"""
test all other .agg behavior
"""

from collections import OrderedDict
import datetime as dt
from functools import partial

import numpy as np
import pytest

import pandas as pd
from pandas import (
    DataFrame,
    Index,
    MultiIndex,
    PeriodIndex,
    Series,
    date_range,
    period_range,
)
from pandas.core.groupby.groupby import SpecificationError
import pandas.util.testing as tm

from pandas.io.formats.printing import pprint_thing


def test_agg_api():
    # GH 6337
    # http://stackoverflow.com/questions/21706030/pandas-groupby-agg-function-column-dtype-error
    # different api for agg when passed custom function with mixed frame

    df = DataFrame(
        {
            "data1": np.random.randn(5),
            "data2": np.random.randn(5),
            "key1": ["a", "a", "b", "b", "a"],
            "key2": ["one", "two", "one", "two", "one"],
        }
    )
    grouped = df.groupby("key1")

    def peak_to_peak(arr):
        return arr.max() - arr.min()

    expected = grouped.agg([peak_to_peak])
    expected.columns = ["data1", "data2"]
    result = grouped.agg(peak_to_peak)
    tm.assert_frame_equal(result, expected)


def test_agg_datetimes_mixed():
    data = [[1, "2012-01-01", 1.0], [2, "2012-01-02", 2.0], [3, None, 3.0]]

    df1 = DataFrame(
        {
            "key": [x[0] for x in data],
            "date": [x[1] for x in data],
            "value": [x[2] for x in data],
        }
    )

    data = [
        [
            row[0],
            (dt.datetime.strptime(row[1], "%Y-%m-%d").date() if row[1] else None),
            row[2],
        ]
        for row in data
    ]

    df2 = DataFrame(
        {
            "key": [x[0] for x in data],
            "date": [x[1] for x in data],
            "value": [x[2] for x in data],
        }
    )

    df1["weights"] = df1["value"] / df1["value"].sum()
    gb1 = df1.groupby("date").aggregate(np.sum)

    df2["weights"] = df1["value"] / df1["value"].sum()
    gb2 = df2.groupby("date").aggregate(np.sum)

    assert len(gb1) == len(gb2)


def test_agg_period_index():
    prng = period_range("2012-1-1", freq="M", periods=3)
    df = DataFrame(np.random.randn(3, 2), index=prng)
    rs = df.groupby(level=0).sum()
    assert isinstance(rs.index, PeriodIndex)

    # GH 3579
    index = period_range(start="1999-01", periods=5, freq="M")
    s1 = Series(np.random.rand(len(index)), index=index)
    s2 = Series(np.random.rand(len(index)), index=index)
    series = [("s1", s1), ("s2", s2)]
    df = DataFrame.from_dict(OrderedDict(series))
    grouped = df.groupby(df.index.month)
    list(grouped)


def test_agg_dict_parameter_cast_result_dtypes():
    # GH 12821

    df = DataFrame(
        {
            "class": ["A", "A", "B", "B", "C", "C", "D", "D"],
            "time": date_range("1/1/2011", periods=8, freq="H"),
        }
    )
    df.loc[[0, 1, 2, 5], "time"] = None

    # test for `first` function
    exp = df.loc[[0, 3, 4, 6]].set_index("class")
    grouped = df.groupby("class")
    tm.assert_frame_equal(grouped.first(), exp)
    tm.assert_frame_equal(grouped.agg("first"), exp)
    tm.assert_frame_equal(grouped.agg({"time": "first"}), exp)
    tm.assert_series_equal(grouped.time.first(), exp["time"])
    tm.assert_series_equal(grouped.time.agg("first"), exp["time"])

    # test for `last` function
    exp = df.loc[[0, 3, 4, 7]].set_index("class")
    grouped = df.groupby("class")
    tm.assert_frame_equal(grouped.last(), exp)
    tm.assert_frame_equal(grouped.agg("last"), exp)
    tm.assert_frame_equal(grouped.agg({"time": "last"}), exp)
    tm.assert_series_equal(grouped.time.last(), exp["time"])
    tm.assert_series_equal(grouped.time.agg("last"), exp["time"])

