import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm
from pandas.core.arrays.sparse import SparseArray


class TestSparseArrayConcat:
    @pytest.mark.parametrize("kind", ["integer", "block"])
    def test_basic(self, kind):
        a = SparseArray([1, 0, 0, 2], kind=kind)
        b = SparseArray([1, 0, 2, 2], kind=kind)

        result = SparseArray._concat_same_type([a, b])
        # Can't make any assertions about the sparse index itself
        # since we aren't don't merge sparse blocs across arrays
        # in to_concat
        expected = np.array([1, 2, 1, 2, 2], dtype="int64")
        tm.assert_numpy_array_equal(result.sp_values, expected)
        assert result.kind == kind

    @pytest.mark.parametrize("kind", ["integer", "block"])
    def test_uses_first_kind(self, kind):
        other = "integer" if kind == "block" else "block"
        a = SparseArray([1, 0, 0, 2], kind=kind)
        b = SparseArray([1, 0, 2, 2], kind=other)

        result = SparseArray._concat_same_type([a, b])
        expected = np.array([1, 2, 1, 2, 2], dtype="int64")
        tm.assert_numpy_array_equal(result.sp_values, expected)
        assert result.kind == kind


def test_concat_different_fill_values(performance_warning):
    # GH#35795 the second array's gap positions hold its own fill_value, so they
    #  have to be converted rather than read as the first array's fill_value
    ser1 = pd.Series(SparseArray([0, 0, 1], fill_value=0))
    ser2 = pd.Series(SparseArray([1, 0, 0], fill_value=1))

    msg = "Concatenating sparse arrays with multiple fill values"
    with tm.assert_produces_warning(performance_warning, match=msg):
        result = pd.concat([ser1, ser2], ignore_index=True)

    expected = pd.Series(SparseArray([0, 0, 1, 1, 0, 0], fill_value=0))
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "other, expected_dtype",
    [
        # compatible dtype -> preserve sparse
        (pd.Series([3, 4, 5], dtype="int64"), pd.SparseDtype("int64", 0)),
        # (pd.Series([3, 4, 5], dtype="Int64"), pd.SparseDtype("int64", 0)),
        # incompatible dtype -> Sparse[common dtype]
        (pd.Series([1.5, 2.5, 3.5], dtype="float64"), pd.SparseDtype("float64", 0)),
        # incompatible dtype -> Sparse[object] dtype
        (pd.Series(["a", "b", "c"], dtype=object), pd.SparseDtype(object, 0)),
        # categorical with compatible categories -> dtype of the categories
        (pd.Series([3, 4, 5], dtype="category"), np.dtype("int64")),
        (pd.Series([1.5, 2.5, 3.5], dtype="category"), np.dtype("float64")),
        # categorical with incompatible categories -> object dtype
        (pd.Series(["a", "b", "c"], dtype="category"), np.dtype(object)),
    ],
)
def test_concat_with_non_sparse(other, expected_dtype):
    # https://github.com/pandas-dev/pandas/issues/34336
    s_sparse = pd.Series([1, 0, 2], dtype=pd.SparseDtype("int64", 0))

    result = pd.concat([s_sparse, other], ignore_index=True)
    expected = pd.Series(list(s_sparse) + list(other)).astype(expected_dtype)
    tm.assert_series_equal(result, expected)

    result = pd.concat([other, s_sparse], ignore_index=True)
    expected = pd.Series(list(other) + list(s_sparse)).astype(expected_dtype)
    tm.assert_series_equal(result, expected)


def test_concat_different_fill_value_warns(performance_warning):
    # GH#68567 the two dtypes used to compare equal, so find_common_type took its
    # all-equal shortcut and the warning about dropping a fill value never fired
    ser = pd.Series(SparseArray([1.0, np.nan, 2.0]))
    other = pd.Series(SparseArray([1.0, 0.0, 2.0], fill_value=0.0))

    msg = "Concatenating sparse arrays with multiple fill values"
    with tm.assert_produces_warning(performance_warning, match=msg):
        result = pd.concat([ser, other], ignore_index=True)

    assert result.dtype == pd.SparseDtype("float64", np.nan)


def test_concat_object_subtype_not_reinferred():
    # GH#69028 - an object subtype holding only datetimelikes stays object
    arr = SparseArray(
        np.array(["2016-01-01", "2000-01-01"], dtype="M8[s]"),
        fill_value=np.datetime64("2000-01-01", "s"),
    ).astype(pd.SparseDtype(object, pd.Timestamp("2000-01-01")))
    result = SparseArray._concat_same_type([arr, arr])
    assert result.dtype == arr.dtype