
        i = Series(np.arange(10), name="iname")

        df = df.reindex(i)
        assert df.index.name == "iname"

        df = df.reindex(Index(np.arange(10), name="tmpname"))
        assert df.index.name == "tmpname"

        s = Series(np.random.rand(10))
        df = DataFrame(s.T, index=np.arange(len(s)))
        i = Series(np.arange(10), name="iname")
        df = df.reindex(columns=i)
        assert df.columns.name == "iname"

    def test_reindex_int(self, int_frame):
        smaller = int_frame.reindex(int_frame.index[::2])

        assert smaller["A"].dtype == np.int64

        bigger = smaller.reindex(int_frame.index)
        assert bigger["A"].dtype == np.float64

        smaller = int_frame.reindex(columns=["A", "B"])
        assert smaller["A"].dtype == np.int64

    def test_reindex_columns(self, float_frame):
        new_frame = float_frame.reindex(columns=["A", "B", "E"])

        tm.assert_series_equal(new_frame["B"], float_frame["B"])
        assert np.isnan(new_frame["E"]).all()
        assert "C" not in new_frame

        # Length zero
        new_frame = float_frame.reindex(columns=[])
        assert new_frame.empty

    def test_reindex_columns_method(self):

        # GH 14992, reindexing over columns ignored method
        df = DataFrame(
            data=[[11, 12, 13], [21, 22, 23], [31, 32, 33]],
            index=[1, 2, 4],
            columns=[1, 2, 4],
            dtype=float,
        )

        # default method
        result = df.reindex(columns=range(6))
        expected = DataFrame(
            data=[
                [np.nan, 11, 12, np.nan, 13, np.nan],
                [np.nan, 21, 22, np.nan, 23, np.nan],
                [np.nan, 31, 32, np.nan, 33, np.nan],
            ],
            index=[1, 2, 4],
            columns=range(6),
            dtype=float,
        )
        tm.assert_frame_equal(result, expected)

        # method='ffill'
        result = df.reindex(columns=range(6), method="ffill")
        expected = DataFrame(
            data=[
                [np.nan, 11, 12, 12, 13, 13],
                [np.nan, 21, 22, 22, 23, 23],
                [np.nan, 31, 32, 32, 33, 33],
            ],
            index=[1, 2, 4],
            columns=range(6),
            dtype=float,
        )
        tm.assert_frame_equal(result, expected)

        # method='bfill'
        result = df.reindex(columns=range(6), method="bfill")
        expected = DataFrame(
            data=[
                [11, 11, 12, 13, 13, np.nan],
                [21, 21, 22, 23, 23, np.nan],
                [31, 31, 32, 33, 33, np.nan],
            ],
            index=[1, 2, 4],
            columns=range(6),
            dtype=float,
        )
        tm.assert_frame_equal(result, expected)

    def test_reindex_axes(self):
        # GH 3317, reindexing by both axes loses freq of the index
        df = DataFrame(
            np.ones((3, 3)),
            index=[datetime(2012, 1, 1), datetime(2012, 1, 2), datetime(2012, 1, 3)],
            columns=["a", "b", "c"],
        )
        time_freq = date_range("2012-01-01", "2012-01-03", freq="d")
        some_cols = ["a", "b"]

        index_freq = df.reindex(index=time_freq).index.freq
        both_freq = df.reindex(index=time_freq, columns=some_cols).index.freq
        seq_freq = df.reindex(index=time_freq).reindex(columns=some_cols).index.freq
        assert index_freq == both_freq
        assert index_freq == seq_freq

    def test_reindex_fill_value(self):
        df = DataFrame(np.random.randn(10, 4))

        # axis=0
        result = df.reindex(list(range(15)))
        assert np.isnan(result.values[-5:]).all()

        result = df.reindex(range(15), fill_value=0)
        expected = df.reindex(range(15)).fillna(0)
        tm.assert_frame_equal(result, expected)

        # axis=1
        result = df.reindex(columns=range(5), fill_value=0.0)
        expected = df.copy()
        expected[4] = 0.0
        tm.assert_frame_equal(result, expected)

        result = df.reindex(columns=range(5), fill_value=0)
        expected = df.copy()
        expected[4] = 0
        tm.assert_frame_equal(result, expected)

        result = df.reindex(columns=range(5), fill_value="foo")
        expected = df.copy()
        expected[4] = "foo"
        tm.assert_frame_equal(result, expected)

