from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings

import numpy as np
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import (
    Categorical,
    DataFrame,
    MultiIndex,
    Series,
    Timestamp,
    date_range,
    isna,
    notna,
    to_datetime,
    to_timedelta,
)
import pandas.core.algorithms as algorithms
import pandas.core.nanops as nanops
import pandas.util.testing as tm


def assert_stat_op_calc(
    opname,
    alternative,
    frame,
    has_skipna=True,
    check_dtype=True,
    check_dates=False,
    check_less_precise=False,
    skipna_alternative=None,
):
    """
    Check that operator opname works as advertised on frame

    Parameters
    ----------
    opname : string
        Name of the operator to test on frame
    alternative : function
        Function that opname is tested against; i.e. "frame.opname()" should
        equal "alternative(frame)".
    frame : DataFrame
        The object that the tests are executed on
    has_skipna : bool, default True
        Whether the method "opname" has the kwarg "skip_na"
    check_dtype : bool, default True
        Whether the dtypes of the result of "frame.opname()" and
        "alternative(frame)" should be checked.
    check_dates : bool, default false
        Whether opname should be tested on a Datetime Series
    check_less_precise : bool, default False
        Whether results should only be compared approximately;
        passed on to tm.assert_series_equal
    skipna_alternative : function, default None
        NaN-safe version of alternative
    """

    f = getattr(frame, opname)

    if check_dates:
        df = DataFrame({"b": date_range("1/1/2001", periods=2)})
        result = getattr(df, opname)()
        assert isinstance(result, Series)

        df["a"] = range(len(df))
        result = getattr(df, opname)()
        assert isinstance(result, Series)
        assert len(result)

    if has_skipna:

        def wrapper(x):
            return alternative(x.values)

        skipna_wrapper = tm._make_skipna_wrapper(alternative, skipna_alternative)
        result0 = f(axis=0, skipna=False)
        result1 = f(axis=1, skipna=False)
        tm.assert_series_equal(
            result0,
            frame.apply(wrapper),
            check_dtype=check_dtype,
            check_less_precise=check_less_precise,
        )
        # HACK: win32
        tm.assert_series_equal(
            result1,
            frame.apply(wrapper, axis=1),
            check_dtype=False,
            check_less_precise=check_less_precise,
        )
    else:
        skipna_wrapper = alternative

    result0 = f(axis=0)
    result1 = f(axis=1)
    tm.assert_series_equal(
        result0,
        frame.apply(skipna_wrapper),
        check_dtype=check_dtype,
        check_less_precise=check_less_precise,
    )

    if opname in ["sum", "prod"]:
        expected = frame.apply(skipna_wrapper, axis=1)
        tm.assert_series_equal(
            result1, expected, check_dtype=False, check_less_precise=check_less_precise
        )

    # check dtypes
    if check_dtype:
        lcd_dtype = frame.values.dtype
        assert lcd_dtype == result0.dtype
        assert lcd_dtype == result1.dtype

    # bad axis
    with pytest.raises(ValueError, match="No axis named 2"):
        f(axis=2)

    # all NA case
    if has_skipna:
        all_na = frame * np.NaN
        r0 = getattr(all_na, opname)(axis=0)
        r1 = getattr(all_na, opname)(axis=1)
        if opname in ["sum", "prod"]:
            unit = 1 if opname == "prod" else 0  # result for empty sum/prod
            expected = pd.Series(unit, index=r0.index, dtype=r0.dtype)
            tm.assert_series_equal(r0, expected)
            expected = pd.Series(unit, index=r1.index, dtype=r1.dtype)
            tm.assert_series_equal(r1, expected)


def assert_stat_op_api(opname, float_frame, float_string_frame, has_numeric_only=False):
    """
    Check that API for operator opname works as advertised on frame

    Parameters
    ----------
    opname : string
        Name of the operator to test on frame
    float_frame : DataFrame
        DataFrame with columns of type float
    float_string_frame : DataFrame
        DataFrame with both float and string columns
    has_numeric_only : bool, default False
        Whether the method "opname" has the kwarg "numeric_only"
    """

    # make sure works on mixed-type frame
    getattr(float_string_frame, opname)(axis=0)
    getattr(float_string_frame, opname)(axis=1)

    if has_numeric_only:
        getattr(float_string_frame, opname)(axis=0, numeric_only=True)
        getattr(float_string_frame, opname)(axis=1, numeric_only=True)
        getattr(float_frame, opname)(axis=0, numeric_only=False)
        getattr(float_frame, opname)(axis=1, numeric_only=False)


def assert_bool_op_calc(opname, alternative, frame, has_skipna=True):
    """
    Check that bool operator opname works as advertised on frame

    Parameters
    ----------
    opname : string
        Name of the operator to test on frame
    alternative : function
        Function that opname is tested against; i.e. "frame.opname()" should
        equal "alternative(frame)".
    frame : DataFrame
        The object that the tests are executed on
    has_skipna : bool, default True
        Whether the method "opname" has the kwarg "skip_na"
    """

    f = getattr(frame, opname)

    if has_skipna:

        def skipna_wrapper(x):
            nona = x.dropna().values
            return alternative(nona)

        def wrapper(x):
            return alternative(x.values)

        result0 = f(axis=0, skipna=False)
        result1 = f(axis=1, skipna=False)

        tm.assert_series_equal(result0, frame.apply(wrapper))
        tm.assert_series_equal(
            result1, frame.apply(wrapper, axis=1), check_dtype=False
        )  # HACK: win32
    else:
        skipna_wrapper = alternative
        wrapper = alternative

    result0 = f(axis=0)
    result1 = f(axis=1)

    tm.assert_series_equal(result0, frame.apply(skipna_wrapper))
    tm.assert_series_equal(
        result1, frame.apply(skipna_wrapper, axis=1), check_dtype=False
    )

    # bad axis
    with pytest.raises(ValueError, match="No axis named 2"):
        f(axis=2)

    # all NA case
    if has_skipna:
        all_na = frame * np.NaN
        r0 = getattr(all_na, opname)(axis=0)
        r1 = getattr(all_na, opname)(axis=1)
        if opname == "any":
            assert not r0.any()
            assert not r1.any()
        else:
            assert r0.all()
            assert r1.all()


def assert_bool_op_api(
    opname, bool_frame_with_na, float_string_frame, has_bool_only=False
):
    """
    Check that API for boolean operator opname works as advertised on frame

    Parameters
    ----------
    opname : string
        Name of the operator to test on frame
    float_frame : DataFrame
        DataFrame with columns of type float
    float_string_frame : DataFrame
        DataFrame with both float and string columns
    has_bool_only : bool, default False
        Whether the method "opname" has the kwarg "bool_only"
    """
    # make sure op works on mixed-type frame
    mixed = float_string_frame
    mixed["_bool_"] = np.random.randn(len(mixed)) > 0.5
    getattr(mixed, opname)(axis=0)
    getattr(mixed, opname)(axis=1)

    if has_bool_only:
        getattr(mixed, opname)(axis=0, bool_only=True)
        getattr(mixed, opname)(axis=1, bool_only=True)
        getattr(bool_frame_with_na, opname)(axis=0, bool_only=False)
        getattr(bool_frame_with_na, opname)(axis=1, bool_only=False)


class TestDataFrameAnalytics:

    # ---------------------------------------------------------------------
    # Correlation and covariance

    @td.skip_if_no_scipy
    def test_corr_pearson(self, float_frame):
        float_frame["A"][:5] = np.nan
        float_frame["B"][5:10] = np.nan

        self._check_method(float_frame, "pearson")

    @td.skip_if_no_scipy
    def test_corr_kendall(self, float_frame):
        float_frame["A"][:5] = np.nan
        float_frame["B"][5:10] = np.nan

        self._check_method(float_frame, "kendall")

    @td.skip_if_no_scipy
    def test_corr_spearman(self, float_frame):
        float_frame["A"][:5] = np.nan
        float_frame["B"][5:10] = np.nan

        self._check_method(float_frame, "spearman")

    def _check_method(self, frame, method="pearson"):
        correls = frame.corr(method=method)
        expected = frame["A"].corr(frame["C"], method=method)
        tm.assert_almost_equal(correls["A"]["C"], expected)

    @td.skip_if_no_scipy
    def test_corr_non_numeric(self, float_frame, float_string_frame):
        float_frame["A"][:5] = np.nan
        float_frame["B"][5:10] = np.nan

        # exclude non-numeric types
        result = float_string_frame.corr()
        expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].corr()
        tm.assert_frame_equal(result, expected)

    @td.skip_if_no_scipy
    @pytest.mark.parametrize("meth", ["pearson", "kendall", "spearman"])
    def test_corr_nooverlap(self, meth):
        # nothing in common
        df = DataFrame(
            {
                "A": [1, 1.5, 1, np.nan, np.nan, np.nan],
                "B": [np.nan, np.nan, np.nan, 1, 1.5, 1],
                "C": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
            }
        )
        rs = df.corr(meth)
        assert isna(rs.loc["A", "B"])
        assert isna(rs.loc["B", "A"])
        assert rs.loc["A", "A"] == 1
        assert rs.loc["B", "B"] == 1
        assert isna(rs.loc["C", "C"])

    @td.skip_if_no_scipy
    @pytest.mark.parametrize("meth", ["pearson", "spearman"])
    def test_corr_constant(self, meth):
        # constant --> all NA

        df = DataFrame(
            {
                "A": [1, 1, 1, np.nan, np.nan, np.nan],
                "B": [np.nan, np.nan, np.nan, 1, 1, 1],
            }
        )
        rs = df.corr(meth)
        assert isna(rs.values).all()

    def test_corr_int(self):
        # dtypes other than float64 #1761
        df3 = DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]})

        df3.cov()
        df3.corr()

    @td.skip_if_no_scipy
    def test_corr_int_and_boolean(self):
        # when dtypes of pandas series are different
        # then ndarray will have dtype=object,
        # so it need to be properly handled
        df = DataFrame({"a": [True, False], "b": [1, 0]})

        expected = DataFrame(np.ones((2, 2)), index=["a", "b"], columns=["a", "b"])
        for meth in ["pearson", "kendall", "spearman"]:

            with warnings.catch_warnings(record=True):
                warnings.simplefilter("ignore", RuntimeWarning)
                result = df.corr(meth)
            tm.assert_frame_equal(result, expected)

    def test_corr_cov_independent_index_column(self):
        # GH 14617
        df = pd.DataFrame(np.random.randn(4 * 10).reshape(10, 4), columns=list("abcd"))
        for method in ["cov", "corr"]:
            result = getattr(df, method)()
            assert result.index is not result.columns
            assert result.index.equals(result.columns)

    def test_corr_invalid_method(self):
        # GH 22298
        df = pd.DataFrame(np.random.normal(size=(10, 2)))
        msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, "
        with pytest.raises(ValueError, match=msg):
            df.corr(method="____")

    def test_cov(self, float_frame, float_string_frame):
        # min_periods no NAs (corner case)
        expected = float_frame.cov()
        result = float_frame.cov(min_periods=len(float_frame))

        tm.assert_frame_equal(expected, result)

        result = float_frame.cov(min_periods=len(float_frame) + 1)
        assert isna(result.values).all()

        # with NAs
        frame = float_frame.copy()
        frame["A"][:5] = np.nan
        frame["B"][5:10] = np.nan
        result = float_frame.cov(min_periods=len(float_frame) - 8)
        expected = float_frame.cov()
        expected.loc["A", "B"] = np.nan
        expected.loc["B", "A"] = np.nan

        # regular
        float_frame["A"][:5] = np.nan
        float_frame["B"][:10] = np.nan
        cov = float_frame.cov()

        tm.assert_almost_equal(cov["A"]["C"], float_frame["A"].cov(float_frame["C"]))

