import numpy as np
import pytest
import pytz

import pandas as pd
from pandas import Timedelta, merge_asof, read_csv, to_datetime
from pandas.core.reshape.merge import MergeError
from pandas.util.testing import assert_frame_equal


class TestAsOfMerge:
    def read_data(self, datapath, name, dedupe=False):
        path = datapath("reshape", "merge", "data", name)
        x = read_csv(path)
        if dedupe:
            x = x.drop_duplicates(["time", "ticker"], keep="last").reset_index(
                drop=True
            )
        x.time = to_datetime(x.time)
        return x

    @pytest.fixture(autouse=True)
    def setup_method(self, datapath):

        self.trades = self.read_data(datapath, "trades.csv")
        self.quotes = self.read_data(datapath, "quotes.csv", dedupe=True)
        self.asof = self.read_data(datapath, "asof.csv")
        self.tolerance = self.read_data(datapath, "tolerance.csv")
        self.allow_exact_matches = self.read_data(datapath, "allow_exact_matches.csv")
        self.allow_exact_matches_and_tolerance = self.read_data(
            datapath, "allow_exact_matches_and_tolerance.csv"
        )

    def test_examples1(self):
        """ doc-string examples """

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 3, 7]}
        )

        result = pd.merge_asof(left, right, on="a")
        assert_frame_equal(result, expected)

    def test_examples2(self):
        """ doc-string examples """

        trades = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.038",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.048",
                    ]
                ),
                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
                "quantity": [75, 155, 100, 100, 100],
            },
            columns=["time", "ticker", "price", "quantity"],
        )

        quotes = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.030",
                        "20160525 13:30:00.041",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.049",
                        "20160525 13:30:00.072",
                        "20160525 13:30:00.075",
                    ]
                ),
                "ticker": [
                    "GOOG",
                    "MSFT",
                    "MSFT",
                    "MSFT",
                    "GOOG",
                    "AAPL",
                    "GOOG",
                    "MSFT",
                ],
                "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
                "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03],
            },
            columns=["time", "ticker", "bid", "ask"],
        )

        pd.merge_asof(trades, quotes, on="time", by="ticker")

        pd.merge_asof(
            trades, quotes, on="time", by="ticker", tolerance=pd.Timedelta("2ms")
        )

        expected = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.038",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.048",
                    ]
                ),
                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
                "quantity": [75, 155, 100, 100, 100],
                "bid": [np.nan, 51.97, np.nan, np.nan, np.nan],
                "ask": [np.nan, 51.98, np.nan, np.nan, np.nan],
            },
            columns=["time", "ticker", "price", "quantity", "bid", "ask"],
        )

        result = pd.merge_asof(
            trades,
            quotes,
            on="time",
            by="ticker",
            tolerance=pd.Timedelta("10ms"),
            allow_exact_matches=False,
        )
        assert_frame_equal(result, expected)

    def test_examples3(self):
        """ doc-string examples """
        # GH14887

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 6, np.nan]}
        )

        result = pd.merge_asof(left, right, on="a", direction="forward")
        assert_frame_equal(result, expected)

    def test_examples4(self):
        """ doc-string examples """
        # GH14887

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 6, 7]}
        )

        result = pd.merge_asof(left, right, on="a", direction="nearest")
        assert_frame_equal(result, expected)

    def test_basic(self):

        expected = self.asof
        trades = self.trades
        quotes = self.quotes

        result = merge_asof(trades, quotes, on="time", by="ticker")
        assert_frame_equal(result, expected)

    def test_basic_categorical(self):

        expected = self.asof
        trades = self.trades.copy()
        trades.ticker = trades.ticker.astype("category")
        quotes = self.quotes.copy()
        quotes.ticker = quotes.ticker.astype("category")
        expected.ticker = expected.ticker.astype("category")

        result = merge_asof(trades, quotes, on="time", by="ticker")
        assert_frame_equal(result, expected)

    def test_basic_left_index(self):