        # other dtypes
        df["foo"] = "foo"
        result = df.reindex(range(15), fill_value=0)
        expected = df.reindex(range(15)).fillna(0)
        tm.assert_frame_equal(result, expected)

    def test_reindex_dups(self):

        # GH4746, reindex on duplicate index error messages
        arr = np.random.randn(10)
        df = DataFrame(arr, index=[1, 2, 3, 4, 5, 1, 2, 3, 4, 5])

        # set index is ok
        result = df.copy()
        result.index = list(range(len(df)))
        expected = DataFrame(arr, index=list(range(len(df))))
        tm.assert_frame_equal(result, expected)

        # reindex fails
        msg = "cannot reindex from a duplicate axis"
        with pytest.raises(ValueError, match=msg):
            df.reindex(index=list(range(len(df))))

    def test_reindex_axis_style(self):
        # https://github.com/pandas-dev/pandas/issues/12392
        df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
        expected = pd.DataFrame(
            {"A": [1, 2, np.nan], "B": [4, 5, np.nan]}, index=[0, 1, 3]
        )
        result = df.reindex([0, 1, 3])
        tm.assert_frame_equal(result, expected)

        result = df.reindex([0, 1, 3], axis=0)
        tm.assert_frame_equal(result, expected)

        result = df.reindex([0, 1, 3], axis="index")
        tm.assert_frame_equal(result, expected)

    def test_reindex_positional_warns(self):
        # https://github.com/pandas-dev/pandas/issues/12392
        df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
        expected = pd.DataFrame({"A": [1.0, 2], "B": [4.0, 5], "C": [np.nan, np.nan]})
        with tm.assert_produces_warning(FutureWarning):
            result = df.reindex([0, 1], ["A", "B", "C"])

        tm.assert_frame_equal(result, expected)

    def test_reindex_axis_style_raises(self):
        # https://github.com/pandas-dev/pandas/issues/12392
        df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
            df.reindex([0, 1], ["A"], axis=1)

        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
            df.reindex([0, 1], ["A"], axis="index")

        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
            df.reindex(index=[0, 1], axis="index")

        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
            df.reindex(index=[0, 1], axis="columns")

        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
            df.reindex(columns=[0, 1], axis="columns")

        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
            df.reindex(index=[0, 1], columns=[0, 1], axis="columns")

        with pytest.raises(TypeError, match="Cannot specify all"):
            df.reindex([0, 1], [0], ["A"])

        # Mixing styles
        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
            df.reindex(index=[0, 1], axis="index")

        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
            df.reindex(index=[0, 1], axis="columns")

        # Duplicates
        with pytest.raises(TypeError, match="multiple values"):
            df.reindex([0, 1], labels=[0, 1])

    def test_reindex_single_named_indexer(self):
        # https://github.com/pandas-dev/pandas/issues/12392
        df = pd.DataFrame({"A": [1, 2, 3], "B": [1, 2, 3]})
        result = df.reindex([0, 1], columns=["A"])
        expected = pd.DataFrame({"A": [1, 2]})
        tm.assert_frame_equal(result, expected)

    def test_reindex_api_equivalence(self):
        # https://github.com/pandas-dev/pandas/issues/12392
        # equivalence of the labels/axis and index/columns API's
        df = DataFrame(
            [[1, 2, 3], [3, 4, 5], [5, 6, 7]],
            index=["a", "b", "c"],
            columns=["d", "e", "f"],
        )

        res1 = df.reindex(["b", "a"])
        res2 = df.reindex(index=["b", "a"])
        res3 = df.reindex(labels=["b", "a"])
        res4 = df.reindex(labels=["b", "a"], axis=0)
        res5 = df.reindex(["b", "a"], axis=0)
        for res in [res2, res3, res4, res5]:
            tm.assert_frame_equal(res1, res)

        res1 = df.reindex(columns=["e", "d"])
        res2 = df.reindex(["e", "d"], axis=1)
        res3 = df.reindex(labels=["e", "d"], axis=1)
        for res in [res2, res3]:
            tm.assert_frame_equal(res1, res)

        with tm.assert_produces_warning(FutureWarning) as m:
            res1 = df.reindex(["b", "a"], ["e", "d"])
        assert "reindex" in str(m[0].message)
        res2 = df.reindex(columns=["e", "d"], index=["b", "a"])
        res3 = df.reindex(labels=["b", "a"], axis=0).reindex(labels=["e", "d"], axis=1)
        for res in [res2, res3]:
            tm.assert_frame_equal(res1, res)

    def test_align_int_fill_bug(self):
        # GH #910
        X = np.arange(10 * 10, dtype="float64").reshape(10, 10)
        Y = np.ones((10, 1), dtype=int)