        # exclude non-numeric types
        result = float_string_frame.cov()
        expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].cov()
        tm.assert_frame_equal(result, expected)

        # Single column frame
        df = DataFrame(np.linspace(0.0, 1.0, 10))
        result = df.cov()
        expected = DataFrame(
            np.cov(df.values.T).reshape((1, 1)), index=df.columns, columns=df.columns
        )
        tm.assert_frame_equal(result, expected)
        df.loc[0] = np.nan
        result = df.cov()
        expected = DataFrame(
            np.cov(df.values[1:].T).reshape((1, 1)),
            index=df.columns,
            columns=df.columns,
        )
        tm.assert_frame_equal(result, expected)

    def test_corrwith(self, datetime_frame):
        a = datetime_frame
        noise = Series(np.random.randn(len(a)), index=a.index)

        b = datetime_frame.add(noise, axis=0)

        # make sure order does not matter
        b = b.reindex(columns=b.columns[::-1], index=b.index[::-1][10:])
        del b["B"]

        colcorr = a.corrwith(b, axis=0)
        tm.assert_almost_equal(colcorr["A"], a["A"].corr(b["A"]))

        rowcorr = a.corrwith(b, axis=1)
        tm.assert_series_equal(rowcorr, a.T.corrwith(b.T, axis=0))

        dropped = a.corrwith(b, axis=0, drop=True)
        tm.assert_almost_equal(dropped["A"], a["A"].corr(b["A"]))
        assert "B" not in dropped

        dropped = a.corrwith(b, axis=1, drop=True)
        assert a.index[-1] not in dropped.index

        # non time-series data
        index = ["a", "b", "c", "d", "e"]
        columns = ["one", "two", "three", "four"]
        df1 = DataFrame(np.random.randn(5, 4), index=index, columns=columns)
        df2 = DataFrame(np.random.randn(4, 4), index=index[:4], columns=columns)
        correls = df1.corrwith(df2, axis=1)
        for row in index[:4]:
            tm.assert_almost_equal(correls[row], df1.loc[row].corr(df2.loc[row]))

    def test_corrwith_with_objects(self):
        df1 = tm.makeTimeDataFrame()
        df2 = tm.makeTimeDataFrame()
        cols = ["A", "B", "C", "D"]

        df1["obj"] = "foo"
        df2["obj"] = "bar"

        result = df1.corrwith(df2)
        expected = df1.loc[:, cols].corrwith(df2.loc[:, cols])
        tm.assert_series_equal(result, expected)

        result = df1.corrwith(df2, axis=1)
        expected = df1.loc[:, cols].corrwith(df2.loc[:, cols], axis=1)
        tm.assert_series_equal(result, expected)

    def test_corrwith_series(self, datetime_frame):
        result = datetime_frame.corrwith(datetime_frame["A"])
        expected = datetime_frame.apply(datetime_frame["A"].corr)

        tm.assert_series_equal(result, expected)

    def test_corrwith_matches_corrcoef(self):
        df1 = DataFrame(np.arange(10000), columns=["a"])
        df2 = DataFrame(np.arange(10000) ** 2, columns=["a"])
        c1 = df1.corrwith(df2)["a"]
        c2 = np.corrcoef(df1["a"], df2["a"])[0][1]

        tm.assert_almost_equal(c1, c2)
        assert c1 < 1

    def test_corrwith_mixed_dtypes(self):
        # GH 18570
        df = pd.DataFrame(
            {"a": [1, 4, 3, 2], "b": [4, 6, 7, 3], "c": ["a", "b", "c", "d"]}
        )
        s = pd.Series([0, 6, 7, 3])
        result = df.corrwith(s)
        corrs = [df["a"].corr(s), df["b"].corr(s)]
        expected = pd.Series(data=corrs, index=["a", "b"])
        tm.assert_series_equal(result, expected)

    def test_corrwith_index_intersection(self):
        df1 = pd.DataFrame(np.random.random(size=(10, 2)), columns=["a", "b"])
        df2 = pd.DataFrame(np.random.random(size=(10, 3)), columns=["a", "b", "c"])

        result = df1.corrwith(df2, drop=True).index.sort_values()
        expected = df1.columns.intersection(df2.columns).sort_values()
        tm.assert_index_equal(result, expected)

    def test_corrwith_index_union(self):
        df1 = pd.DataFrame(np.random.random(size=(10, 2)), columns=["a", "b"])
        df2 = pd.DataFrame(np.random.random(size=(10, 3)), columns=["a", "b", "c"])

        result = df1.corrwith(df2, drop=False).index.sort_values()
        expected = df1.columns.union(df2.columns).sort_values()
        tm.assert_index_equal(result, expected)

    def test_corrwith_dup_cols(self):
        # GH 21925
        df1 = pd.DataFrame(np.vstack([np.arange(10)] * 3).T)
        df2 = df1.copy()
        df2 = pd.concat((df2, df2[0]), axis=1)

        result = df1.corrwith(df2)
        expected = pd.Series(np.ones(4), index=[0, 0, 1, 2])
        tm.assert_series_equal(result, expected)

    @td.skip_if_no_scipy
    def test_corrwith_spearman(self):
        # GH 21925
        df = pd.DataFrame(np.random.random(size=(100, 3)))
        result = df.corrwith(df ** 2, method="spearman")
        expected = Series(np.ones(len(result)))
        tm.assert_series_equal(result, expected)

    @td.skip_if_no_scipy
    def test_corrwith_kendall(self):
        # GH 21925
        df = pd.DataFrame(np.random.random(size=(100, 3)))
        result = df.corrwith(df ** 2, method="kendall")
        expected = Series(np.ones(len(result)))
        tm.assert_series_equal(result, expected)

    # ---------------------------------------------------------------------
    # Describe

    def test_bool_describe_in_mixed_frame(self):
        df = DataFrame(
            {
                "string_data": ["a", "b", "c", "d", "e"],
                "bool_data": [True, True, False, False, False],
                "int_data": [10, 20, 30, 40, 50],
            }
        )

        # Integer data are included in .describe() output,
        # Boolean and string data are not.
        result = df.describe()
        expected = DataFrame(
            {"int_data": [5, 30, df.int_data.std(), 10, 20, 30, 40, 50]},
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
        )
        tm.assert_frame_equal(result, expected)

        # Top value is a boolean value that is False
        result = df.describe(include=["bool"])

        expected = DataFrame(
            {"bool_data": [5, 2, False, 3]}, index=["count", "unique", "top", "freq"]
        )
        tm.assert_frame_equal(result, expected)

    def test_describe_empty_object(self):
        # https://github.com/pandas-dev/pandas/issues/27183
        df = pd.DataFrame({"A": [None, None]}, dtype=object)
        result = df.describe()
        expected = pd.DataFrame(
            {"A": [0, 0, np.nan, np.nan]},
            dtype=object,
            index=["count", "unique", "top", "freq"],
        )
        tm.assert_frame_equal(result, expected)

        result = df.iloc[:0].describe()
        tm.assert_frame_equal(result, expected)

    def test_describe_bool_frame(self):
        # GH 13891
        df = pd.DataFrame(
            {
                "bool_data_1": [False, False, True, True],
                "bool_data_2": [False, True, True, True],
            }
        )
        result = df.describe()
        expected = DataFrame(
            {"bool_data_1": [4, 2, True, 2], "bool_data_2": [4, 2, True, 3]},
            index=["count", "unique", "top", "freq"],
        )
        tm.assert_frame_equal(result, expected)

        df = pd.DataFrame(
            {
                "bool_data": [False, False, True, True, False],
                "int_data": [0, 1, 2, 3, 4],
            }
        )
        result = df.describe()
        expected = DataFrame(
            {"int_data": [5, 2, df.int_data.std(), 0, 1, 2, 3, 4]},
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
        )
        tm.assert_frame_equal(result, expected)

        df = pd.DataFrame(
            {"bool_data": [False, False, True, True], "str_data": ["a", "b", "c", "a"]}
        )
        result = df.describe()
        expected = DataFrame(
            {"bool_data": [4, 2, True, 2], "str_data": [4, 3, "a", 2]},
            index=["count", "unique", "top", "freq"],
        )
        tm.assert_frame_equal(result, expected)

    def test_describe_categorical(self):
        df = DataFrame({"value": np.random.randint(0, 10000, 100)})
        labels = ["{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500)]
        cat_labels = Categorical(labels, labels)

        df = df.sort_values(by=["value"], ascending=True)
        df["value_group"] = pd.cut(
            df.value, range(0, 10500, 500), right=False, labels=cat_labels
        )
        cat = df

        # Categoricals should not show up together with numerical columns
        result = cat.describe()
        assert len(result.columns) == 1

        # In a frame, describe() for the cat should be the same as for string
        # arrays (count, unique, top, freq)

        cat = Categorical(
            ["a", "b", "b", "b"], categories=["a", "b", "c"], ordered=True
        )
        s = Series(cat)
        result = s.describe()
        expected = Series([4, 2, "b", 3], index=["count", "unique", "top", "freq"])
        tm.assert_series_equal(result, expected)

        cat = Series(Categorical(["a", "b", "c", "c"]))
        df3 = DataFrame({"cat": cat, "s": ["a", "b", "c", "c"]})
        result = df3.describe()
        tm.assert_numpy_array_equal(result["cat"].values, result["s"].values)

    def test_describe_empty_categorical_column(self):
        # GH 26397
        # Ensure the index of an an empty categorical DataFrame column
        # also contains (count, unique, top, freq)
        df = pd.DataFrame({"empty_col": Categorical([])})
        result = df.describe()
        expected = DataFrame(
            {"empty_col": [0, 0, np.nan, np.nan]},
            index=["count", "unique", "top", "freq"],
            dtype="object",
        )
        tm.assert_frame_equal(result, expected)
        # ensure NaN, not None
        assert np.isnan(result.iloc[2, 0])
        assert np.isnan(result.iloc[3, 0])

    def test_describe_categorical_columns(self):
        # GH 11558
        columns = pd.CategoricalIndex(["int1", "int2", "obj"], ordered=True, name="XXX")
        df = DataFrame(
            {
                "int1": [10, 20, 30, 40, 50],
                "int2": [10, 20, 30, 40, 50],
                "obj": ["A", 0, None, "X", 1],
            },
            columns=columns,
        )
        result = df.describe()

        exp_columns = pd.CategoricalIndex(
            ["int1", "int2"],
            categories=["int1", "int2", "obj"],
            ordered=True,
            name="XXX",
        )
        expected = DataFrame(
            {
                "int1": [5, 30, df.int1.std(), 10, 20, 30, 40, 50],
                "int2": [5, 30, df.int2.std(), 10, 20, 30, 40, 50],
            },
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
            columns=exp_columns,
        )

        tm.assert_frame_equal(result, expected)
        tm.assert_categorical_equal(result.columns.values, expected.columns.values)

    def test_describe_datetime_columns(self):
        columns = pd.DatetimeIndex(
            ["2011-01-01", "2011-02-01", "2011-03-01"],
            freq="MS",
            tz="US/Eastern",
            name="XXX",
        )
        df = DataFrame(
            {
                0: [10, 20, 30, 40, 50],
                1: [10, 20, 30, 40, 50],
                2: ["A", 0, None, "X", 1],
            }
        )
        df.columns = columns
        result = df.describe()

        exp_columns = pd.DatetimeIndex(
            ["2011-01-01", "2011-02-01"], freq="MS", tz="US/Eastern", name="XXX"
        )
        expected = DataFrame(
            {
                0: [5, 30, df.iloc[:, 0].std(), 10, 20, 30, 40, 50],
                1: [5, 30, df.iloc[:, 1].std(), 10, 20, 30, 40, 50],
            },
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
        )
        expected.columns = exp_columns
        tm.assert_frame_equal(result, expected)
        assert result.columns.freq == "MS"
        assert result.columns.tz == expected.columns.tz