        # GH14253
        expected = self.asof
        trades = self.trades.set_index("time")
        quotes = self.quotes

        result = merge_asof(
            trades, quotes, left_index=True, right_on="time", by="ticker"
        )
        # left-only index uses right"s index, oddly
        expected.index = result.index
        # time column appears after left"s columns
        expected = expected[result.columns]
        assert_frame_equal(result, expected)

    def test_basic_right_index(self):

        expected = self.asof
        trades = self.trades
        quotes = self.quotes.set_index("time")

        result = merge_asof(
            trades, quotes, left_on="time", right_index=True, by="ticker"
        )
        assert_frame_equal(result, expected)

    def test_basic_left_index_right_index(self):

        expected = self.asof.set_index("time")
        trades = self.trades.set_index("time")
        quotes = self.quotes.set_index("time")

        result = merge_asof(
            trades, quotes, left_index=True, right_index=True, by="ticker"
        )
        assert_frame_equal(result, expected)

    def test_multi_index(self):

        # MultiIndex is prohibited
        trades = self.trades.set_index(["time", "price"])
        quotes = self.quotes.set_index("time")
        with pytest.raises(MergeError):
            merge_asof(trades, quotes, left_index=True, right_index=True)

        trades = self.trades.set_index("time")
        quotes = self.quotes.set_index(["time", "bid"])
        with pytest.raises(MergeError):
            merge_asof(trades, quotes, left_index=True, right_index=True)

    def test_on_and_index(self):

        # "on" parameter and index together is prohibited
        trades = self.trades.set_index("time")
        quotes = self.quotes.set_index("time")
        with pytest.raises(MergeError):
            merge_asof(
                trades, quotes, left_on="price", left_index=True, right_index=True
            )

        trades = self.trades.set_index("time")
        quotes = self.quotes.set_index("time")
        with pytest.raises(MergeError):
            merge_asof(
                trades, quotes, right_on="bid", left_index=True, right_index=True
            )

    def test_basic_left_by_right_by(self):

        # GH14253
        expected = self.asof
        trades = self.trades
        quotes = self.quotes

        result = merge_asof(
            trades, quotes, on="time", left_by="ticker", right_by="ticker"
        )
        assert_frame_equal(result, expected)

    def test_missing_right_by(self):

        expected = self.asof
        trades = self.trades
        quotes = self.quotes

        q = quotes[quotes.ticker != "MSFT"]
        result = merge_asof(trades, q, on="time", by="ticker")
        expected.loc[expected.ticker == "MSFT", ["bid", "ask"]] = np.nan
        assert_frame_equal(result, expected)

    def test_multiby(self):
        # GH13936
        trades = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.046",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.050",
                    ]
                ),
                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
                "quantity": [75, 155, 100, 100, 100],
            },
            columns=["time", "ticker", "exch", "price", "quantity"],
        )

        quotes = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.030",
                        "20160525 13:30:00.041",
                        "20160525 13:30:00.045",
                        "20160525 13:30:00.049",
                    ]
                ),
                "ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG", "AAPL"],
                "exch": ["BATS", "NSDQ", "ARCA", "ARCA", "NSDQ", "ARCA"],
                "bid": [720.51, 51.95, 51.97, 51.99, 720.50, 97.99],
                "ask": [720.92, 51.96, 51.98, 52.00, 720.93, 98.01],
            },
            columns=["time", "ticker", "exch", "bid", "ask"],
        )

        expected = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.046",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.050",
                    ]
                ),
                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
                "quantity": [75, 155, 100, 100, 100],
                "bid": [np.nan, 51.95, 720.50, 720.51, np.nan],
                "ask": [np.nan, 51.96, 720.93, 720.92, np.nan],
            },
            columns=["time", "ticker", "exch", "price", "quantity", "bid", "ask"],
        )

        result = pd.merge_asof(trades, quotes, on="time", by=["ticker", "exch"])
        assert_frame_equal(result, expected)

    def test_multiby_heterogeneous_types(self):
        # GH13936
        trades = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.046",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.050",
                    ]
                ),
                "ticker": [0, 0, 1, 1, 2],
                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
                "quantity": [75, 155, 100, 100, 100],
            },
            columns=["time", "ticker", "exch", "price", "quantity"],
        )

        quotes = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.030",
                        "20160525 13:30:00.041",
                        "20160525 13:30:00.045",
                        "20160525 13:30:00.049",
                    ]
                ),
                "ticker": [1, 0, 0, 0, 1, 2],
                "exch": ["BATS", "NSDQ", "ARCA", "ARCA", "NSDQ", "ARCA"],
                "bid": [720.51, 51.95, 51.97, 51.99, 720.50, 97.99],
                "ask": [720.92, 51.96, 51.98, 52.00, 720.93, 98.01],
            },
            columns=["time", "ticker", "exch", "bid", "ask"],
        )