    # count
    exp = pd.Series([2, 2, 2, 2], index=Index(list("ABCD"), name="class"), name="time")
    tm.assert_series_equal(grouped.time.agg(len), exp)
    tm.assert_series_equal(grouped.time.size(), exp)

    exp = pd.Series([0, 1, 1, 2], index=Index(list("ABCD"), name="class"), name="time")
    tm.assert_series_equal(grouped.time.count(), exp)


def test_agg_cast_results_dtypes():
    # similar to GH12821
    # xref #11444
    u = [dt.datetime(2015, x + 1, 1) for x in range(12)]
    v = list("aaabbbbbbccd")
    df = pd.DataFrame({"X": v, "Y": u})

    result = df.groupby("X")["Y"].agg(len)
    expected = df.groupby("X")["Y"].count()
    tm.assert_series_equal(result, expected)


def test_aggregate_float64_no_int64():
    # see gh-11199
    df = DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 2, 2, 4, 5], "c": [1, 2, 3, 4, 5]})

    expected = DataFrame({"a": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
    expected.index.name = "b"

    result = df.groupby("b")[["a"]].mean()
    tm.assert_frame_equal(result, expected)

    expected = DataFrame({"a": [1, 2.5, 4, 5], "c": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
    expected.index.name = "b"

    result = df.groupby("b")[["a", "c"]].mean()
    tm.assert_frame_equal(result, expected)


def test_aggregate_api_consistency():
    # GH 9052
    # make sure that the aggregates via dict
    # are consistent
    df = DataFrame(
        {
            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
            "C": np.random.randn(8) + 1.0,
            "D": np.arange(8),
        }
    )

    grouped = df.groupby(["A", "B"])
    c_mean = grouped["C"].mean()
    c_sum = grouped["C"].sum()
    d_mean = grouped["D"].mean()
    d_sum = grouped["D"].sum()

    result = grouped["D"].agg(["sum", "mean"])
    expected = pd.concat([d_sum, d_mean], axis=1)
    expected.columns = ["sum", "mean"]
    tm.assert_frame_equal(result, expected, check_like=True)

    result = grouped.agg([np.sum, np.mean])
    expected = pd.concat([c_sum, c_mean, d_sum, d_mean], axis=1)
    expected.columns = MultiIndex.from_product([["C", "D"], ["sum", "mean"]])
    tm.assert_frame_equal(result, expected, check_like=True)

    result = grouped[["D", "C"]].agg([np.sum, np.mean])
    expected = pd.concat([d_sum, d_mean, c_sum, c_mean], axis=1)
    expected.columns = MultiIndex.from_product([["D", "C"], ["sum", "mean"]])
    tm.assert_frame_equal(result, expected, check_like=True)

    result = grouped.agg({"C": "mean", "D": "sum"})
    expected = pd.concat([d_sum, c_mean], axis=1)
    tm.assert_frame_equal(result, expected, check_like=True)

    result = grouped.agg({"C": ["mean", "sum"], "D": ["mean", "sum"]})
    expected = pd.concat([c_mean, c_sum, d_mean, d_sum], axis=1)
    expected.columns = MultiIndex.from_product([["C", "D"], ["mean", "sum"]])

    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        result = grouped[["D", "C"]].agg({"r": np.sum, "r2": np.mean})
    expected = pd.concat([d_sum, c_sum, d_mean, c_mean], axis=1)
    expected.columns = MultiIndex.from_product([["r", "r2"], ["D", "C"]])
    tm.assert_frame_equal(result, expected, check_like=True)


def test_agg_dict_renaming_deprecation():
    # 15931
    df = pd.DataFrame({"A": [1, 1, 1, 2, 2], "B": range(5), "C": range(5)})