    def test_describe_timedelta_values(self):
        # GH 6145
        t1 = pd.timedelta_range("1 days", freq="D", periods=5)
        t2 = pd.timedelta_range("1 hours", freq="H", periods=5)
        df = pd.DataFrame({"t1": t1, "t2": t2})

        expected = DataFrame(
            {
                "t1": [
                    5,
                    pd.Timedelta("3 days"),
                    df.iloc[:, 0].std(),
                    pd.Timedelta("1 days"),
                    pd.Timedelta("2 days"),
                    pd.Timedelta("3 days"),
                    pd.Timedelta("4 days"),
                    pd.Timedelta("5 days"),
                ],
                "t2": [
                    5,
                    pd.Timedelta("3 hours"),
                    df.iloc[:, 1].std(),
                    pd.Timedelta("1 hours"),
                    pd.Timedelta("2 hours"),
                    pd.Timedelta("3 hours"),
                    pd.Timedelta("4 hours"),
                    pd.Timedelta("5 hours"),
                ],
            },
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
        )

        result = df.describe()
        tm.assert_frame_equal(result, expected)

        exp_repr = (
            "                           t1                      t2\n"
            "count                       5                       5\n"
            "mean          3 days 00:00:00         0 days 03:00:00\n"
            "std    1 days 13:56:50.394919  0 days 01:34:52.099788\n"
            "min           1 days 00:00:00         0 days 01:00:00\n"
            "25%           2 days 00:00:00         0 days 02:00:00\n"
            "50%           3 days 00:00:00         0 days 03:00:00\n"
            "75%           4 days 00:00:00         0 days 04:00:00\n"
            "max           5 days 00:00:00         0 days 05:00:00"
        )
        assert repr(result) == exp_repr

    def test_describe_tz_values(self, tz_naive_fixture):
        # GH 21332
        tz = tz_naive_fixture
        s1 = Series(range(5))
        start = Timestamp(2018, 1, 1)
        end = Timestamp(2018, 1, 5)
        s2 = Series(date_range(start, end, tz=tz))
        df = pd.DataFrame({"s1": s1, "s2": s2})

        expected = DataFrame(
            {
                "s1": [
                    5,
                    np.nan,
                    np.nan,
                    np.nan,
                    np.nan,
                    np.nan,
                    2,
                    1.581139,
                    0,
                    1,
                    2,
                    3,
                    4,
                ],
                "s2": [
                    5,
                    5,
                    s2.value_counts().index[0],
                    1,
                    start.tz_localize(tz),
                    end.tz_localize(tz),
                    np.nan,
                    np.nan,
                    np.nan,
                    np.nan,
                    np.nan,
                    np.nan,
                    np.nan,
                ],
            },
            index=[
                "count",
                "unique",
                "top",
                "freq",
                "first",
                "last",
                "mean",
                "std",
                "min",
                "25%",
                "50%",
                "75%",
                "max",
            ],
        )
        result = df.describe(include="all")
        tm.assert_frame_equal(result, expected)

    def test_describe_percentiles_integer_idx(self):
        # Issue 26660
        df = pd.DataFrame({"x": [1]})
        pct = np.linspace(0, 1, 10 + 1)
        result = df.describe(percentiles=pct)

        expected = DataFrame(
            {"x": [1.0, 1.0, np.NaN, 1.0, *[1.0 for _ in pct], 1.0]},
            index=[
                "count",
                "mean",
                "std",
                "min",
                "0%",
                "10%",
                "20%",
                "30%",
                "40%",
                "50%",
                "60%",
                "70%",
                "80%",
                "90%",
                "100%",
                "max",
            ],
        )
        tm.assert_frame_equal(result, expected)

    # ---------------------------------------------------------------------
    # Reductions

    def test_stat_op_api(self, float_frame, float_string_frame):
        assert_stat_op_api(
            "count", float_frame, float_string_frame, has_numeric_only=True
        )
        assert_stat_op_api(
            "sum", float_frame, float_string_frame, has_numeric_only=True
        )

        assert_stat_op_api("nunique", float_frame, float_string_frame)
        assert_stat_op_api("mean", float_frame, float_string_frame)
        assert_stat_op_api("product", float_frame, float_string_frame)
        assert_stat_op_api("median", float_frame, float_string_frame)
        assert_stat_op_api("min", float_frame, float_string_frame)
        assert_stat_op_api("max", float_frame, float_string_frame)
        assert_stat_op_api("mad", float_frame, float_string_frame)
        assert_stat_op_api("var", float_frame, float_string_frame)
        assert_stat_op_api("std", float_frame, float_string_frame)
        assert_stat_op_api("sem", float_frame, float_string_frame)
        assert_stat_op_api("median", float_frame, float_string_frame)

        try:
            from scipy.stats import skew, kurtosis  # noqa:F401

            assert_stat_op_api("skew", float_frame, float_string_frame)
            assert_stat_op_api("kurt", float_frame, float_string_frame)
        except ImportError:
            pass

    def test_stat_op_calc(self, float_frame_with_na, mixed_float_frame):
        def count(s):
            return notna(s).sum()

        def nunique(s):
            return len(algorithms.unique1d(s.dropna()))

        def mad(x):
            return np.abs(x - x.mean()).mean()

        def var(x):
            return np.var(x, ddof=1)

        def std(x):
            return np.std(x, ddof=1)

        def sem(x):
            return np.std(x, ddof=1) / np.sqrt(len(x))

        def skewness(x):
            from scipy.stats import skew  # noqa:F811

            if len(x) < 3:
                return np.nan
            return skew(x, bias=False)

        def kurt(x):
            from scipy.stats import kurtosis  # noqa:F811

            if len(x) < 4:
                return np.nan
            return kurtosis(x, bias=False)

        assert_stat_op_calc(
            "nunique",
            nunique,
            float_frame_with_na,
            has_skipna=False,
            check_dtype=False,
            check_dates=True,
        )

        # mixed types (with upcasting happening)
        assert_stat_op_calc(
            "sum",
            np.sum,
            mixed_float_frame.astype("float32"),
            check_dtype=False,
            check_less_precise=True,
        )

        assert_stat_op_calc(
            "sum", np.sum, float_frame_with_na, skipna_alternative=np.nansum
        )
        assert_stat_op_calc("mean", np.mean, float_frame_with_na, check_dates=True)
        assert_stat_op_calc("product", np.prod, float_frame_with_na)

        assert_stat_op_calc("mad", mad, float_frame_with_na)
        assert_stat_op_calc("var", var, float_frame_with_na)
        assert_stat_op_calc("std", std, float_frame_with_na)
        assert_stat_op_calc("sem", sem, float_frame_with_na)

        assert_stat_op_calc(
            "count",
            count,
            float_frame_with_na,
            has_skipna=False,
            check_dtype=False,
            check_dates=True,
        )

        try:
            from scipy import skew, kurtosis  # noqa:F401

            assert_stat_op_calc("skew", skewness, float_frame_with_na)
            assert_stat_op_calc("kurt", kurt, float_frame_with_na)
        except ImportError:
            pass

    # TODO: Ensure warning isn't emitted in the first place
    @pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning")
    def test_median(self, float_frame_with_na, int_frame):
        def wrapper(x):
            if isna(x).any():
                return np.nan
            return np.median(x)

        assert_stat_op_calc("median", wrapper, float_frame_with_na, check_dates=True)
        assert_stat_op_calc(
            "median", wrapper, int_frame, check_dtype=False, check_dates=True
        )

    @pytest.mark.parametrize(
        "method", ["sum", "mean", "prod", "var", "std", "skew", "min", "max"]
    )
    def test_stat_operators_attempt_obj_array(self, method):
        # GH#676
        data = {
            "a": [
                -0.00049987540199591344,
                -0.0016467257772919831,
                0.00067695870775883013,
            ],
            "b": [-0, -0, 0.0],
            "c": [
                0.00031111847529610595,
                0.0014902627951905339,
                -0.00094099200035979691,
            ],
        }
        df1 = DataFrame(data, index=["foo", "bar", "baz"], dtype="O")

        df2 = DataFrame({0: [np.nan, 2], 1: [np.nan, 3], 2: [np.nan, 4]}, dtype=object)

        for df in [df1, df2]:
            assert df.values.dtype == np.object_
            result = getattr(df, method)(1)
            expected = getattr(df.astype("f8"), method)(1)

            if method in ["sum", "prod"]:
                tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("op", ["mean", "std", "var", "skew", "kurt", "sem"])
    def test_mixed_ops(self, op):
        # GH#16116
        df = DataFrame(
            {
                "int": [1, 2, 3, 4],
                "float": [1.0, 2.0, 3.0, 4.0],
                "str": ["a", "b", "c", "d"],
            }
        )

        result = getattr(df, op)()
        assert len(result) == 2

        with pd.option_context("use_bottleneck", False):
            result = getattr(df, op)()
            assert len(result) == 2

    def test_reduce_mixed_frame(self):
        # GH 6806
        df = DataFrame(
            {
                "bool_data": [True, True, False, False, False],
                "int_data": [10, 20, 30, 40, 50],
                "string_data": ["a", "b", "c", "d", "e"],
            }
        )
        df.reindex(columns=["bool_data", "int_data", "string_data"])
        test = df.sum(axis=0)
        tm.assert_numpy_array_equal(
            test.values, np.array([2, 150, "abcde"], dtype=object)
        )
        tm.assert_series_equal(test, df.T.sum(axis=1))

    def test_nunique(self):
        df = DataFrame({"A": [1, 1, 1], "B": [1, 2, 3], "C": [1, np.nan, 3]})
        tm.assert_series_equal(df.nunique(), Series({"A": 1, "B": 3, "C": 2}))
        tm.assert_series_equal(
            df.nunique(dropna=False), Series({"A": 1, "B": 3, "C": 3})
        )
        tm.assert_series_equal(df.nunique(axis=1), Series({0: 1, 1: 2, 2: 2}))
        tm.assert_series_equal(
            df.nunique(axis=1, dropna=False), Series({0: 1, 1: 3, 2: 2})
        )

    @pytest.mark.parametrize("tz", [None, "UTC"])
    def test_mean_mixed_datetime_numeric(self, tz):
        # https://github.com/pandas-dev/pandas/issues/24752
        df = pd.DataFrame({"A": [1, 1], "B": [pd.Timestamp("2000", tz=tz)] * 2})
        result = df.mean()
        expected = pd.Series([1.0], index=["A"])
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("tz", [None, "UTC"])
    def test_mean_excludeds_datetimes(self, tz):
        # https://github.com/pandas-dev/pandas/issues/24752
        # Our long-term desired behavior is unclear, but the behavior in
        # 0.24.0rc1 was buggy.
        df = pd.DataFrame({"A": [pd.Timestamp("2000", tz=tz)] * 2})
        result = df.mean()
        expected = pd.Series()
        tm.assert_series_equal(result, expected)

    def test_var_std(self, datetime_frame):
        result = datetime_frame.std(ddof=4)
        expected = datetime_frame.apply(lambda x: x.std(ddof=4))
        tm.assert_almost_equal(result, expected)

        result = datetime_frame.var(ddof=4)
        expected = datetime_frame.apply(lambda x: x.var(ddof=4))
        tm.assert_almost_equal(result, expected)

        arr = np.repeat(np.random.random((1, 1000)), 1000, 0)
        result = nanops.nanvar(arr, axis=0)
        assert not (result < 0).any()

        with pd.option_context("use_bottleneck", False):
            result = nanops.nanvar(arr, axis=0)
            assert not (result < 0).any()

