import numpy as np
import pytest

import pandas as pd
from pandas import (
    DataFrame,
    Series,
    Timestamp,
    date_range,
    timedelta_range,
)
import pandas._testing as tm


class TestDataFrameAppend:
    @pytest.mark.filterwarnings("ignore:.*append method is deprecated.*:FutureWarning")
    def test_append_multiindex(self, multiindex_dataframe_random_data, frame_or_series):
        obj = multiindex_dataframe_random_data
        obj = tm.get_obj(obj, frame_or_series)

        a = obj[:5]
        b = obj[5:]

        result = a.append(b)
        tm.assert_equal(result, obj)

    def test_append_empty_list(self):
        # GH 28769
        df = DataFrame()
        result = df._append([])
        expected = df
        tm.assert_frame_equal(result, expected)
        assert result is not df

        df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"])
        result = df._append([])
        expected = df
        tm.assert_frame_equal(result, expected)
        assert result is not df  # ._append() should return a new object

    def test_append_series_dict(self):
        df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"])

        series = df.loc[4]
        msg = "Indexes have overlapping values"
        with pytest.raises(ValueError, match=msg):
            df._append(series, verify_integrity=True)

        series.name = None
        msg = "Can only append a Series if ignore_index=True"
        with pytest.raises(TypeError, match=msg):
            df._append(series, verify_integrity=True)

        result = df._append(series[::-1], ignore_index=True)
        expected = df._append(
            DataFrame({0: series[::-1]}, index=df.columns).T, ignore_index=True
        )
        tm.assert_frame_equal(result, expected)

        # dict
        result = df._append(series.to_dict(), ignore_index=True)
        tm.assert_frame_equal(result, expected)

        result = df._append(series[::-1][:3], ignore_index=True)
        expected = df._append(
            DataFrame({0: series[::-1][:3]}).T, ignore_index=True, sort=True
        )
        tm.assert_frame_equal(result, expected.loc[:, result.columns])

        msg = "Can only append a dict if ignore_index=True"
        with pytest.raises(TypeError, match=msg):
            df._append(series.to_dict())

        # can append when name set
        row = df.loc[4]
        row.name = 5
        result = df._append(row)
        expected = df._append(df[-1:], ignore_index=True)
        tm.assert_frame_equal(result, expected)

    def test_append_list_of_series_dicts(self):
        df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"])

        dicts = [x.to_dict() for idx, x in df.iterrows()]

        result = df._append(dicts, ignore_index=True)
        expected = df._append(df, ignore_index=True)
        tm.assert_frame_equal(result, expected)

        # different columns
        dicts = [
            {"foo": 1, "bar": 2, "baz": 3, "peekaboo": 4},
            {"foo": 5, "bar": 6, "baz": 7, "peekaboo": 8},
        ]
        result = df._append(dicts, ignore_index=True, sort=True)
        expected = df._append(DataFrame(dicts), ignore_index=True, sort=True)
        tm.assert_frame_equal(result, expected)

    def test_append_list_retain_index_name(self):
        df = DataFrame(
            [[1, 2], [3, 4]], index=pd.Index(["a", "b"], name="keepthisname")
        )

        serc = Series([5, 6], name="c")

        expected = DataFrame(
            [[1, 2], [3, 4], [5, 6]],
            index=pd.Index(["a", "b", "c"], name="keepthisname"),
        )

        # append series
        result = df._append(serc)
        tm.assert_frame_equal(result, expected)

        # append list of series
        result = df._append([serc])
        tm.assert_frame_equal(result, expected)

    def test_append_missing_cols(self):
        # GH22252
        # exercise the conditional branch in append method where the data
        # to be appended is a list and does not contain all columns that are in
        # the target DataFrame
        df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"])

        dicts = [{"foo": 9}, {"bar": 10}]
        result = df._append(dicts, ignore_index=True, sort=True)

        expected = df._append(DataFrame(dicts), ignore_index=True, sort=True)
        tm.assert_frame_equal(result, expected)

    def test_append_empty_dataframe(self):

        # Empty df append empty df
        df1 = DataFrame()
        df2 = DataFrame()
        result = df1._append(df2)
        expected = df1.copy()
        tm.assert_frame_equal(result, expected)

        # Non-empty df append empty df
        df1 = DataFrame(np.random.randn(5, 2))
        df2 = DataFrame()
        result = df1._append(df2)
        expected = df1.copy()
        tm.assert_frame_equal(result, expected)

        # Empty df with columns append empty df
        df1 = DataFrame(columns=["bar", "foo"])
        df2 = DataFrame()
        result = df1._append(df2)
        expected = df1.copy()
        tm.assert_frame_equal(result, expected)