        expected = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.023",
                        "20160525 13:30:00.046",
                        "20160525 13:30:00.048",
                        "20160525 13:30:00.050",
                    ]
                ),
                "ticker": [0, 0, 1, 1, 2],
                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
                "quantity": [75, 155, 100, 100, 100],
                "bid": [np.nan, 51.95, 720.50, 720.51, np.nan],
                "ask": [np.nan, 51.96, 720.93, 720.92, np.nan],
            },
            columns=["time", "ticker", "exch", "price", "quantity", "bid", "ask"],
        )

        result = pd.merge_asof(trades, quotes, on="time", by=["ticker", "exch"])
        assert_frame_equal(result, expected)

    def test_multiby_indexed(self):
        # GH15676
        left = pd.DataFrame(
            [
                [pd.to_datetime("20160602"), 1, "a"],
                [pd.to_datetime("20160602"), 2, "a"],
                [pd.to_datetime("20160603"), 1, "b"],
                [pd.to_datetime("20160603"), 2, "b"],
            ],
            columns=["time", "k1", "k2"],
        ).set_index("time")

        right = pd.DataFrame(
            [
                [pd.to_datetime("20160502"), 1, "a", 1.0],
                [pd.to_datetime("20160502"), 2, "a", 2.0],
                [pd.to_datetime("20160503"), 1, "b", 3.0],
                [pd.to_datetime("20160503"), 2, "b", 4.0],
            ],
            columns=["time", "k1", "k2", "value"],
        ).set_index("time")

        expected = pd.DataFrame(
            [
                [pd.to_datetime("20160602"), 1, "a", 1.0],
                [pd.to_datetime("20160602"), 2, "a", 2.0],
                [pd.to_datetime("20160603"), 1, "b", 3.0],
                [pd.to_datetime("20160603"), 2, "b", 4.0],
            ],
            columns=["time", "k1", "k2", "value"],
        ).set_index("time")

        result = pd.merge_asof(
            left, right, left_index=True, right_index=True, by=["k1", "k2"]
        )

        assert_frame_equal(expected, result)

        with pytest.raises(MergeError):
            pd.merge_asof(
                left,
                right,
                left_index=True,
                right_index=True,
                left_by=["k1", "k2"],
                right_by=["k1"],
            )

    def test_basic2(self, datapath):

        expected = self.read_data(datapath, "asof2.csv")
        trades = self.read_data(datapath, "trades2.csv")
        quotes = self.read_data(datapath, "quotes2.csv", dedupe=True)

        result = merge_asof(trades, quotes, on="time", by="ticker")
        assert_frame_equal(result, expected)

    def test_basic_no_by(self):
        f = (
            lambda x: x[x.ticker == "MSFT"]
            .drop("ticker", axis=1)
            .reset_index(drop=True)
        )

        # just use a single ticker
        expected = f(self.asof)
        trades = f(self.trades)
        quotes = f(self.quotes)

        result = merge_asof(trades, quotes, on="time")
        assert_frame_equal(result, expected)

    def test_valid_join_keys(self):

        trades = self.trades
        quotes = self.quotes

        with pytest.raises(MergeError):
            merge_asof(trades, quotes, left_on="time", right_on="bid", by="ticker")

        with pytest.raises(MergeError):
            merge_asof(trades, quotes, on=["time", "ticker"], by="ticker")

        with pytest.raises(MergeError):
            merge_asof(trades, quotes, by="ticker")

    def test_with_duplicates(self, datapath):

        q = (
            pd.concat([self.quotes, self.quotes])
            .sort_values(["time", "ticker"])
            .reset_index(drop=True)
        )
        result = merge_asof(self.trades, q, on="time", by="ticker")
        expected = self.read_data(datapath, "asof.csv")
        assert_frame_equal(result, expected)

    def test_with_duplicates_no_on(self):

