import numpy as np
import pytest

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


class TestDataFrameDiff:
    def test_diff_requires_integer(self):
        df = DataFrame(np.random.randn(2, 2))
        with pytest.raises(ValueError, match="periods must be an integer"):
            df.diff(1.5)

    # GH#44572 np.int64 is accepted
    @pytest.mark.parametrize("num", [1, np.int64(1)])
    def test_diff(self, datetime_frame, num):
        df = datetime_frame
        the_diff = df.diff(num)

        expected = df["A"] - df["A"].shift(num)
        tm.assert_series_equal(the_diff["A"], expected)

    def test_diff_int_dtype(self):
        # int dtype
        a = 10_000_000_000_000_000
        b = a + 1
        ser = Series([a, b])

        rs = DataFrame({"s": ser}).diff()
        assert rs.s[1] == 1

    def test_diff_mixed_numeric(self, datetime_frame):
        # mixed numeric
        tf = datetime_frame.astype("float32")
        the_diff = tf.diff(1)
        tm.assert_series_equal(the_diff["A"], tf["A"] - tf["A"].shift(1))

    def test_diff_axis1_nonconsolidated(self):
        # GH#10907
        df = DataFrame({"y": Series([2]), "z": Series([3])})
        df.insert(0, "x", 1)
        result = df.diff(axis=1)
        expected = DataFrame({"x": np.nan, "y": Series(1), "z": Series(1)})
        tm.assert_frame_equal(result, expected)

    def test_diff_timedelta64_with_nat(self):
        # GH#32441
        arr = np.arange(6).reshape(3, 2).astype("timedelta64[ns]")
        arr[:, 0] = np.timedelta64("NaT", "ns")

        df = DataFrame(arr)
        result = df.diff(1, axis=0)

        expected = DataFrame({0: df[0], 1: [pd.NaT, pd.Timedelta(2), pd.Timedelta(2)]})
        tm.assert_equal(result, expected)

        result = df.diff(0)
        expected = df - df
        assert expected[0].isna().all()
        tm.assert_equal(result, expected)

        result = df.diff(-1, axis=1)
        expected = df * np.nan
        tm.assert_equal(result, expected)

    @pytest.mark.parametrize("tz", [None, "UTC"])
    def test_diff_datetime_axis0_with_nat(self, tz):
        # GH#32441
        dti = pd.DatetimeIndex(["NaT", "2019-01-01", "2019-01-02"], tz=tz)
        ser = Series(dti)

        df = ser.to_frame()

        result = df.diff()
        ex_index = pd.TimedeltaIndex([pd.NaT, pd.NaT, pd.Timedelta(days=1)])
        expected = Series(ex_index).to_frame()
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize("tz", [pytest.param(None, marks=pytest.mark.xfail), "UTC"])
    def test_diff_datetime_with_nat_zero_periods(self, tz):
        # diff on NaT values should give NaT, not timedelta64(0)
        dti = date_range("2016-01-01", periods=4, tz=tz)
        ser = Series(dti)
        df = ser.to_frame()

        df[1] = ser.copy()

        with tm.assert_produces_warning(None):
            df.iloc[:, 0] = pd.NaT

        expected = df - df
        assert expected[0].isna().all()

        result = df.diff(0, axis=0)
        tm.assert_frame_equal(result, expected)

        result = df.diff(0, axis=1)
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize("tz", [None, "UTC"])
    def test_diff_datetime_axis0(self, tz):
        # GH#18578
        df = DataFrame(
            {
                0: date_range("2010", freq="D", periods=2, tz=tz),
                1: date_range("2010", freq="D", periods=2, tz=tz),
            }
        )

        result = df.diff(axis=0)
        expected = DataFrame(
            {
                0: pd.TimedeltaIndex(["NaT", "1 days"]),
                1: pd.TimedeltaIndex(["NaT", "1 days"]),
            }
        )
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize("tz", [None, "UTC"])
    def test_diff_datetime_axis1(self, tz):
        # GH#18578
        df = DataFrame(
            {
                0: date_range("2010", freq="D", periods=2, tz=tz),
                1: date_range("2010", freq="D", periods=2, tz=tz),
            }
        )

        result = df.diff(axis=1)
        expected = DataFrame(
            {
                0: pd.TimedeltaIndex(["NaT", "NaT"]),
                1: pd.TimedeltaIndex(["0 days", "0 days"]),
            }
        )
        tm.assert_frame_equal(result, expected)

    def test_diff_timedelta(self):
        # GH#4533
        df = DataFrame(
            {
                "time": [Timestamp("20130101 9:01"), Timestamp("20130101 9:02")],
                "value": [1.0, 2.0],
            }
        )

        res = df.diff()
        exp = DataFrame(
            [[pd.NaT, np.nan], [pd.Timedelta("00:01:00"), 1]], columns=["time", "value"]
        )
        tm.assert_frame_equal(res, exp)

    def test_diff_mixed_dtype(self):
        df = DataFrame(np.random.randn(5, 3))
        df["A"] = np.array([1, 2, 3, 4, 5], dtype=object)