    @pytest.mark.parametrize("meth", ["sem", "var", "std"])
    def test_numeric_only_flag(self, meth):
        # GH 9201
        df1 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"])
        # set one entry to a number in str format
        df1.loc[0, "foo"] = "100"

        df2 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"])
        # set one entry to a non-number str
        df2.loc[0, "foo"] = "a"

        result = getattr(df1, meth)(axis=1, numeric_only=True)
        expected = getattr(df1[["bar", "baz"]], meth)(axis=1)
        tm.assert_series_equal(expected, result)

        result = getattr(df2, meth)(axis=1, numeric_only=True)
        expected = getattr(df2[["bar", "baz"]], meth)(axis=1)
        tm.assert_series_equal(expected, result)

        # df1 has all numbers, df2 has a letter inside
        msg = r"unsupported operand type\(s\) for -: 'float' and 'str'"
        with pytest.raises(TypeError, match=msg):
            getattr(df1, meth)(axis=1, numeric_only=False)
        msg = "could not convert string to float: 'a'"
        with pytest.raises(TypeError, match=msg):
            getattr(df2, meth)(axis=1, numeric_only=False)

    def test_sem(self, datetime_frame):
        result = datetime_frame.sem(ddof=4)
        expected = datetime_frame.apply(lambda x: x.std(ddof=4) / np.sqrt(len(x)))
        tm.assert_almost_equal(result, expected)

        arr = np.repeat(np.random.random((1, 1000)), 1000, 0)
        result = nanops.nansem(arr, axis=0)
        assert not (result < 0).any()

        with pd.option_context("use_bottleneck", False):
            result = nanops.nansem(arr, axis=0)
            assert not (result < 0).any()

    @td.skip_if_no_scipy
    def test_kurt(self):
        index = MultiIndex(
            levels=[["bar"], ["one", "two", "three"], [0, 1]],
            codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
        )
        df = DataFrame(np.random.randn(6, 3), index=index)

        kurt = df.kurt()
        kurt2 = df.kurt(level=0).xs("bar")
        tm.assert_series_equal(kurt, kurt2, check_names=False)
        assert kurt.name is None
        assert kurt2.name == "bar"

    @pytest.mark.parametrize(
        "dropna, expected",
        [
            (
                True,
                {
                    "A": [12],
                    "B": [10.0],
                    "C": [1.0],
                    "D": ["a"],
                    "E": Categorical(["a"], categories=["a"]),
                    "F": to_datetime(["2000-1-2"]),
                    "G": to_timedelta(["1 days"]),
                },
            ),
            (
                False,
                {
                    "A": [12],
                    "B": [10.0],
                    "C": [np.nan],
                    "D": np.array([np.nan], dtype=object),
                    "E": Categorical([np.nan], categories=["a"]),
                    "F": [pd.NaT],
                    "G": to_timedelta([pd.NaT]),
                },
            ),
            (
                True,
                {
                    "H": [8, 9, np.nan, np.nan],
                    "I": [8, 9, np.nan, np.nan],
                    "J": [1, np.nan, np.nan, np.nan],
                    "K": Categorical(["a", np.nan, np.nan, np.nan], categories=["a"]),
                    "L": to_datetime(["2000-1-2", "NaT", "NaT", "NaT"]),
                    "M": to_timedelta(["1 days", "nan", "nan", "nan"]),
                    "N": [0, 1, 2, 3],
                },
            ),
            (
                False,
                {
                    "H": [8, 9, np.nan, np.nan],
                    "I": [8, 9, np.nan, np.nan],
                    "J": [1, np.nan, np.nan, np.nan],
                    "K": Categorical([np.nan, "a", np.nan, np.nan], categories=["a"]),
                    "L": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]),
                    "M": to_timedelta(["nan", "1 days", "nan", "nan"]),
                    "N": [0, 1, 2, 3],
                },
            ),
        ],
    )
    def test_mode_dropna(self, dropna, expected):

        df = DataFrame(
            {
                "A": [12, 12, 19, 11],
                "B": [10, 10, np.nan, 3],
                "C": [1, np.nan, np.nan, np.nan],
                "D": [np.nan, np.nan, "a", np.nan],
                "E": Categorical([np.nan, np.nan, "a", np.nan]),
                "F": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]),
                "G": to_timedelta(["1 days", "nan", "nan", "nan"]),
                "H": [8, 8, 9, 9],
                "I": [9, 9, 8, 8],
                "J": [1, 1, np.nan, np.nan],
                "K": Categorical(["a", np.nan, "a", np.nan]),
                "L": to_datetime(["2000-1-2", "2000-1-2", "NaT", "NaT"]),
                "M": to_timedelta(["1 days", "nan", "1 days", "nan"]),
                "N": np.arange(4, dtype="int64"),
            }
        )

        result = df[sorted(list(expected.keys()))].mode(dropna=dropna)
        expected = DataFrame(expected)
        tm.assert_frame_equal(result, expected)

    def test_mode_sortwarning(self):
        # Check for the warning that is raised when the mode
        # results cannot be sorted

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

        with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
            result = df.mode(dropna=False)
            result = result.sort_values(by="A").reset_index(drop=True)

        tm.assert_frame_equal(result, expected)

    def test_operators_timedelta64(self):
        df = DataFrame(
            dict(
                A=date_range("2012-1-1", periods=3, freq="D"),
                B=date_range("2012-1-2", periods=3, freq="D"),
                C=Timestamp("20120101") - timedelta(minutes=5, seconds=5),
            )
        )

        diffs = DataFrame(dict(A=df["A"] - df["C"], B=df["A"] - df["B"]))

        # min
        result = diffs.min()
        assert result[0] == diffs.loc[0, "A"]
        assert result[1] == diffs.loc[0, "B"]

        result = diffs.min(axis=1)
        assert (result == diffs.loc[0, "B"]).all()

        # max
        result = diffs.max()
        assert result[0] == diffs.loc[2, "A"]
        assert result[1] == diffs.loc[2, "B"]

        result = diffs.max(axis=1)
        assert (result == diffs["A"]).all()

        # abs
        result = diffs.abs()
        result2 = abs(diffs)
        expected = DataFrame(dict(A=df["A"] - df["C"], B=df["B"] - df["A"]))
        tm.assert_frame_equal(result, expected)
        tm.assert_frame_equal(result2, expected)

        # mixed frame
        mixed = diffs.copy()
        mixed["C"] = "foo"
        mixed["D"] = 1
        mixed["E"] = 1.0
        mixed["F"] = Timestamp("20130101")

        # results in an object array
        result = mixed.min()
        expected = Series(
            [
                pd.Timedelta(timedelta(seconds=5 * 60 + 5)),
                pd.Timedelta(timedelta(days=-1)),
                "foo",
                1,
                1.0,
                Timestamp("20130101"),
            ],
            index=mixed.columns,
        )
        tm.assert_series_equal(result, expected)

        # excludes numeric
        result = mixed.min(axis=1)
        expected = Series([1, 1, 1.0], index=[0, 1, 2])
        tm.assert_series_equal(result, expected)

        # works when only those columns are selected
        result = mixed[["A", "B"]].min(1)
        expected = Series([timedelta(days=-1)] * 3)
        tm.assert_series_equal(result, expected)

        result = mixed[["A", "B"]].min()
        expected = Series(
            [timedelta(seconds=5 * 60 + 5), timedelta(days=-1)], index=["A", "B"]
        )
        tm.assert_series_equal(result, expected)

        # GH 3106
        df = DataFrame(
            {
                "time": date_range("20130102", periods=5),
                "time2": date_range("20130105", periods=5),
            }
        )
        df["off1"] = df["time2"] - df["time"]
        assert df["off1"].dtype == "timedelta64[ns]"

        df["off2"] = df["time"] - df["time2"]
        df._consolidate_inplace()
        assert df["off1"].dtype == "timedelta64[ns]"
        assert df["off2"].dtype == "timedelta64[ns]"

    def test_sum_corner(self):
        empty_frame = DataFrame()

        axis0 = empty_frame.sum(0)
        axis1 = empty_frame.sum(1)
        assert isinstance(axis0, Series)
        assert isinstance(axis1, Series)
        assert len(axis0) == 0
        assert len(axis1) == 0

    @pytest.mark.parametrize("method, unit", [("sum", 0), ("prod", 1)])
    def test_sum_prod_nanops(self, method, unit):
        idx = ["a", "b", "c"]
        df = pd.DataFrame(
            {"a": [unit, unit], "b": [unit, np.nan], "c": [np.nan, np.nan]}
        )
        # The default
        result = getattr(df, method)
        expected = pd.Series([unit, unit, unit], index=idx, dtype="float64")

        # min_count=1
        result = getattr(df, method)(min_count=1)
        expected = pd.Series([unit, unit, np.nan], index=idx)
        tm.assert_series_equal(result, expected)

        # min_count=0
        result = getattr(df, method)(min_count=0)
        expected = pd.Series([unit, unit, unit], index=idx, dtype="float64")
        tm.assert_series_equal(result, expected)

        result = getattr(df.iloc[1:], method)(min_count=1)
        expected = pd.Series([unit, np.nan, np.nan], index=idx)
        tm.assert_series_equal(result, expected)

        # min_count > 1
        df = pd.DataFrame({"A": [unit] * 10, "B": [unit] * 5 + [np.nan] * 5})
        result = getattr(df, method)(min_count=5)
        expected = pd.Series(result, index=["A", "B"])
        tm.assert_series_equal(result, expected)

        result = getattr(df, method)(min_count=6)
        expected = pd.Series(result, index=["A", "B"])
        tm.assert_series_equal(result, expected)

    def test_sum_nanops_timedelta(self):
        # prod isn't defined on timedeltas
        idx = ["a", "b", "c"]
        df = pd.DataFrame({"a": [0, 0], "b": [0, np.nan], "c": [np.nan, np.nan]})

        df2 = df.apply(pd.to_timedelta)

        # 0 by default
        result = df2.sum()
        expected = pd.Series([0, 0, 0], dtype="m8[ns]", index=idx)
        tm.assert_series_equal(result, expected)

        # min_count=0
        result = df2.sum(min_count=0)
        tm.assert_series_equal(result, expected)

        # min_count=1
        result = df2.sum(min_count=1)
        expected = pd.Series([0, 0, np.nan], dtype="m8[ns]", index=idx)
        tm.assert_series_equal(result, expected)

    def test_sum_object(self, float_frame):
        values = float_frame.values.astype(int)
        frame = DataFrame(values, index=float_frame.index, columns=float_frame.columns)
        deltas = frame * timedelta(1)
        deltas.sum()

    def test_sum_bool(self, float_frame):
        # ensure this works, bug report
        bools = np.isnan(float_frame)
        bools.sum(1)
        bools.sum(0)

    def test_mean_corner(self, float_frame, float_string_frame):
        # unit test when have object data
        the_mean = float_string_frame.mean(axis=0)
        the_sum = float_string_frame.sum(axis=0, numeric_only=True)
        tm.assert_index_equal(the_sum.index, the_mean.index)
        assert len(the_mean.index) < len(float_string_frame.columns)

        # xs sum mixed type, just want to know it works...
        the_mean = float_string_frame.mean(axis=1)
        the_sum = float_string_frame.sum(axis=1, numeric_only=True)
        tm.assert_index_equal(the_sum.index, the_mean.index)

        # take mean of boolean column
        float_frame["bool"] = float_frame["A"] > 0
        means = float_frame.mean(0)
        assert means["bool"] == float_frame["bool"].values.mean()

    def test_mean_datetimelike(self):
        # GH#24757 check that datetimelike are excluded by default, handled
        #  correctly with numeric_only=True

        df = pd.DataFrame(
            {
                "A": np.arange(3),
                "B": pd.date_range("2016-01-01", periods=3),
                "C": pd.timedelta_range("1D", periods=3),
                "D": pd.period_range("2016", periods=3, freq="A"),
            }
        )
        result = df.mean(numeric_only=True)
        expected = pd.Series({"A": 1.0})
        tm.assert_series_equal(result, expected)

        result = df.mean()
        expected = pd.Series({"A": 1.0, "C": df.loc[1, "C"]})
        tm.assert_series_equal(result, expected)