        df1 = pd.DataFrame({"key": [1, 1, 3], "left_val": [1, 2, 3]})
        df2 = pd.DataFrame({"key": [1, 2, 2], "right_val": [1, 2, 3]})
        result = merge_asof(df1, df2, on="key")
        expected = pd.DataFrame(
            {"key": [1, 1, 3], "left_val": [1, 2, 3], "right_val": [1, 1, 3]}
        )
        assert_frame_equal(result, expected)

    def test_valid_allow_exact_matches(self):

        trades = self.trades
        quotes = self.quotes

        with pytest.raises(MergeError):
            merge_asof(
                trades, quotes, on="time", by="ticker", allow_exact_matches="foo"
            )

    def test_valid_tolerance(self):

        trades = self.trades
        quotes = self.quotes

        # dti
        merge_asof(trades, quotes, on="time", by="ticker", tolerance=Timedelta("1s"))

        # integer
        merge_asof(
            trades.reset_index(),
            quotes.reset_index(),
            on="index",
            by="ticker",
            tolerance=1,
        )

        # incompat
        with pytest.raises(MergeError):
            merge_asof(trades, quotes, on="time", by="ticker", tolerance=1)

        # invalid
        with pytest.raises(MergeError):
            merge_asof(
                trades.reset_index(),
                quotes.reset_index(),
                on="index",
                by="ticker",
                tolerance=1.0,
            )

        # invalid negative
        with pytest.raises(MergeError):
            merge_asof(
                trades, quotes, on="time", by="ticker", tolerance=-Timedelta("1s")
            )

        with pytest.raises(MergeError):
            merge_asof(
                trades.reset_index(),
                quotes.reset_index(),
                on="index",
                by="ticker",
                tolerance=-1,
            )

    def test_non_sorted(self):

        trades = self.trades.sort_values("time", ascending=False)
        quotes = self.quotes.sort_values("time", ascending=False)

        # we require that we are already sorted on time & quotes
        assert not trades.time.is_monotonic
        assert not quotes.time.is_monotonic
        with pytest.raises(ValueError):
            merge_asof(trades, quotes, on="time", by="ticker")

        trades = self.trades.sort_values("time")
        assert trades.time.is_monotonic
        assert not quotes.time.is_monotonic
        with pytest.raises(ValueError):
            merge_asof(trades, quotes, on="time", by="ticker")

        quotes = self.quotes.sort_values("time")
        assert trades.time.is_monotonic
        assert quotes.time.is_monotonic

        # ok, though has dupes
        merge_asof(trades, self.quotes, on="time", by="ticker")

    def test_tolerance(self):

        trades = self.trades
        quotes = self.quotes

        result = merge_asof(
            trades, quotes, on="time", by="ticker", tolerance=Timedelta("1day")
        )
        expected = self.tolerance
        assert_frame_equal(result, expected)

    def test_tolerance_forward(self):
        # GH14887

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, np.nan, 11]}
        )

        result = pd.merge_asof(left, right, on="a", direction="forward", tolerance=1)
        assert_frame_equal(result, expected)

    def test_tolerance_nearest(self):
        # GH14887

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, np.nan, 11]}
        )

        result = pd.merge_asof(left, right, on="a", direction="nearest", tolerance=1)
        assert_frame_equal(result, expected)

    def test_tolerance_tz(self):
        # GH 14844
        left = pd.DataFrame(
            {
                "date": pd.date_range(
                    start=pd.to_datetime("2016-01-02"),
                    freq="D",
                    periods=5,
                    tz=pytz.timezone("UTC"),
                ),
                "value1": np.arange(5),
            }
        )
        right = pd.DataFrame(
            {
                "date": pd.date_range(
                    start=pd.to_datetime("2016-01-01"),
                    freq="D",
                    periods=5,
                    tz=pytz.timezone("UTC"),
                ),
                "value2": list("ABCDE"),
            }
        )
        result = pd.merge_asof(left, right, on="date", tolerance=pd.Timedelta("1 day"))

        expected = pd.DataFrame(
            {
                "date": pd.date_range(
                    start=pd.to_datetime("2016-01-02"),
                    freq="D",
                    periods=5,
                    tz=pytz.timezone("UTC"),
                ),
                "value1": np.arange(5),
                "value2": list("BCDEE"),
            }
        )
        assert_frame_equal(result, expected)