        # Non-Empty df with columns append empty df
        df1 = DataFrame(np.random.randn(5, 2), columns=["bar", "foo"])
        df2 = DataFrame()
        result = df1._append(df2)
        expected = df1.copy()
        tm.assert_frame_equal(result, expected)

    def test_append_dtypes(self):

        # GH 5754
        # row appends of different dtypes (so need to do by-item)
        # can sometimes infer the correct type

        df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(5))
        df2 = DataFrame()
        result = df1._append(df2)
        expected = df1.copy()
        tm.assert_frame_equal(result, expected)

        df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
        df2 = DataFrame({"bar": "foo"}, index=range(1, 2))
        result = df1._append(df2)
        expected = DataFrame({"bar": [Timestamp("20130101"), "foo"]})
        tm.assert_frame_equal(result, expected)

        df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
        df2 = DataFrame({"bar": np.nan}, index=range(1, 2))
        result = df1._append(df2)
        expected = DataFrame(
            {"bar": Series([Timestamp("20130101"), np.nan], dtype="M8[ns]")}
        )
        expected = expected.astype(object)
        tm.assert_frame_equal(result, expected)

        df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
        df2 = DataFrame({"bar": np.nan}, index=range(1, 2), dtype=object)
        result = df1._append(df2)
        expected = DataFrame(
            {"bar": Series([Timestamp("20130101"), np.nan], dtype="M8[ns]")}
        )
        expected = expected.astype(object)
        tm.assert_frame_equal(result, expected)

        df1 = DataFrame({"bar": np.nan}, index=range(1))
        df2 = DataFrame({"bar": Timestamp("20130101")}, index=range(1, 2))
        result = df1._append(df2)
        expected = DataFrame(
            {"bar": Series([np.nan, Timestamp("20130101")], dtype="M8[ns]")}
        )
        expected = expected.astype(object)
        tm.assert_frame_equal(result, expected)

        df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
        df2 = DataFrame({"bar": 1}, index=range(1, 2), dtype=object)
        result = df1._append(df2)
        expected = DataFrame({"bar": Series([Timestamp("20130101"), 1])})
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        "timestamp", ["2019-07-19 07:04:57+0100", "2019-07-19 07:04:57"]
    )
    def test_append_timestamps_aware_or_naive(self, tz_naive_fixture, timestamp):
        # GH 30238
        tz = tz_naive_fixture
        df = DataFrame([Timestamp(timestamp, tz=tz)])
        result = df._append(df.iloc[0]).iloc[-1]
        expected = Series(Timestamp(timestamp, tz=tz), name=0)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "data, dtype",
        [
            ([1], pd.Int64Dtype()),
            ([1], pd.CategoricalDtype()),
            ([pd.Interval(left=0, right=5)], pd.IntervalDtype()),
            ([pd.Period("2000-03", freq="M")], pd.PeriodDtype("M")),
            ([1], pd.SparseDtype()),
        ],
    )
    def test_other_dtypes(self, data, dtype):
        df = DataFrame(data, dtype=dtype)
        result = df._append(df.iloc[0]).iloc[-1]
        expected = Series(data, name=0, dtype=dtype)
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
    def test_append_numpy_bug_1681(self, dtype):
        # another datetime64 bug
        if dtype == "datetime64[ns]":
            index = date_range("2011/1/1", "2012/1/1", freq="W-FRI")
        else:
            index = timedelta_range("1 days", "10 days", freq="2D")

        df = DataFrame()
        other = DataFrame({"A": "foo", "B": index}, index=index)

        result = df._append(other)
        assert (result["B"] == index).all()

    @pytest.mark.filterwarnings("ignore:The values in the array:RuntimeWarning")
    def test_multiindex_column_append_multiple(self):
        # GH 29699
        df = DataFrame(
            [[1, 11], [2, 12], [3, 13]],
            columns=pd.MultiIndex.from_tuples(
                [("multi", "col1"), ("multi", "col2")], names=["level1", None]
            ),
        )
        df2 = df.copy()
        for i in range(1, 10):
            df[i, "colA"] = 10
            df = df._append(df2, ignore_index=True)
            result = df["multi"]
            expected = DataFrame(
                {"col1": [1, 2, 3] * (i + 1), "col2": [11, 12, 13] * (i + 1)}
            )
            tm.assert_frame_equal(result, expected)

    def test_append_raises_future_warning(self):
        # GH#35407
        df1 = DataFrame([[1, 2], [3, 4]])
        df2 = DataFrame([[5, 6], [7, 8]])
        with tm.assert_produces_warning(FutureWarning):
            df1.append(df2)