    @pytest.mark.xfail(
        reason="casts to object-dtype and then tries to add timestamps",
        raises=TypeError,
        strict=True,
    )
    def test_mean_datetimelike_numeric_only_false(self):
        df = pd.DataFrame(
            {
                "A": np.arange(3),
                "B": pd.date_range("2016-01-01", periods=3),
                "C": pd.timedelta_range("1D", periods=3),
                "D": pd.period_range("2016", periods=3, freq="A"),
            }
        )

        result = df.mean(numeric_only=False)
        expected = pd.Series(
            {"A": 1, "B": df.loc[1, "B"], "C": df.loc[1, "C"], "D": df.loc[1, "D"]}
        )
        tm.assert_series_equal(result, expected)

    def test_stats_mixed_type(self, float_string_frame):
        # don't blow up
        float_string_frame.std(1)
        float_string_frame.var(1)
        float_string_frame.mean(1)
        float_string_frame.skew(1)

    def test_sum_bools(self):
        df = DataFrame(index=range(1), columns=range(10))
        bools = isna(df)
        assert bools.sum(axis=1)[0] == 10

    # ---------------------------------------------------------------------
    # Cumulative Reductions - cumsum, cummax, ...

    def test_cumsum_corner(self):
        dm = DataFrame(np.arange(20).reshape(4, 5), index=range(4), columns=range(5))
        # ?(wesm)
        result = dm.cumsum()  # noqa

    def test_cumsum(self, datetime_frame):
        datetime_frame.loc[5:10, 0] = np.nan
        datetime_frame.loc[10:15, 1] = np.nan
        datetime_frame.loc[15:, 2] = np.nan

        # axis = 0
        cumsum = datetime_frame.cumsum()
        expected = datetime_frame.apply(Series.cumsum)
        tm.assert_frame_equal(cumsum, expected)

        # axis = 1
        cumsum = datetime_frame.cumsum(axis=1)
        expected = datetime_frame.apply(Series.cumsum, axis=1)
        tm.assert_frame_equal(cumsum, expected)

        # works
        df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
        result = df.cumsum()  # noqa

        # fix issue
        cumsum_xs = datetime_frame.cumsum(axis=1)
        assert np.shape(cumsum_xs) == np.shape(datetime_frame)

    def test_cumprod(self, datetime_frame):
        datetime_frame.loc[5:10, 0] = np.nan
        datetime_frame.loc[10:15, 1] = np.nan
        datetime_frame.loc[15:, 2] = np.nan

        # axis = 0
        cumprod = datetime_frame.cumprod()
        expected = datetime_frame.apply(Series.cumprod)
        tm.assert_frame_equal(cumprod, expected)

        # axis = 1
        cumprod = datetime_frame.cumprod(axis=1)
        expected = datetime_frame.apply(Series.cumprod, axis=1)
        tm.assert_frame_equal(cumprod, expected)

        # fix issue
        cumprod_xs = datetime_frame.cumprod(axis=1)
        assert np.shape(cumprod_xs) == np.shape(datetime_frame)

        # ints
        df = datetime_frame.fillna(0).astype(int)
        df.cumprod(0)
        df.cumprod(1)

        # ints32
        df = datetime_frame.fillna(0).astype(np.int32)
        df.cumprod(0)
        df.cumprod(1)

    def test_cummin(self, datetime_frame):
        datetime_frame.loc[5:10, 0] = np.nan
        datetime_frame.loc[10:15, 1] = np.nan
        datetime_frame.loc[15:, 2] = np.nan

        # axis = 0
        cummin = datetime_frame.cummin()
        expected = datetime_frame.apply(Series.cummin)
        tm.assert_frame_equal(cummin, expected)

        # axis = 1
        cummin = datetime_frame.cummin(axis=1)
        expected = datetime_frame.apply(Series.cummin, axis=1)
        tm.assert_frame_equal(cummin, expected)

        # it works
        df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
        result = df.cummin()  # noqa

        # fix issue
        cummin_xs = datetime_frame.cummin(axis=1)
        assert np.shape(cummin_xs) == np.shape(datetime_frame)

    def test_cummax(self, datetime_frame):
        datetime_frame.loc[5:10, 0] = np.nan
        datetime_frame.loc[10:15, 1] = np.nan
        datetime_frame.loc[15:, 2] = np.nan

        # axis = 0
        cummax = datetime_frame.cummax()
        expected = datetime_frame.apply(Series.cummax)
        tm.assert_frame_equal(cummax, expected)

        # axis = 1
        cummax = datetime_frame.cummax(axis=1)
        expected = datetime_frame.apply(Series.cummax, axis=1)
        tm.assert_frame_equal(cummax, expected)

        # it works
        df = DataFrame({"A": np.arange(20)}, index=np.arange(20))
        result = df.cummax()  # noqa

        # fix issue
        cummax_xs = datetime_frame.cummax(axis=1)
        assert np.shape(cummax_xs) == np.shape(datetime_frame)

    # ---------------------------------------------------------------------
    # Miscellanea

    def test_count(self):
        # corner case
        frame = DataFrame()
        ct1 = frame.count(1)
        assert isinstance(ct1, Series)

        ct2 = frame.count(0)
        assert isinstance(ct2, Series)

        # GH#423
        df = DataFrame(index=range(10))
        result = df.count(1)
        expected = Series(0, index=df.index)
        tm.assert_series_equal(result, expected)

        df = DataFrame(columns=range(10))
        result = df.count(0)
        expected = Series(0, index=df.columns)
        tm.assert_series_equal(result, expected)

        df = DataFrame()
        result = df.count()
        expected = Series(0, index=[])
        tm.assert_series_equal(result, expected)

    def test_count_objects(self, float_string_frame):
        dm = DataFrame(float_string_frame._series)
        df = DataFrame(float_string_frame._series)

        tm.assert_series_equal(dm.count(), df.count())
        tm.assert_series_equal(dm.count(1), df.count(1))

    def test_pct_change(self):
        # GH#11150
        pnl = DataFrame(
            [np.arange(0, 40, 10), np.arange(0, 40, 10), np.arange(0, 40, 10)]
        ).astype(np.float64)
        pnl.iat[1, 0] = np.nan
        pnl.iat[1, 1] = np.nan
        pnl.iat[2, 3] = 60

        for axis in range(2):
            expected = pnl.ffill(axis=axis) / pnl.ffill(axis=axis).shift(axis=axis) - 1
            result = pnl.pct_change(axis=axis, fill_method="pad")

            tm.assert_frame_equal(result, expected)

    # ----------------------------------------------------------------------
    # Index of max / min

    def test_idxmin(self, float_frame, int_frame):
        frame = float_frame
        frame.loc[5:10] = np.nan
        frame.loc[15:20, -2:] = np.nan
        for skipna in [True, False]:
            for axis in [0, 1]:
                for df in [frame, int_frame]:
                    result = df.idxmin(axis=axis, skipna=skipna)
                    expected = df.apply(Series.idxmin, axis=axis, skipna=skipna)
                    tm.assert_series_equal(result, expected)

        msg = "No axis named 2 for object type <class 'pandas.core.frame.DataFrame'>"
        with pytest.raises(ValueError, match=msg):
            frame.idxmin(axis=2)

    def test_idxmax(self, float_frame, int_frame):
        frame = float_frame
        frame.loc[5:10] = np.nan
        frame.loc[15:20, -2:] = np.nan
        for skipna in [True, False]:
            for axis in [0, 1]:
                for df in [frame, int_frame]:
                    result = df.idxmax(axis=axis, skipna=skipna)
                    expected = df.apply(Series.idxmax, axis=axis, skipna=skipna)
                    tm.assert_series_equal(result, expected)

        msg = "No axis named 2 for object type <class 'pandas.core.frame.DataFrame'>"
        with pytest.raises(ValueError, match=msg):
            frame.idxmax(axis=2)

    # ----------------------------------------------------------------------
    # Logical reductions

    @pytest.mark.parametrize("opname", ["any", "all"])
    def test_any_all(self, opname, bool_frame_with_na, float_string_frame):
        assert_bool_op_calc(
            opname, getattr(np, opname), bool_frame_with_na, has_skipna=True
        )
        assert_bool_op_api(
            opname, bool_frame_with_na, float_string_frame, has_bool_only=True
        )

    def test_any_all_extra(self):
        df = DataFrame(
            {
                "A": [True, False, False],
                "B": [True, True, False],
                "C": [True, True, True],
            },
            index=["a", "b", "c"],
        )
        result = df[["A", "B"]].any(1)
        expected = Series([True, True, False], index=["a", "b", "c"])
        tm.assert_series_equal(result, expected)

        result = df[["A", "B"]].any(1, bool_only=True)
        tm.assert_series_equal(result, expected)

        result = df.all(1)
        expected = Series([True, False, False], index=["a", "b", "c"])
        tm.assert_series_equal(result, expected)

        result = df.all(1, bool_only=True)
        tm.assert_series_equal(result, expected)

        # Axis is None
        result = df.all(axis=None).item()
        assert result is False

        result = df.any(axis=None).item()
        assert result is True

        result = df[["C"]].all(axis=None).item()
        assert result is True

    def test_any_datetime(self):

        # GH 23070
        float_data = [1, np.nan, 3, np.nan]
        datetime_data = [
            pd.Timestamp("1960-02-15"),
            pd.Timestamp("1960-02-16"),
            pd.NaT,
            pd.NaT,
        ]
        df = DataFrame({"A": float_data, "B": datetime_data})

        result = df.any(1)
        expected = Series([True, True, True, False])
        tm.assert_series_equal(result, expected)

    def test_any_all_bool_only(self):