    def test_tolerance_float(self):
        # GH22981
        left = pd.DataFrame({"a": [1.1, 3.5, 10.9], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame(
            {"a": [1.0, 2.5, 3.3, 7.5, 11.5], "right_val": [1.0, 2.5, 3.3, 7.5, 11.5]}
        )

        expected = pd.DataFrame(
            {
                "a": [1.1, 3.5, 10.9],
                "left_val": ["a", "b", "c"],
                "right_val": [1, 3.3, np.nan],
            }
        )

        result = pd.merge_asof(left, right, on="a", direction="nearest", tolerance=0.5)
        assert_frame_equal(result, expected)

    def test_index_tolerance(self):
        # GH 15135
        expected = self.tolerance.set_index("time")
        trades = self.trades.set_index("time")
        quotes = self.quotes.set_index("time")

        result = pd.merge_asof(
            trades,
            quotes,
            left_index=True,
            right_index=True,
            by="ticker",
            tolerance=pd.Timedelta("1day"),
        )
        assert_frame_equal(result, expected)

    def test_allow_exact_matches(self):

        result = merge_asof(
            self.trades, self.quotes, on="time", by="ticker", allow_exact_matches=False
        )
        expected = self.allow_exact_matches
        assert_frame_equal(result, expected)

    def test_allow_exact_matches_forward(self):
        # GH14887

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [2, 7, 11]}
        )

        result = pd.merge_asof(
            left, right, on="a", direction="forward", allow_exact_matches=False
        )
        assert_frame_equal(result, expected)

    def test_allow_exact_matches_nearest(self):
        # GH14887

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [2, 3, 11]}
        )

        result = pd.merge_asof(
            left, right, on="a", direction="nearest", allow_exact_matches=False
        )
        assert_frame_equal(result, expected)

    def test_allow_exact_matches_and_tolerance(self):

        result = merge_asof(
            self.trades,
            self.quotes,
            on="time",
            by="ticker",
            tolerance=Timedelta("100ms"),
            allow_exact_matches=False,
        )
        expected = self.allow_exact_matches_and_tolerance
        assert_frame_equal(result, expected)

    def test_allow_exact_matches_and_tolerance2(self):
        # GH 13695
        df1 = pd.DataFrame(
            {"time": pd.to_datetime(["2016-07-15 13:30:00.030"]), "username": ["bob"]}
        )
        df2 = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    ["2016-07-15 13:30:00.000", "2016-07-15 13:30:00.030"]
                ),
                "version": [1, 2],
            }
        )

        result = pd.merge_asof(df1, df2, on="time")
        expected = pd.DataFrame(
            {
                "time": pd.to_datetime(["2016-07-15 13:30:00.030"]),
                "username": ["bob"],
                "version": [2],
            }
        )
        assert_frame_equal(result, expected)

        result = pd.merge_asof(df1, df2, on="time", allow_exact_matches=False)
        expected = pd.DataFrame(
            {
                "time": pd.to_datetime(["2016-07-15 13:30:00.030"]),
                "username": ["bob"],
                "version": [1],
            }
        )
        assert_frame_equal(result, expected)

        result = pd.merge_asof(
            df1,
            df2,
            on="time",
            allow_exact_matches=False,
            tolerance=pd.Timedelta("10ms"),
        )
        expected = pd.DataFrame(
            {
                "time": pd.to_datetime(["2016-07-15 13:30:00.030"]),
                "username": ["bob"],
                "version": [np.nan],
            }
        )
        assert_frame_equal(result, expected)

    def test_allow_exact_matches_and_tolerance3(self):
        # GH 13709
        df1 = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    ["2016-07-15 13:30:00.030", "2016-07-15 13:30:00.030"]
                ),
                "username": ["bob", "charlie"],
            }
        )
        df2 = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    ["2016-07-15 13:30:00.000", "2016-07-15 13:30:00.030"]
                ),
                "version": [1, 2],
            }
        )

        result = pd.merge_asof(
            df1,
            df2,
            on="time",
            allow_exact_matches=False,
            tolerance=pd.Timedelta("10ms"),
        )
        expected = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    ["2016-07-15 13:30:00.030", "2016-07-15 13:30:00.030"]
                ),
                "username": ["bob", "charlie"],
                "version": [np.nan, np.nan],
            }
        )
        assert_frame_equal(result, expected)