        df1 = DataFrame(X)
        df1["0.X"] = Y.squeeze()

        df2 = df1.astype(float)

        result = df1 - df1.mean()
        expected = df2 - df2.mean()
        tm.assert_frame_equal(result, expected)

    def test_reindex_boolean(self):
        frame = DataFrame(
            np.ones((10, 2), dtype=bool), index=np.arange(0, 20, 2), columns=[0, 2]
        )

        reindexed = frame.reindex(np.arange(10))
        assert reindexed.values.dtype == np.object_
        assert isna(reindexed[0][1])

        reindexed = frame.reindex(columns=range(3))
        assert reindexed.values.dtype == np.object_
        assert isna(reindexed[1]).all()

    def test_reindex_objects(self, float_string_frame):
        reindexed = float_string_frame.reindex(columns=["foo", "A", "B"])
        assert "foo" in reindexed

        reindexed = float_string_frame.reindex(columns=["A", "B"])
        assert "foo" not in reindexed

    def test_reindex_corner(self, int_frame):
        index = Index(["a", "b", "c"])
        dm = DataFrame({}).reindex(index=[1, 2, 3])
        reindexed = dm.reindex(columns=index)
        tm.assert_index_equal(reindexed.columns, index)

        # ints are weird
        smaller = int_frame.reindex(columns=["A", "B", "E"])
        assert smaller["E"].dtype == np.float64

    def test_reindex_with_nans(self):
        df = DataFrame(
            [[1, 2], [3, 4], [np.nan, np.nan], [7, 8], [9, 10]],
            columns=["a", "b"],
            index=[100.0, 101.0, np.nan, 102.0, 103.0],
        )

        result = df.reindex(index=[101.0, 102.0, 103.0])
        expected = df.iloc[[1, 3, 4]]
        tm.assert_frame_equal(result, expected)

        result = df.reindex(index=[103.0])
        expected = df.iloc[[4]]
        tm.assert_frame_equal(result, expected)

        result = df.reindex(index=[101.0])
        expected = df.iloc[[1]]
        tm.assert_frame_equal(result, expected)

    def test_reindex_multi(self):
        df = DataFrame(np.random.randn(3, 3))

        result = df.reindex(index=range(4), columns=range(4))
        expected = df.reindex(list(range(4))).reindex(columns=range(4))

        tm.assert_frame_equal(result, expected)

        df = DataFrame(np.random.randint(0, 10, (3, 3)))

        result = df.reindex(index=range(4), columns=range(4))
        expected = df.reindex(list(range(4))).reindex(columns=range(4))

        tm.assert_frame_equal(result, expected)

        df = DataFrame(np.random.randint(0, 10, (3, 3)))

        result = df.reindex(index=range(2), columns=range(2))
        expected = df.reindex(range(2)).reindex(columns=range(2))

        tm.assert_frame_equal(result, expected)

        df = DataFrame(np.random.randn(5, 3) + 1j, columns=["a", "b", "c"])

        result = df.reindex(index=[0, 1], columns=["a", "b"])
        expected = df.reindex([0, 1]).reindex(columns=["a", "b"])

        tm.assert_frame_equal(result, expected)

    def test_reindex_multi_categorical_time(self):
        # https://github.com/pandas-dev/pandas/issues/21390
        midx = pd.MultiIndex.from_product(
            [
                Categorical(["a", "b", "c"]),
                Categorical(date_range("2012-01-01", periods=3, freq="H")),
            ]
        )
        df = pd.DataFrame({"a": range(len(midx))}, index=midx)
        df2 = df.iloc[[0, 1, 2, 3, 4, 5, 6, 8]]

        result = df2.reindex(midx)
        expected = pd.DataFrame({"a": [0, 1, 2, 3, 4, 5, 6, np.nan, 8]}, index=midx)
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        "operation", ["__iadd__", "__isub__", "__imul__", "__ipow__"]
    )
    @pytest.mark.parametrize("inplace", [False, True])
    def test_inplace_drop_and_operation(self, operation, inplace):
        # GH 30484
        df = pd.DataFrame({"x": range(5)})
        expected = df.copy()
        df["y"] = range(5)
        y = df["y"]

        with tm.assert_produces_warning(None):
            if inplace:
                df.drop("y", axis=1, inplace=inplace)
            else:
                df = df.drop("y", axis=1, inplace=inplace)

            # Perform operation and check result
            getattr(y, operation)(1)
            tm.assert_frame_equal(df, expected)