        result = df.diff()
        assert result[0].dtype == np.float64

    def test_diff_neg_n(self, datetime_frame):
        rs = datetime_frame.diff(-1)
        xp = datetime_frame - datetime_frame.shift(-1)
        tm.assert_frame_equal(rs, xp)

    def test_diff_float_n(self, datetime_frame):
        rs = datetime_frame.diff(1.0)
        xp = datetime_frame.diff(1)
        tm.assert_frame_equal(rs, xp)

    def test_diff_axis(self):
        # GH#9727
        df = DataFrame([[1.0, 2.0], [3.0, 4.0]])
        tm.assert_frame_equal(
            df.diff(axis=1), DataFrame([[np.nan, 1.0], [np.nan, 1.0]])
        )
        tm.assert_frame_equal(
            df.diff(axis=0), DataFrame([[np.nan, np.nan], [2.0, 2.0]])
        )

    def test_diff_period(self):
        # GH#32995 Don't pass an incorrect axis
        pi = date_range("2016-01-01", periods=3).to_period("D")
        df = DataFrame({"A": pi})

        result = df.diff(1, axis=1)

        expected = (df - pd.NaT).astype(object)
        tm.assert_frame_equal(result, expected)

    def test_diff_axis1_mixed_dtypes(self):
        # GH#32995 operate column-wise when we have mixed dtypes and axis=1
        df = DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)})

        expected = DataFrame({"A": [np.nan, np.nan, np.nan], "B": df["B"] / 2})

        result = df.diff(axis=1)
        tm.assert_frame_equal(result, expected)

        # GH#21437 mixed-float-dtypes
        df = DataFrame(
            {"a": np.arange(3, dtype="float32"), "b": np.arange(3, dtype="float64")}
        )
        result = df.diff(axis=1)
        expected = DataFrame({"a": df["a"] * np.nan, "b": df["b"] * 0})
        tm.assert_frame_equal(result, expected)

    def test_diff_axis1_mixed_dtypes_large_periods(self):
        # GH#32995 operate column-wise when we have mixed dtypes and axis=1
        df = DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)})

        expected = df * np.nan

        result = df.diff(axis=1, periods=3)
        tm.assert_frame_equal(result, expected)

    def test_diff_axis1_mixed_dtypes_negative_periods(self):
        # GH#32995 operate column-wise when we have mixed dtypes and axis=1
        df = DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)})

        expected = DataFrame({"A": -1.0 * df["A"], "B": df["B"] * np.nan})

        result = df.diff(axis=1, periods=-1)
        tm.assert_frame_equal(result, expected)

    def test_diff_sparse(self):
        # GH#28813 .diff() should work for sparse dataframes as well
        sparse_df = DataFrame([[0, 1], [1, 0]], dtype="Sparse[int]")

        result = sparse_df.diff()
        expected = DataFrame(
            [[np.nan, np.nan], [1.0, -1.0]], dtype=pd.SparseDtype("float", 0.0)
        )

        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        "axis,expected",
        [
            (
                0,
                DataFrame(
                    {
                        "a": [np.nan, 0, 1, 0, np.nan, np.nan, np.nan, 0],
                        "b": [np.nan, 1, np.nan, np.nan, -2, 1, np.nan, np.nan],
                        "c": np.repeat(np.nan, 8),
                        "d": [np.nan, 3, 5, 7, 9, 11, 13, 15],
                    },
                    dtype="Int64",
                ),
            ),
            (
                1,
                DataFrame(
                    {
                        "a": np.repeat(np.nan, 8),
                        "b": [0, 1, np.nan, 1, np.nan, np.nan, np.nan, 0],
                        "c": np.repeat(np.nan, 8),
                        "d": np.repeat(np.nan, 8),
                    },
                    dtype="Int64",
                ),
            ),
        ],
    )
    def test_diff_integer_na(self, axis, expected):
        # GH#24171 IntegerNA Support for DataFrame.diff()
        df = DataFrame(
            {
                "a": np.repeat([0, 1, np.nan, 2], 2),
                "b": np.tile([0, 1, np.nan, 2], 2),
                "c": np.repeat(np.nan, 8),
                "d": np.arange(1, 9) ** 2,
            },
            dtype="Int64",
        )

        # Test case for default behaviour of diff
        result = df.diff(axis=axis)
        tm.assert_frame_equal(result, expected)

    def test_diff_readonly(self):
        # https://github.com/pandas-dev/pandas/issues/35559
        arr = np.random.randn(5, 2)
        arr.flags.writeable = False
        df = DataFrame(arr)
        result = df.diff()
        expected = DataFrame(np.array(df)).diff()
        tm.assert_frame_equal(result, expected)

    def test_diff_all_int_dtype(self, any_int_numpy_dtype):
        # GH 14773
        df = DataFrame(range(5))
        df = df.astype(any_int_numpy_dtype)
        result = df.diff()
        expected_dtype = (
            "float32" if any_int_numpy_dtype in ("int8", "int16") else "float64"
        )
        expected = DataFrame([np.nan, 1.0, 1.0, 1.0, 1.0], dtype=expected_dtype)
        tm.assert_frame_equal(result, expected)