    def test_allow_exact_matches_and_tolerance_forward(self):
        # GH14887

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 3, 4, 6, 11], "right_val": [1, 3, 4, 6, 11]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [np.nan, 6, 11]}
        )

        result = pd.merge_asof(
            left,
            right,
            on="a",
            direction="forward",
            allow_exact_matches=False,
            tolerance=1,
        )
        assert_frame_equal(result, expected)

    def test_allow_exact_matches_and_tolerance_nearest(self):
        # GH14887

        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
        right = pd.DataFrame({"a": [1, 3, 4, 6, 11], "right_val": [1, 3, 4, 7, 11]})

        expected = pd.DataFrame(
            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [np.nan, 4, 11]}
        )

        result = pd.merge_asof(
            left,
            right,
            on="a",
            direction="nearest",
            allow_exact_matches=False,
            tolerance=1,
        )
        assert_frame_equal(result, expected)

    def test_forward_by(self):
        # GH14887

        left = pd.DataFrame(
            {
                "a": [1, 5, 10, 12, 15],
                "b": ["X", "X", "Y", "Z", "Y"],
                "left_val": ["a", "b", "c", "d", "e"],
            }
        )
        right = pd.DataFrame(
            {
                "a": [1, 6, 11, 15, 16],
                "b": ["X", "Z", "Y", "Z", "Y"],
                "right_val": [1, 6, 11, 15, 16],
            }
        )

        expected = pd.DataFrame(
            {
                "a": [1, 5, 10, 12, 15],
                "b": ["X", "X", "Y", "Z", "Y"],
                "left_val": ["a", "b", "c", "d", "e"],
                "right_val": [1, np.nan, 11, 15, 16],
            }
        )

        result = pd.merge_asof(left, right, on="a", by="b", direction="forward")
        assert_frame_equal(result, expected)

    def test_nearest_by(self):
        # GH14887

        left = pd.DataFrame(
            {
                "a": [1, 5, 10, 12, 15],
                "b": ["X", "X", "Z", "Z", "Y"],
                "left_val": ["a", "b", "c", "d", "e"],
            }
        )
        right = pd.DataFrame(
            {
                "a": [1, 6, 11, 15, 16],
                "b": ["X", "Z", "Z", "Z", "Y"],
                "right_val": [1, 6, 11, 15, 16],
            }
        )

        expected = pd.DataFrame(
            {
                "a": [1, 5, 10, 12, 15],
                "b": ["X", "X", "Z", "Z", "Y"],
                "left_val": ["a", "b", "c", "d", "e"],
                "right_val": [1, 1, 11, 11, 16],
            }
        )

        result = pd.merge_asof(left, right, on="a", by="b", direction="nearest")
        assert_frame_equal(result, expected)

    def test_by_int(self):
        # we specialize by type, so test that this is correct
        df1 = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.020",
                        "20160525 13:30:00.030",
                        "20160525 13:30:00.040",
                        "20160525 13:30:00.050",
                        "20160525 13:30:00.060",
                    ]
                ),
                "key": [1, 2, 1, 3, 2],
                "value1": [1.1, 1.2, 1.3, 1.4, 1.5],
            },
            columns=["time", "key", "value1"],
        )

        df2 = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.015",
                        "20160525 13:30:00.020",
                        "20160525 13:30:00.025",
                        "20160525 13:30:00.035",
                        "20160525 13:30:00.040",
                        "20160525 13:30:00.055",
                        "20160525 13:30:00.060",
                        "20160525 13:30:00.065",
                    ]
                ),
                "key": [2, 1, 1, 3, 2, 1, 2, 3],
                "value2": [2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8],
            },
            columns=["time", "key", "value2"],
        )

        result = pd.merge_asof(df1, df2, on="time", by="key")

        expected = pd.DataFrame(
            {
                "time": pd.to_datetime(
                    [
                        "20160525 13:30:00.020",
                        "20160525 13:30:00.030",
                        "20160525 13:30:00.040",
                        "20160525 13:30:00.050",
                        "20160525 13:30:00.060",
                    ]
                ),
                "key": [1, 2, 1, 3, 2],
                "value1": [1.1, 1.2, 1.3, 1.4, 1.5],
                "value2": [2.2, 2.1, 2.3, 2.4, 2.7],
            },
            columns=["time", "key", "value1", "value2"],
        )

