import numpy as np

import pandas as pd
from pandas import (
    DataFrame,
    Index,
)
import pandas._testing as tm
from pandas.core.api import Int64Index


def test_pipe():
    # Test the pipe method of DataFrameGroupBy.
    # Issue #17871

    random_state = np.random.RandomState(1234567890)

    df = DataFrame(
        {
            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
            "B": random_state.randn(8),
            "C": random_state.randn(8),
        }
    )

    def f(dfgb):
        return dfgb.B.max() - dfgb.C.min().min()

    def square(srs):
        return srs**2

    # Note that the transformations are
    # GroupBy -> Series
    # Series -> Series
    # This then chains the GroupBy.pipe and the
    # NDFrame.pipe methods
    result = df.groupby("A").pipe(f).pipe(square)

    index = Index(["bar", "foo"], dtype="object", name="A")
    expected = pd.Series([8.99110003361, 8.17516964785], name="B", index=index)

    tm.assert_series_equal(expected, result)


def test_pipe_args():
    # Test passing args to the pipe method of DataFrameGroupBy.
    # Issue #17871

    df = DataFrame(
        {
            "group": ["A", "A", "B", "B", "C"],
            "x": [1.0, 2.0, 3.0, 2.0, 5.0],
            "y": [10.0, 100.0, 1000.0, -100.0, -1000.0],
        }
    )

    def f(dfgb, arg1):
        return dfgb.filter(lambda grp: grp.y.mean() > arg1, dropna=False).groupby(
            dfgb.grouper
        )

    def g(dfgb, arg2):
        msg = "The default value of numeric_only"
        with tm.assert_produces_warning(FutureWarning, match=msg):
            return dfgb.sum() / dfgb.sum().sum() + arg2

    def h(df, arg3):
        return df.x + df.y - arg3

    result = df.groupby("group").pipe(f, 0).pipe(g, 10).pipe(h, 100)

    # Assert the results here
    index = Index(["A", "B", "C"], name="group")
    expected = pd.Series([-79.5160891089, -78.4839108911, -80], index=index)

    tm.assert_series_equal(expected, result)

    # test SeriesGroupby.pipe
    ser = pd.Series([1, 1, 2, 2, 3, 3])
    result = ser.groupby(ser).pipe(lambda grp: grp.sum() * grp.count())

    expected = pd.Series([4, 8, 12], index=Int64Index([1, 2, 3]))

    tm.assert_series_equal(result, expected)