    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False) as w:
        df.groupby("A").agg(
            {"B": {"foo": ["sum", "max"]}, "C": {"bar": ["count", "min"]}}
        )
        assert "using a dict with renaming" in str(w[0].message)
        assert "named aggregation" in str(w[0].message)

    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        df.groupby("A")[["B", "C"]].agg({"ma": "max"})

    with tm.assert_produces_warning(FutureWarning) as w:
        df.groupby("A").B.agg({"foo": "count"})
        assert "using a dict on a Series for aggregation" in str(w[0].message)
        assert "named aggregation instead." in str(w[0].message)


def test_agg_compat():
    # GH 12334
    df = DataFrame(
        {
            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
            "C": np.random.randn(8) + 1.0,
            "D": np.arange(8),
        }
    )

    g = df.groupby(["A", "B"])

    expected = pd.concat([g["D"].sum(), g["D"].std()], axis=1)
    expected.columns = MultiIndex.from_tuples([("C", "sum"), ("C", "std")])
    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        result = g["D"].agg({"C": ["sum", "std"]})
    tm.assert_frame_equal(result, expected, check_like=True)

    expected = pd.concat([g["D"].sum(), g["D"].std()], axis=1)
    expected.columns = ["C", "D"]

    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        result = g["D"].agg({"C": "sum", "D": "std"})
    tm.assert_frame_equal(result, expected, check_like=True)


def test_agg_nested_dicts():
    # API change for disallowing these types of nested dicts
    df = DataFrame(
        {
            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
            "C": np.random.randn(8) + 1.0,
            "D": np.arange(8),
        }
    )

    g = df.groupby(["A", "B"])

    msg = r"cannot perform renaming for r[1-2] with a nested dictionary"
    with pytest.raises(SpecificationError, match=msg):
        g.aggregate({"r1": {"C": ["mean", "sum"]}, "r2": {"D": ["mean", "sum"]}})

    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        result = g.agg({"C": {"ra": ["mean", "std"]}, "D": {"rb": ["mean", "std"]}})
    expected = pd.concat(
        [g["C"].mean(), g["C"].std(), g["D"].mean(), g["D"].std()], axis=1
    )
    expected.columns = pd.MultiIndex.from_tuples(
        [("ra", "mean"), ("ra", "std"), ("rb", "mean"), ("rb", "std")]
    )
    tm.assert_frame_equal(result, expected, check_like=True)

    # same name as the original column
    # GH9052
    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        expected = g["D"].agg({"result1": np.sum, "result2": np.mean})
    expected = expected.rename(columns={"result1": "D"})

    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        result = g["D"].agg({"D": np.sum, "result2": np.mean})
    tm.assert_frame_equal(result, expected, check_like=True)


def test_agg_item_by_item_raise_typeerror():
    df = DataFrame(np.random.randint(10, size=(20, 10)))

    def raiseException(df):
        pprint_thing("----------------------------------------")
        pprint_thing(df.to_string())
        raise TypeError("test")

    with pytest.raises(TypeError, match="test"):
        df.groupby(0).agg(raiseException)


def test_series_agg_multikey():
    ts = tm.makeTimeSeries()
    grouped = ts.groupby([lambda x: x.year, lambda x: x.month])

    result = grouped.agg(np.sum)
    expected = grouped.sum()
    tm.assert_series_equal(result, expected)


def test_series_agg_multi_pure_python():
    data = DataFrame(
        {
            "A": [
                "foo",
                "foo",
                "foo",
                "foo",
                "bar",
                "bar",
                "bar",
                "bar",
                "foo",
                "foo",
                "foo",
            ],
            "B": [
                "one",
                "one",
                "one",
                "two",
                "one",
                "one",
                "one",
                "two",
                "two",
                "two",
                "one",
            ],
            "C": [
                "dull",
                "dull",
                "shiny",
                "dull",
                "dull",
                "shiny",
                "shiny",
                "dull",
                "shiny",
                "shiny",
                "shiny",
            ],
            "D": np.random.randn(11),
            "E": np.random.randn(11),
            "F": np.random.randn(11),
        }
    )