        # GH 25101
        df = DataFrame(
            {"col1": [1, 2, 3], "col2": [4, 5, 6], "col3": [None, None, None]}
        )

        result = df.all(bool_only=True)
        expected = Series(dtype=np.bool)
        tm.assert_series_equal(result, expected)

        df = DataFrame(
            {
                "col1": [1, 2, 3],
                "col2": [4, 5, 6],
                "col3": [None, None, None],
                "col4": [False, False, True],
            }
        )

        result = df.all(bool_only=True)
        expected = Series({"col4": False})
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "func, data, expected",
        [
            (np.any, {}, False),
            (np.all, {}, True),
            (np.any, {"A": []}, False),
            (np.all, {"A": []}, True),
            (np.any, {"A": [False, False]}, False),
            (np.all, {"A": [False, False]}, False),
            (np.any, {"A": [True, False]}, True),
            (np.all, {"A": [True, False]}, False),
            (np.any, {"A": [True, True]}, True),
            (np.all, {"A": [True, True]}, True),
            (np.any, {"A": [False], "B": [False]}, False),
            (np.all, {"A": [False], "B": [False]}, False),
            (np.any, {"A": [False, False], "B": [False, True]}, True),
            (np.all, {"A": [False, False], "B": [False, True]}, False),
            # other types
            (np.all, {"A": pd.Series([0.0, 1.0], dtype="float")}, False),
            (np.any, {"A": pd.Series([0.0, 1.0], dtype="float")}, True),
            (np.all, {"A": pd.Series([0, 1], dtype=int)}, False),
            (np.any, {"A": pd.Series([0, 1], dtype=int)}, True),
            pytest.param(
                np.all,
                {"A": pd.Series([0, 1], dtype="M8[ns]")},
                False,
                marks=[td.skip_if_np_lt("1.15")],
            ),
            pytest.param(
                np.any,
                {"A": pd.Series([0, 1], dtype="M8[ns]")},
                True,
                marks=[td.skip_if_np_lt("1.15")],
            ),
            pytest.param(
                np.all,
                {"A": pd.Series([1, 2], dtype="M8[ns]")},
                True,
                marks=[td.skip_if_np_lt("1.15")],
            ),
            pytest.param(
                np.any,
                {"A": pd.Series([1, 2], dtype="M8[ns]")},
                True,
                marks=[td.skip_if_np_lt("1.15")],
            ),
            pytest.param(
                np.all,
                {"A": pd.Series([0, 1], dtype="m8[ns]")},
                False,
                marks=[td.skip_if_np_lt("1.15")],
            ),
            pytest.param(
                np.any,
                {"A": pd.Series([0, 1], dtype="m8[ns]")},
                True,
                marks=[td.skip_if_np_lt("1.15")],
            ),
            pytest.param(
                np.all,
                {"A": pd.Series([1, 2], dtype="m8[ns]")},
                True,
                marks=[td.skip_if_np_lt("1.15")],
            ),
            pytest.param(
                np.any,
                {"A": pd.Series([1, 2], dtype="m8[ns]")},
                True,
                marks=[td.skip_if_np_lt("1.15")],
            ),
            (np.all, {"A": pd.Series([0, 1], dtype="category")}, False),
            (np.any, {"A": pd.Series([0, 1], dtype="category")}, True),
            (np.all, {"A": pd.Series([1, 2], dtype="category")}, True),
            (np.any, {"A": pd.Series([1, 2], dtype="category")}, True),
            # Mix GH#21484
            pytest.param(
                np.all,
                {
                    "A": pd.Series([10, 20], dtype="M8[ns]"),
                    "B": pd.Series([10, 20], dtype="m8[ns]"),
                },
                True,
                # In 1.13.3 and 1.14 np.all(df) returns a Timedelta here
                marks=[td.skip_if_np_lt("1.15")],
            ),
        ],
    )
    def test_any_all_np_func(self, func, data, expected):
        # GH 19976
        data = DataFrame(data)
        result = func(data)
        assert isinstance(result, np.bool_)
        assert result.item() is expected

        # method version
        result = getattr(DataFrame(data), func.__name__)(axis=None)
        assert isinstance(result, np.bool_)
        assert result.item() is expected

    def test_any_all_object(self):
        # GH 19976
        result = np.all(DataFrame(columns=["a", "b"])).item()
        assert result is True

        result = np.any(DataFrame(columns=["a", "b"])).item()
        assert result is False

    @pytest.mark.parametrize("method", ["any", "all"])
    def test_any_all_level_axis_none_raises(self, method):
        df = DataFrame(
            {"A": 1},
            index=MultiIndex.from_product(
                [["A", "B"], ["a", "b"]], names=["out", "in"]
            ),
        )
        xpr = "Must specify 'axis' when aggregating by level."
        with pytest.raises(ValueError, match=xpr):
            getattr(df, method)(axis=None, level="out")

    # ----------------------------------------------------------------------
    # Isin

    def test_isin(self):
        # GH 4211
        df = DataFrame(
            {
                "vals": [1, 2, 3, 4],
                "ids": ["a", "b", "f", "n"],
                "ids2": ["a", "n", "c", "n"],
            },
            index=["foo", "bar", "baz", "qux"],
        )
        other = ["a", "b", "c"]

        result = df.isin(other)
        expected = DataFrame([df.loc[s].isin(other) for s in df.index])
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize("empty", [[], Series(), np.array([])])
    def test_isin_empty(self, empty):
        # GH 16991
        df = DataFrame({"A": ["a", "b", "c"], "B": ["a", "e", "f"]})
        expected = DataFrame(False, df.index, df.columns)

        result = df.isin(empty)
        tm.assert_frame_equal(result, expected)

    def test_isin_dict(self):
        df = DataFrame({"A": ["a", "b", "c"], "B": ["a", "e", "f"]})
        d = {"A": ["a"]}

        expected = DataFrame(False, df.index, df.columns)
        expected.loc[0, "A"] = True

        result = df.isin(d)
        tm.assert_frame_equal(result, expected)

        # non unique columns
        df = DataFrame({"A": ["a", "b", "c"], "B": ["a", "e", "f"]})
        df.columns = ["A", "A"]
        expected = DataFrame(False, df.index, df.columns)
        expected.loc[0, "A"] = True
        result = df.isin(d)
        tm.assert_frame_equal(result, expected)

    def test_isin_with_string_scalar(self):
        # GH 4763
        df = DataFrame(
            {
                "vals": [1, 2, 3, 4],
                "ids": ["a", "b", "f", "n"],
                "ids2": ["a", "n", "c", "n"],
            },
            index=["foo", "bar", "baz", "qux"],
        )
        with pytest.raises(TypeError):
            df.isin("a")

        with pytest.raises(TypeError):
            df.isin("aaa")

    def test_isin_df(self):
        df1 = DataFrame({"A": [1, 2, 3, 4], "B": [2, np.nan, 4, 4]})
        df2 = DataFrame({"A": [0, 2, 12, 4], "B": [2, np.nan, 4, 5]})
        expected = DataFrame(False, df1.index, df1.columns)
        result = df1.isin(df2)
        expected["A"].loc[[1, 3]] = True
        expected["B"].loc[[0, 2]] = True
        tm.assert_frame_equal(result, expected)

        # partial overlapping columns
        df2.columns = ["A", "C"]
        result = df1.isin(df2)
        expected["B"] = False
        tm.assert_frame_equal(result, expected)

    def test_isin_tuples(self):
        # GH 16394
        df = pd.DataFrame({"A": [1, 2, 3], "B": ["a", "b", "f"]})
        df["C"] = list(zip(df["A"], df["B"]))
        result = df["C"].isin([(1, "a")])
        tm.assert_series_equal(result, Series([True, False, False], name="C"))

    def test_isin_df_dupe_values(self):
        df1 = DataFrame({"A": [1, 2, 3, 4], "B": [2, np.nan, 4, 4]})
        # just cols duped
        df2 = DataFrame([[0, 2], [12, 4], [2, np.nan], [4, 5]], columns=["B", "B"])
        with pytest.raises(ValueError):
            df1.isin(df2)

        # just index duped
        df2 = DataFrame(
            [[0, 2], [12, 4], [2, np.nan], [4, 5]],
            columns=["A", "B"],
            index=[0, 0, 1, 1],
        )
        with pytest.raises(ValueError):
            df1.isin(df2)

        # cols and index:
        df2.columns = ["B", "B"]
        with pytest.raises(ValueError):
            df1.isin(df2)

    def test_isin_dupe_self(self):
        other = DataFrame({"A": [1, 0, 1, 0], "B": [1, 1, 0, 0]})
        df = DataFrame([[1, 1], [1, 0], [0, 0]], columns=["A", "A"])
        result = df.isin(other)
        expected = DataFrame(False, index=df.index, columns=df.columns)
        expected.loc[0] = True
        expected.iloc[1, 1] = True
        tm.assert_frame_equal(result, expected)

    def test_isin_against_series(self):
        df = pd.DataFrame(
            {"A": [1, 2, 3, 4], "B": [2, np.nan, 4, 4]}, index=["a", "b", "c", "d"]
        )
        s = pd.Series([1, 3, 11, 4], index=["a", "b", "c", "d"])
        expected = DataFrame(False, index=df.index, columns=df.columns)
        expected["A"].loc["a"] = True
        expected.loc["d"] = True
        result = df.isin(s)
        tm.assert_frame_equal(result, expected)

    def test_isin_multiIndex(self):
        idx = MultiIndex.from_tuples(
            [
                (0, "a", "foo"),
                (0, "a", "bar"),
                (0, "b", "bar"),
                (0, "b", "baz"),
                (2, "a", "foo"),
                (2, "a", "bar"),
                (2, "c", "bar"),
                (2, "c", "baz"),
                (1, "b", "foo"),
                (1, "b", "bar"),
                (1, "c", "bar"),
                (1, "c", "baz"),
            ]
        )
        df1 = DataFrame({"A": np.ones(12), "B": np.zeros(12)}, index=idx)
        df2 = DataFrame(
            {
                "A": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
                "B": [1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1],
            }
        )
        # against regular index
        expected = DataFrame(False, index=df1.index, columns=df1.columns)
        result = df1.isin(df2)
        tm.assert_frame_equal(result, expected)

        df2.index = idx
        expected = df2.values.astype(np.bool)
        expected[:, 1] = ~expected[:, 1]
        expected = DataFrame(expected, columns=["A", "B"], index=idx)

        result = df1.isin(df2)
        tm.assert_frame_equal(result, expected)

    def test_isin_empty_datetimelike(self):
        # GH 15473
        df1_ts = DataFrame({"date": pd.to_datetime(["2014-01-01", "2014-01-02"])})
        df1_td = DataFrame({"date": [pd.Timedelta(1, "s"), pd.Timedelta(2, "s")]})
        df2 = DataFrame({"date": []})
        df3 = DataFrame()

        expected = DataFrame({"date": [False, False]})

        result = df1_ts.isin(df2)
        tm.assert_frame_equal(result, expected)
        result = df1_ts.isin(df3)
        tm.assert_frame_equal(result, expected)

        result = df1_td.isin(df2)
        tm.assert_frame_equal(result, expected)
        result = df1_td.isin(df3)
        tm.assert_frame_equal(result, expected)

    # ---------------------------------------------------------------------
    # Rounding

    def test_round(self):
        # GH 2665

        # Test that rounding an empty DataFrame does nothing
        df = DataFrame()
        tm.assert_frame_equal(df, df.round())

        # Here's the test frame we'll be working with
        df = DataFrame({"col1": [1.123, 2.123, 3.123], "col2": [1.234, 2.234, 3.234]})

        # Default round to integer (i.e. decimals=0)
        expected_rounded = DataFrame({"col1": [1.0, 2.0, 3.0], "col2": [1.0, 2.0, 3.0]})
        tm.assert_frame_equal(df.round(), expected_rounded)

        # Round with an integer
        decimals = 2
        expected_rounded = DataFrame(
            {"col1": [1.12, 2.12, 3.12], "col2": [1.23, 2.23, 3.23]}
        )
        tm.assert_frame_equal(df.round(decimals), expected_rounded)

        # This should also work with np.round (since np.round dispatches to
        # df.round)
        tm.assert_frame_equal(np.round(df, decimals), expected_rounded)

        # Round with a list
        round_list = [1, 2]
        with pytest.raises(TypeError):
            df.round(round_list)

        # Round with a dictionary
        expected_rounded = DataFrame(
            {"col1": [1.1, 2.1, 3.1], "col2": [1.23, 2.23, 3.23]}
        )
        round_dict = {"col1": 1, "col2": 2}
        tm.assert_frame_equal(df.round(round_dict), expected_rounded)

        # Incomplete dict
        expected_partially_rounded = DataFrame(
            {"col1": [1.123, 2.123, 3.123], "col2": [1.2, 2.2, 3.2]}
        )
        partial_round_dict = {"col2": 1}
        tm.assert_frame_equal(df.round(partial_round_dict), expected_partially_rounded)

        # Dict with unknown elements
        wrong_round_dict = {"col3": 2, "col2": 1}
        tm.assert_frame_equal(df.round(wrong_round_dict), expected_partially_rounded)

        # float input to `decimals`
        non_int_round_dict = {"col1": 1, "col2": 0.5}
        with pytest.raises(TypeError):
            df.round(non_int_round_dict)