        assert_frame_equal(result, expected)

    def test_on_float(self):
        # mimics how to determine the minimum-price variation
        df1 = pd.DataFrame(
            {
                "price": [5.01, 0.0023, 25.13, 340.05, 30.78, 1040.90, 0.0078],
                "symbol": list("ABCDEFG"),
            },
            columns=["symbol", "price"],
        )

        df2 = pd.DataFrame(
            {"price": [0.0, 1.0, 100.0], "mpv": [0.0001, 0.01, 0.05]},
            columns=["price", "mpv"],
        )

        df1 = df1.sort_values("price").reset_index(drop=True)

        result = pd.merge_asof(df1, df2, on="price")

        expected = pd.DataFrame(
            {
                "symbol": list("BGACEDF"),
                "price": [0.0023, 0.0078, 5.01, 25.13, 30.78, 340.05, 1040.90],
                "mpv": [0.0001, 0.0001, 0.01, 0.01, 0.01, 0.05, 0.05],
            },
            columns=["symbol", "price", "mpv"],
        )

        assert_frame_equal(result, expected)

    def test_on_specialized_type(self, any_real_dtype):
        # see gh-13936
        dtype = np.dtype(any_real_dtype).type

        df1 = pd.DataFrame(
            {"value": [5, 2, 25, 100, 78, 120, 79], "symbol": list("ABCDEFG")},
            columns=["symbol", "value"],
        )
        df1.value = dtype(df1.value)

        df2 = pd.DataFrame(
            {"value": [0, 80, 120, 125], "result": list("xyzw")},
            columns=["value", "result"],
        )
        df2.value = dtype(df2.value)

        df1 = df1.sort_values("value").reset_index(drop=True)
        result = pd.merge_asof(df1, df2, on="value")

        expected = pd.DataFrame(
            {
                "symbol": list("BACEGDF"),
                "value": [2, 5, 25, 78, 79, 100, 120],
                "result": list("xxxxxyz"),
            },
            columns=["symbol", "value", "result"],
        )
        expected.value = dtype(expected.value)

        assert_frame_equal(result, expected)

    def test_on_specialized_type_by_int(self, any_real_dtype):
        # see gh-13936
        dtype = np.dtype(any_real_dtype).type

        df1 = pd.DataFrame(
            {
                "value": [5, 2, 25, 100, 78, 120, 79],
                "key": [1, 2, 3, 2, 3, 1, 2],
                "symbol": list("ABCDEFG"),
            },
            columns=["symbol", "key", "value"],
        )
        df1.value = dtype(df1.value)

        df2 = pd.DataFrame(
            {"value": [0, 80, 120, 125], "key": [1, 2, 2, 3], "result": list("xyzw")},
            columns=["value", "key", "result"],
        )
        df2.value = dtype(df2.value)

        df1 = df1.sort_values("value").reset_index(drop=True)
        result = pd.merge_asof(df1, df2, on="value", by="key")

        expected = pd.DataFrame(
            {
                "symbol": list("BACEGDF"),
                "key": [2, 1, 3, 3, 2, 2, 1],
                "value": [2, 5, 25, 78, 79, 100, 120],
                "result": [np.nan, "x", np.nan, np.nan, np.nan, "y", "x"],
            },
            columns=["symbol", "key", "value", "result"],
        )
        expected.value = dtype(expected.value)

        assert_frame_equal(result, expected)

    def test_on_float_by_int(self):
        # type specialize both "by" and "on" parameters
        df1 = pd.DataFrame(
            {
                "symbol": list("AAABBBCCC"),
                "exch": [1, 2, 3, 1, 2, 3, 1, 2, 3],
                "price": [
                    3.26,
                    3.2599,
                    3.2598,
                    12.58,
                    12.59,
                    12.5,
                    378.15,
                    378.2,
                    378.25,
                ],
            },
            columns=["symbol", "exch", "price"],
        )

        df2 = pd.DataFrame(
            {
                "exch": [1, 1, 1, 2, 2, 2, 3, 3, 3],
                "price": [0.0, 1.0, 100.0, 0.0, 5.0, 100.0, 0.0, 5.0, 1000.0],
                "mpv": [0.0001, 0.01, 0.05, 0.0001, 0.01, 0.1, 0.0001, 0.25, 1.0],
            },
            columns=["exch", "price", "mpv"],
        )