    def bad(x):
        assert len(x.values.base) > 0
        return "foo"

    result = data.groupby(["A", "B"]).agg(bad)
    expected = data.groupby(["A", "B"]).agg(lambda x: "foo")
    tm.assert_frame_equal(result, expected)


def test_agg_consistency():
    # agg with ([]) and () not consistent
    # GH 6715
    def P1(a):
        try:
            return np.percentile(a.dropna(), q=1)
        except Exception:
            return np.nan

    df = DataFrame(
        {
            "col1": [1, 2, 3, 4],
            "col2": [10, 25, 26, 31],
            "date": [
                dt.date(2013, 2, 10),
                dt.date(2013, 2, 10),
                dt.date(2013, 2, 11),
                dt.date(2013, 2, 11),
            ],
        }
    )

    g = df.groupby("date")

    expected = g.agg([P1])
    expected.columns = expected.columns.levels[0]

    result = g.agg(P1)
    tm.assert_frame_equal(result, expected)


def test_agg_callables():
    # GH 7929
    df = DataFrame({"foo": [1, 2], "bar": [3, 4]}).astype(np.int64)

    class fn_class:
        def __call__(self, x):
            return sum(x)

    equiv_callables = [
        sum,
        np.sum,
        lambda x: sum(x),
        lambda x: x.sum(),
        partial(sum),
        fn_class(),
    ]

    expected = df.groupby("foo").agg(sum)
    for ecall in equiv_callables:
        result = df.groupby("foo").agg(ecall)
        tm.assert_frame_equal(result, expected)


def test_agg_over_numpy_arrays():
    # GH 3788
    df = pd.DataFrame(
        [
            [1, np.array([10, 20, 30])],
            [1, np.array([40, 50, 60])],
            [2, np.array([20, 30, 40])],
        ],
        columns=["category", "arraydata"],
    )
    result = df.groupby("category").agg(sum)

    expected_data = [[np.array([50, 70, 90])], [np.array([20, 30, 40])]]
    expected_index = pd.Index([1, 2], name="category")
    expected_column = ["arraydata"]
    expected = pd.DataFrame(
        expected_data, index=expected_index, columns=expected_column
    )

    tm.assert_frame_equal(result, expected)


def test_agg_timezone_round_trip():
    # GH 15426
    ts = pd.Timestamp("2016-01-01 12:00:00", tz="US/Pacific")
    df = pd.DataFrame(
        {"a": 1, "b": [ts + dt.timedelta(minutes=nn) for nn in range(10)]}
    )

    result1 = df.groupby("a")["b"].agg(np.min).iloc[0]
    result2 = df.groupby("a")["b"].agg(lambda x: np.min(x)).iloc[0]
    result3 = df.groupby("a")["b"].min().iloc[0]

    assert result1 == ts
    assert result2 == ts
    assert result3 == ts

    dates = [
        pd.Timestamp("2016-01-0%d 12:00:00" % i, tz="US/Pacific") for i in range(1, 5)
    ]
    df = pd.DataFrame({"A": ["a", "b"] * 2, "B": dates})
    grouped = df.groupby("A")

    ts = df["B"].iloc[0]
    assert ts == grouped.nth(0)["B"].iloc[0]
    assert ts == grouped.head(1)["B"].iloc[0]
    assert ts == grouped.first()["B"].iloc[0]

    # GH#27110 applying iloc should return a DataFrame
    assert ts == grouped.apply(lambda x: x.iloc[0]).iloc[0, 0]

    ts = df["B"].iloc[2]
    assert ts == grouped.last()["B"].iloc[0]