        # String input
        non_int_round_dict = {"col1": 1, "col2": "foo"}
        with pytest.raises(TypeError):
            df.round(non_int_round_dict)

        non_int_round_Series = Series(non_int_round_dict)
        with pytest.raises(TypeError):
            df.round(non_int_round_Series)

        # List input
        non_int_round_dict = {"col1": 1, "col2": [1, 2]}
        with pytest.raises(TypeError):
            df.round(non_int_round_dict)

        non_int_round_Series = Series(non_int_round_dict)
        with pytest.raises(TypeError):
            df.round(non_int_round_Series)

        # Non integer Series inputs
        non_int_round_Series = Series(non_int_round_dict)
        with pytest.raises(TypeError):
            df.round(non_int_round_Series)

        non_int_round_Series = Series(non_int_round_dict)
        with pytest.raises(TypeError):
            df.round(non_int_round_Series)

        # Negative numbers
        negative_round_dict = {"col1": -1, "col2": -2}
        big_df = df * 100
        expected_neg_rounded = DataFrame(
            {"col1": [110.0, 210, 310], "col2": [100.0, 200, 300]}
        )
        tm.assert_frame_equal(big_df.round(negative_round_dict), expected_neg_rounded)

        # nan in Series round
        nan_round_Series = Series({"col1": np.nan, "col2": 1})

        # TODO(wesm): unused?
        expected_nan_round = DataFrame(  # noqa
            {"col1": [1.123, 2.123, 3.123], "col2": [1.2, 2.2, 3.2]}
        )

        with pytest.raises(TypeError):
            df.round(nan_round_Series)

        # Make sure this doesn't break existing Series.round
        tm.assert_series_equal(df["col1"].round(1), expected_rounded["col1"])

        # named columns
        # GH 11986
        decimals = 2
        expected_rounded = DataFrame(
            {"col1": [1.12, 2.12, 3.12], "col2": [1.23, 2.23, 3.23]}
        )
        df.columns.name = "cols"
        expected_rounded.columns.name = "cols"
        tm.assert_frame_equal(df.round(decimals), expected_rounded)

        # interaction of named columns & series
        tm.assert_series_equal(df["col1"].round(decimals), expected_rounded["col1"])
        tm.assert_series_equal(df.round(decimals)["col1"], expected_rounded["col1"])

    def test_numpy_round(self):
        # GH 12600
        df = DataFrame([[1.53, 1.36], [0.06, 7.01]])
        out = np.round(df, decimals=0)
        expected = DataFrame([[2.0, 1.0], [0.0, 7.0]])
        tm.assert_frame_equal(out, expected)

        msg = "the 'out' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.round(df, decimals=0, out=df)

    def test_numpy_round_nan(self):
        # See gh-14197
        df = Series([1.53, np.nan, 0.06]).to_frame()
        with tm.assert_produces_warning(None):
            result = df.round()
        expected = Series([2.0, np.nan, 0.0]).to_frame()
        tm.assert_frame_equal(result, expected)

    def test_round_mixed_type(self):
        # GH 11885
        df = DataFrame(
            {
                "col1": [1.1, 2.2, 3.3, 4.4],
                "col2": ["1", "a", "c", "f"],
                "col3": date_range("20111111", periods=4),
            }
        )
        round_0 = DataFrame(
            {
                "col1": [1.0, 2.0, 3.0, 4.0],
                "col2": ["1", "a", "c", "f"],
                "col3": date_range("20111111", periods=4),
            }
        )
        tm.assert_frame_equal(df.round(), round_0)
        tm.assert_frame_equal(df.round(1), df)
        tm.assert_frame_equal(df.round({"col1": 1}), df)
        tm.assert_frame_equal(df.round({"col1": 0}), round_0)
        tm.assert_frame_equal(df.round({"col1": 0, "col2": 1}), round_0)
        tm.assert_frame_equal(df.round({"col3": 1}), df)

    def test_round_issue(self):
        # GH 11611

        df = pd.DataFrame(
            np.random.random([3, 3]),
            columns=["A", "B", "C"],
            index=["first", "second", "third"],
        )

        dfs = pd.concat((df, df), axis=1)
        rounded = dfs.round()
        tm.assert_index_equal(rounded.index, dfs.index)

        decimals = pd.Series([1, 0, 2], index=["A", "B", "A"])
        msg = "Index of decimals must be unique"
        with pytest.raises(ValueError, match=msg):
            df.round(decimals)

    def test_built_in_round(self):
        # GH 11763
        # Here's the test frame we'll be working with
        df = DataFrame({"col1": [1.123, 2.123, 3.123], "col2": [1.234, 2.234, 3.234]})

        # Default round to integer (i.e. decimals=0)
        expected_rounded = DataFrame({"col1": [1.0, 2.0, 3.0], "col2": [1.0, 2.0, 3.0]})
        tm.assert_frame_equal(round(df), expected_rounded)

    def test_round_nonunique_categorical(self):
        # See GH21809
        idx = pd.CategoricalIndex(["low"] * 3 + ["hi"] * 3)
        df = pd.DataFrame(np.random.rand(6, 3), columns=list("abc"))

        expected = df.round(3)
        expected.index = idx

        df_categorical = df.copy().set_index(idx)
        assert df_categorical.shape == (6, 3)
        result = df_categorical.round(3)
        assert result.shape == (6, 3)

        tm.assert_frame_equal(result, expected)

    # ---------------------------------------------------------------------
    # Clip

    def test_clip(self, float_frame):
        median = float_frame.median().median()
        original = float_frame.copy()

        with tm.assert_produces_warning(FutureWarning):
            capped = float_frame.clip_upper(median)
        assert not (capped.values > median).any()

        with tm.assert_produces_warning(FutureWarning):
            floored = float_frame.clip_lower(median)
        assert not (floored.values < median).any()

        double = float_frame.clip(upper=median, lower=median)
        assert not (double.values != median).any()

        # Verify that float_frame was not changed inplace
        assert (float_frame.values == original.values).all()

    def test_inplace_clip(self, float_frame):
        # GH 15388
        median = float_frame.median().median()
        frame_copy = float_frame.copy()

        with tm.assert_produces_warning(FutureWarning):
            frame_copy.clip_upper(median, inplace=True)
        assert not (frame_copy.values > median).any()
        frame_copy = float_frame.copy()

        with tm.assert_produces_warning(FutureWarning):
            frame_copy.clip_lower(median, inplace=True)
        assert not (frame_copy.values < median).any()
        frame_copy = float_frame.copy()

        frame_copy.clip(upper=median, lower=median, inplace=True)
        assert not (frame_copy.values != median).any()

    def test_dataframe_clip(self):
        # GH 2747
        df = DataFrame(np.random.randn(1000, 2))

        for lb, ub in [(-1, 1), (1, -1)]:
            clipped_df = df.clip(lb, ub)

            lb, ub = min(lb, ub), max(ub, lb)
            lb_mask = df.values <= lb
            ub_mask = df.values >= ub
            mask = ~lb_mask & ~ub_mask
            assert (clipped_df.values[lb_mask] == lb).all()
            assert (clipped_df.values[ub_mask] == ub).all()
            assert (clipped_df.values[mask] == df.values[mask]).all()

    def test_clip_mixed_numeric(self):
        # TODO(jreback)
        # clip on mixed integer or floats
        # with integer clippers coerces to float
        df = DataFrame({"A": [1, 2, 3], "B": [1.0, np.nan, 3.0]})
        result = df.clip(1, 2)
        expected = DataFrame({"A": [1, 2, 2], "B": [1.0, np.nan, 2.0]})
        tm.assert_frame_equal(result, expected, check_like=True)

        # GH 24162, clipping now preserves numeric types per column
        df = DataFrame([[1, 2, 3.4], [3, 4, 5.6]], columns=["foo", "bar", "baz"])
        expected = df.dtypes
        result = df.clip(upper=3).dtypes
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("inplace", [True, False])
    def test_clip_against_series(self, inplace):
        # GH 6966

        df = DataFrame(np.random.randn(1000, 2))
        lb = Series(np.random.randn(1000))
        ub = lb + 1

        original = df.copy()
        clipped_df = df.clip(lb, ub, axis=0, inplace=inplace)

        if inplace:
            clipped_df = df

        for i in range(2):
            lb_mask = original.iloc[:, i] <= lb
            ub_mask = original.iloc[:, i] >= ub
            mask = ~lb_mask & ~ub_mask

            result = clipped_df.loc[lb_mask, i]
            tm.assert_series_equal(result, lb[lb_mask], check_names=False)
            assert result.name == i

            result = clipped_df.loc[ub_mask, i]
            tm.assert_series_equal(result, ub[ub_mask], check_names=False)
            assert result.name == i

            tm.assert_series_equal(clipped_df.loc[mask, i], df.loc[mask, i])

    @pytest.mark.parametrize("inplace", [True, False])
    @pytest.mark.parametrize("lower", [[2, 3, 4], np.asarray([2, 3, 4])])
    @pytest.mark.parametrize(
        "axis,res",
        [
            (0, [[2.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 7.0, 7.0]]),
            (1, [[2.0, 3.0, 4.0], [4.0, 5.0, 6.0], [5.0, 6.0, 7.0]]),
        ],
    )
    def test_clip_against_list_like(self, simple_frame, inplace, lower, axis, res):
        # GH 15390
        original = simple_frame.copy(deep=True)

        result = original.clip(lower=lower, upper=[5, 6, 7], axis=axis, inplace=inplace)

        expected = pd.DataFrame(res, columns=original.columns, index=original.index)
        if inplace:
            result = original
        tm.assert_frame_equal(result, expected, check_exact=True)

    @pytest.mark.parametrize("axis", [0, 1, None])
    def test_clip_against_frame(self, axis):
        df = DataFrame(np.random.randn(1000, 2))
        lb = DataFrame(np.random.randn(1000, 2))
        ub = lb + 1

        clipped_df = df.clip(lb, ub, axis=axis)

        lb_mask = df <= lb
        ub_mask = df >= ub
        mask = ~lb_mask & ~ub_mask

        tm.assert_frame_equal(clipped_df[lb_mask], lb[lb_mask])
        tm.assert_frame_equal(clipped_df[ub_mask], ub[ub_mask])
        tm.assert_frame_equal(clipped_df[mask], df[mask])

    def test_clip_against_unordered_columns(self):
        # GH 20911
        df1 = DataFrame(np.random.randn(1000, 4), columns=["A", "B", "C", "D"])
        df2 = DataFrame(np.random.randn(1000, 4), columns=["D", "A", "B", "C"])
        df3 = DataFrame(df2.values - 1, columns=["B", "D", "C", "A"])
        result_upper = df1.clip(lower=0, upper=df2)
        expected_upper = df1.clip(lower=0, upper=df2[df1.columns])
        result_lower = df1.clip(lower=df3, upper=3)
        expected_lower = df1.clip(lower=df3[df1.columns], upper=3)
        result_lower_upper = df1.clip(lower=df3, upper=df2)
        expected_lower_upper = df1.clip(lower=df3[df1.columns], upper=df2[df1.columns])
        tm.assert_frame_equal(result_upper, expected_upper)
        tm.assert_frame_equal(result_lower, expected_lower)
        tm.assert_frame_equal(result_lower_upper, expected_lower_upper)

    def test_clip_with_na_args(self, float_frame):
        """Should process np.nan argument as None """
        # GH 17276
        tm.assert_frame_equal(float_frame.clip(np.nan), float_frame)
        tm.assert_frame_equal(float_frame.clip(upper=np.nan, lower=np.nan), float_frame)