        df1 = df1.sort_values("price").reset_index(drop=True)
        df2 = df2.sort_values("price").reset_index(drop=True)

        result = pd.merge_asof(df1, df2, on="price", by="exch")

        expected = pd.DataFrame(
            {
                "symbol": list("AAABBBCCC"),
                "exch": [3, 2, 1, 3, 1, 2, 1, 2, 3],
                "price": [
                    3.2598,
                    3.2599,
                    3.26,
                    12.5,
                    12.58,
                    12.59,
                    378.15,
                    378.2,
                    378.25,
                ],
                "mpv": [0.0001, 0.0001, 0.01, 0.25, 0.01, 0.01, 0.05, 0.1, 0.25],
            },
            columns=["symbol", "exch", "price", "mpv"],
        )

        assert_frame_equal(result, expected)

    def test_merge_datatype_error_raises(self):
        msg = r"incompatible merge keys \[0\] .*, must be the same type"

        left = pd.DataFrame({"left_val": [1, 5, 10], "a": ["a", "b", "c"]})
        right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7], "a": [1, 2, 3, 6, 7]})

        with pytest.raises(MergeError, match=msg):
            merge_asof(left, right, on="a")

    def test_merge_datatype_categorical_error_raises(self):
        msg = (
            r"incompatible merge keys \[0\] .* both sides category, "
            "but not equal ones"
        )

        left = pd.DataFrame(
            {"left_val": [1, 5, 10], "a": pd.Categorical(["a", "b", "c"])}
        )
        right = pd.DataFrame(
            {
                "right_val": [1, 2, 3, 6, 7],
                "a": pd.Categorical(["a", "X", "c", "X", "b"]),
            }
        )

        with pytest.raises(MergeError, match=msg):
            merge_asof(left, right, on="a")

    @pytest.mark.parametrize(
        "func", [lambda x: x, lambda x: to_datetime(x)], ids=["numeric", "datetime"]
    )
    @pytest.mark.parametrize("side", ["left", "right"])
    def test_merge_on_nans(self, func, side):
        # GH 23189
        msg = "Merge keys contain null values on {} side".format(side)
        nulls = func([1.0, 5.0, np.nan])
        non_nulls = func([1.0, 5.0, 10.0])
        df_null = pd.DataFrame({"a": nulls, "left_val": ["a", "b", "c"]})
        df = pd.DataFrame({"a": non_nulls, "right_val": [1, 6, 11]})

        with pytest.raises(ValueError, match=msg):
            if side == "left":
                merge_asof(df_null, df, on="a")
            else:
                merge_asof(df, df_null, on="a")

    def test_merge_by_col_tz_aware(self):
        # GH 21184
        left = pd.DataFrame(
            {
                "by_col": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
                "on_col": [2],
                "values": ["a"],
            }
        )
        right = pd.DataFrame(
            {
                "by_col": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
                "on_col": [1],
                "values": ["b"],
            }
        )
        result = pd.merge_asof(left, right, by="by_col", on="on_col")
        expected = pd.DataFrame(
            [[pd.Timestamp("2018-01-01", tz="UTC"), 2, "a", "b"]],
            columns=["by_col", "on_col", "values_x", "values_y"],
        )
        assert_frame_equal(result, expected)

    def test_by_mixed_tz_aware(self):
        # GH 26649
        left = pd.DataFrame(
            {
                "by_col1": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
                "by_col2": ["HELLO"],
                "on_col": [2],
                "value": ["a"],
            }
        )
        right = pd.DataFrame(
            {
                "by_col1": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
                "by_col2": ["WORLD"],
                "on_col": [1],
                "value": ["b"],
            }
        )
        result = pd.merge_asof(left, right, by=["by_col1", "by_col2"], on="on_col")
        expected = pd.DataFrame(
            [[pd.Timestamp("2018-01-01", tz="UTC"), "HELLO", 2, "a"]],
            columns=["by_col1", "by_col2", "on_col", "value_x"],
        )
        expected["value_y"] = np.array([np.nan], dtype=object)
        assert_frame_equal(result, expected)