    # GH#27110 applying iloc should return a DataFrame
    assert ts == grouped.apply(lambda x: x.iloc[-1]).iloc[0, 0]


def test_sum_uint64_overflow():
    # see gh-14758
    # Convert to uint64 and don't overflow
    df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], dtype=object)
    df = df + 9223372036854775807

    index = pd.Index(
        [9223372036854775808, 9223372036854775810, 9223372036854775812], dtype=np.uint64
    )
    expected = pd.DataFrame(
        {1: [9223372036854775809, 9223372036854775811, 9223372036854775813]},
        index=index,
    )

    expected.index.name = 0
    result = df.groupby(0).sum()
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "structure, expected",
    [
        (tuple, pd.DataFrame({"C": {(1, 1): (1, 1, 1), (3, 4): (3, 4, 4)}})),
        (list, pd.DataFrame({"C": {(1, 1): [1, 1, 1], (3, 4): [3, 4, 4]}})),
        (
            lambda x: tuple(x),
            pd.DataFrame({"C": {(1, 1): (1, 1, 1), (3, 4): (3, 4, 4)}}),
        ),
        (
            lambda x: list(x),
            pd.DataFrame({"C": {(1, 1): [1, 1, 1], (3, 4): [3, 4, 4]}}),
        ),
    ],
)
def test_agg_structs_dataframe(structure, expected):
    df = pd.DataFrame(
        {"A": [1, 1, 1, 3, 3, 3], "B": [1, 1, 1, 4, 4, 4], "C": [1, 1, 1, 3, 4, 4]}
    )

    result = df.groupby(["A", "B"]).aggregate(structure)
    expected.index.names = ["A", "B"]
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "structure, expected",
    [
        (tuple, pd.Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name="C")),
        (list, pd.Series([[1, 1, 1], [3, 4, 4]], index=[1, 3], name="C")),
        (lambda x: tuple(x), pd.Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name="C")),
        (lambda x: list(x), pd.Series([[1, 1, 1], [3, 4, 4]], index=[1, 3], name="C")),
    ],
)
def test_agg_structs_series(structure, expected):
    # Issue #18079
    df = pd.DataFrame(
        {"A": [1, 1, 1, 3, 3, 3], "B": [1, 1, 1, 4, 4, 4], "C": [1, 1, 1, 3, 4, 4]}
    )

    result = df.groupby("A")["C"].aggregate(structure)
    expected.index.name = "A"
    tm.assert_series_equal(result, expected)


def test_agg_category_nansum(observed):
    categories = ["a", "b", "c"]
    df = pd.DataFrame(
        {"A": pd.Categorical(["a", "a", "b"], categories=categories), "B": [1, 2, 3]}
    )
    result = df.groupby("A", observed=observed).B.agg(np.nansum)
    expected = pd.Series(
        [3, 3, 0],
        index=pd.CategoricalIndex(["a", "b", "c"], categories=categories, name="A"),
        name="B",
    )
    if observed:
        expected = expected[expected != 0]
    tm.assert_series_equal(result, expected)


def test_agg_list_like_func():
    # GH 18473
    df = pd.DataFrame(
        {"A": [str(x) for x in range(3)], "B": [str(x) for x in range(3)]}
    )
    grouped = df.groupby("A", as_index=False, sort=False)
    result = grouped.agg({"B": lambda x: list(x)})
    expected = pd.DataFrame(
        {"A": [str(x) for x in range(3)], "B": [[str(x)] for x in range(3)]}
    )
    tm.assert_frame_equal(result, expected)


def test_agg_lambda_with_timezone():
    # GH 23683
    df = pd.DataFrame(
        {
            "tag": [1, 1],
            "date": [
                pd.Timestamp("2018-01-01", tz="UTC"),
                pd.Timestamp("2018-01-02", tz="UTC"),
            ],
        }
    )
    result = df.groupby("tag").agg({"date": lambda e: e.head(1)})
    expected = pd.DataFrame(
        [pd.Timestamp("2018-01-01", tz="UTC")],
        index=pd.Index([1], name="tag"),
        columns=["date"],
    )
    tm.assert_frame_equal(result, expected)

numpy.min numpy.random.randint numpy.arange numpy.array numpy.random.randn