        # GH 19992
        df = DataFrame({"col_0": [1, 2, 3], "col_1": [4, 5, 6], "col_2": [7, 8, 9]})

        result = df.clip(lower=[4, 5, np.nan], axis=0)
        expected = DataFrame(
            {"col_0": [4, 5, np.nan], "col_1": [4, 5, np.nan], "col_2": [7, 8, np.nan]}
        )
        tm.assert_frame_equal(result, expected)

        result = df.clip(lower=[4, 5, np.nan], axis=1)
        expected = DataFrame(
            {"col_0": [4, 4, 4], "col_1": [5, 5, 6], "col_2": [np.nan, np.nan, np.nan]}
        )
        tm.assert_frame_equal(result, expected)

    # ---------------------------------------------------------------------
    # Matrix-like

    def test_dot(self):
        a = DataFrame(
            np.random.randn(3, 4), index=["a", "b", "c"], columns=["p", "q", "r", "s"]
        )
        b = DataFrame(
            np.random.randn(4, 2), index=["p", "q", "r", "s"], columns=["one", "two"]
        )

        result = a.dot(b)
        expected = DataFrame(
            np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
        )
        # Check alignment
        b1 = b.reindex(index=reversed(b.index))
        result = a.dot(b)
        tm.assert_frame_equal(result, expected)

        # Check series argument
        result = a.dot(b["one"])
        tm.assert_series_equal(result, expected["one"], check_names=False)
        assert result.name is None

        result = a.dot(b1["one"])
        tm.assert_series_equal(result, expected["one"], check_names=False)
        assert result.name is None

        # can pass correct-length arrays
        row = a.iloc[0].values

        result = a.dot(row)
        expected = a.dot(a.iloc[0])
        tm.assert_series_equal(result, expected)

        with pytest.raises(ValueError, match="Dot product shape mismatch"):
            a.dot(row[:-1])

        a = np.random.rand(1, 5)
        b = np.random.rand(5, 1)
        A = DataFrame(a)

        # TODO(wesm): unused
        B = DataFrame(b)  # noqa

        # it works
        result = A.dot(b)

        # unaligned
        df = DataFrame(np.random.randn(3, 4), index=[1, 2, 3], columns=range(4))
        df2 = DataFrame(np.random.randn(5, 3), index=range(5), columns=[1, 2, 3])

        with pytest.raises(ValueError, match="aligned"):
            df.dot(df2)

    def test_matmul(self):
        # matmul test is for GH 10259
        a = DataFrame(
            np.random.randn(3, 4), index=["a", "b", "c"], columns=["p", "q", "r", "s"]
        )
        b = DataFrame(
            np.random.randn(4, 2), index=["p", "q", "r", "s"], columns=["one", "two"]
        )

        # DataFrame @ DataFrame
        result = operator.matmul(a, b)
        expected = DataFrame(
            np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
        )
        tm.assert_frame_equal(result, expected)

        # DataFrame @ Series
        result = operator.matmul(a, b.one)
        expected = Series(np.dot(a.values, b.one.values), index=["a", "b", "c"])
        tm.assert_series_equal(result, expected)

        # np.array @ DataFrame
        result = operator.matmul(a.values, b)
        assert isinstance(result, DataFrame)
        assert result.columns.equals(b.columns)
        assert result.index.equals(pd.Index(range(3)))
        expected = np.dot(a.values, b.values)
        tm.assert_almost_equal(result.values, expected)

        # nested list @ DataFrame (__rmatmul__)
        result = operator.matmul(a.values.tolist(), b)
        expected = DataFrame(
            np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
        )
        tm.assert_almost_equal(result.values, expected.values)

        # mixed dtype DataFrame @ DataFrame
        a["q"] = a.q.round().astype(int)
        result = operator.matmul(a, b)
        expected = DataFrame(
            np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
        )
        tm.assert_frame_equal(result, expected)

        # different dtypes DataFrame @ DataFrame
        a = a.astype(int)
        result = operator.matmul(a, b)
        expected = DataFrame(
            np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
        )
        tm.assert_frame_equal(result, expected)

        # unaligned
        df = DataFrame(np.random.randn(3, 4), index=[1, 2, 3], columns=range(4))
        df2 = DataFrame(np.random.randn(5, 3), index=range(5), columns=[1, 2, 3])

        with pytest.raises(ValueError, match="aligned"):
            operator.matmul(df, df2)


@pytest.fixture
def df_duplicates():
    return pd.DataFrame(
        {"a": [1, 2, 3, 4, 4], "b": [1, 1, 1, 1, 1], "c": [0, 1, 2, 5, 4]},
        index=[0, 0, 1, 1, 1],
    )


@pytest.fixture
def df_strings():
    return pd.DataFrame(
        {
            "a": np.random.permutation(10),
            "b": list(ascii_lowercase[:10]),
            "c": np.random.permutation(10).astype("float64"),
        }
    )


@pytest.fixture
def df_main_dtypes():
    return pd.DataFrame(
        {
            "group": [1, 1, 2],
            "int": [1, 2, 3],
            "float": [4.0, 5.0, 6.0],
            "string": list("abc"),
            "category_string": pd.Series(list("abc")).astype("category"),
            "category_int": [7, 8, 9],
            "datetime": pd.date_range("20130101", periods=3),
            "datetimetz": pd.date_range("20130101", periods=3, tz="US/Eastern"),
            "timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
        },
        columns=[
            "group",
            "int",
            "float",
            "string",
            "category_string",
            "category_int",
            "datetime",
            "datetimetz",
            "timedelta",
        ],
    )


class TestNLargestNSmallest:

    dtype_error_msg_template = (
        "Column {column!r} has dtype {dtype}, cannot "
        "use method {method!r} with this dtype"
    )

    # ----------------------------------------------------------------------
    # Top / bottom
    @pytest.mark.parametrize(
        "order",
        [
            ["a"],
            ["c"],
            ["a", "b"],
            ["a", "c"],
            ["b", "a"],
            ["b", "c"],
            ["a", "b", "c"],
            ["c", "a", "b"],
            ["c", "b", "a"],
            ["b", "c", "a"],
            ["b", "a", "c"],
            # dups!
            ["b", "c", "c"],
        ],
    )
    @pytest.mark.parametrize("n", range(1, 11))
    def test_n(self, df_strings, nselect_method, n, order):
        # GH 10393
        df = df_strings
        if "b" in order:

            error_msg = self.dtype_error_msg_template.format(
                column="b", method=nselect_method, dtype="object"
            )
            with pytest.raises(TypeError, match=error_msg):
                getattr(df, nselect_method)(n, order)
        else:
            ascending = nselect_method == "nsmallest"
            result = getattr(df, nselect_method)(n, order)
            expected = df.sort_values(order, ascending=ascending).head(n)
            tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        "columns", [["group", "category_string"], ["group", "string"]]
    )
    def test_n_error(self, df_main_dtypes, nselect_method, columns):
        df = df_main_dtypes
        col = columns[1]
        error_msg = self.dtype_error_msg_template.format(
            column=col, method=nselect_method, dtype=df[col].dtype
        )
        # escape some characters that may be in the repr
        error_msg = (
            error_msg.replace("(", "\\(")
            .replace(")", "\\)")
            .replace("[", "\\[")
            .replace("]", "\\]")
        )
        with pytest.raises(TypeError, match=error_msg):
            getattr(df, nselect_method)(2, columns)

    def test_n_all_dtypes(self, df_main_dtypes):
        df = df_main_dtypes
        df.nsmallest(2, list(set(df) - {"category_string", "string"}))
        df.nlargest(2, list(set(df) - {"category_string", "string"}))

    @pytest.mark.parametrize(
        "method,expected",
        [
            (
                "nlargest",
                pd.DataFrame(
                    {"a": [2, 2, 2, 1], "b": [3, 2, 1, 3]}, index=[2, 1, 0, 3]
                ),
            ),
            (
                "nsmallest",
                pd.DataFrame(
                    {"a": [1, 1, 1, 2], "b": [1, 2, 3, 1]}, index=[5, 4, 3, 0]
                ),
            ),
        ],
    )
    def test_duplicates_on_starter_columns(self, method, expected):
        # regression test for #22752

        df = pd.DataFrame({"a": [2, 2, 2, 1, 1, 1], "b": [1, 2, 3, 3, 2, 1]})

        result = getattr(df, method)(4, columns=["a", "b"])
        tm.assert_frame_equal(result, expected)

    def test_n_identical_values(self):
        # GH 15297
        df = pd.DataFrame({"a": [1] * 5, "b": [1, 2, 3, 4, 5]})

        result = df.nlargest(3, "a")
        expected = pd.DataFrame({"a": [1] * 3, "b": [1, 2, 3]}, index=[0, 1, 2])
        tm.assert_frame_equal(result, expected)

        result = df.nsmallest(3, "a")
        expected = pd.DataFrame({"a": [1] * 3, "b": [1, 2, 3]})
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        "order",
        [["a", "b", "c"], ["c", "b", "a"], ["a"], ["b"], ["a", "b"], ["c", "b"]],
    )
    @pytest.mark.parametrize("n", range(1, 6))
    def test_n_duplicate_index(self, df_duplicates, n, order):
        # GH 13412

        df = df_duplicates
        result = df.nsmallest(n, order)
        expected = df.sort_values(order).head(n)
        tm.assert_frame_equal(result, expected)

        result = df.nlargest(n, order)
        expected = df.sort_values(order, ascending=False).head(n)
        tm.assert_frame_equal(result, expected)

    def test_duplicate_keep_all_ties(self):
        # GH 16818
        df = pd.DataFrame(
            {"a": [5, 4, 4, 2, 3, 3, 3, 3], "b": [10, 9, 8, 7, 5, 50, 10, 20]}
        )
        result = df.nlargest(4, "a", keep="all")
        expected = pd.DataFrame(
            {
                "a": {0: 5, 1: 4, 2: 4, 4: 3, 5: 3, 6: 3, 7: 3},
                "b": {0: 10, 1: 9, 2: 8, 4: 5, 5: 50, 6: 10, 7: 20},
            }
        )
        tm.assert_frame_equal(result, expected)

        result = df.nsmallest(2, "a", keep="all")
        expected = pd.DataFrame(
            {
                "a": {3: 2, 4: 3, 5: 3, 6: 3, 7: 3},
                "b": {3: 7, 4: 5, 5: 50, 6: 10, 7: 20},
            }
        )
        tm.assert_frame_equal(result, expected)

    def test_series_broadcasting(self):
        # smoke test for numpy warnings
        # GH 16378, GH 16306
        df = DataFrame([1.0, 1.0, 1.0])
        df_nan = DataFrame({"A": [np.nan, 2.0, np.nan]})
        s = Series([1, 1, 1])
        s_nan = Series([np.nan, np.nan, 1])

        with tm.assert_produces_warning(None):
            with tm.assert_produces_warning(FutureWarning):
                df_nan.clip_lower(s, axis=0)
            for op in ["lt", "le", "gt", "ge", "eq", "ne"]:
                getattr(df, op)(s_nan, axis=0)

    def test_series_nat_conversion(self):
        # GH 18521
        # Check rank does not mutate DataFrame
        df = DataFrame(np.random.randn(10, 3), dtype="float64")
        expected = df.copy()
        df.rank()
        result = df
        tm.assert_frame_equal(result, expected)

    def test_multiindex_column_lookup(self):
        # Check whether tuples are correctly treated as multi-level lookups.
        # GH 23033
        df = pd.DataFrame(
            columns=pd.MultiIndex.from_product([["x"], ["a", "b"]]),
            data=[[0.33, 0.13], [0.86, 0.25], [0.25, 0.70], [0.85, 0.91]],
        )

        # nsmallest
        result = df.nsmallest(3, ("x", "a"))
        expected = df.iloc[[2, 0, 3]]
        tm.assert_frame_equal(result, expected)

        # nlargest
        result = df.nlargest(3, ("x", "b"))
        expected = df.iloc[[3, 2, 1]]
        tm.assert_frame_equal(result, expected)
