ame or named Series
    on : label
        Field name to join on. Must be found in both DataFrames.
        The data MUST be ordered. Furthermore this must be a numeric column,
        such as datetimelike, integer, or float. On or left_on/right_on
        must be given.
    left_on : label
        Field name to join on in left DataFrame.
    right_on : label
        Field name to join on in right DataFrame.
    left_index : bool
        Use the index of the left DataFrame as the join key.
    right_index : bool
        Use the index of the right DataFrame as the join key.
    by : column name or list of column names
        Match on these columns before performing merge operation.
    left_by : column name
        Field names to match on in the left DataFrame.
    right_by : column name
        Field names to match on in the right DataFrame.
    suffixes : 2-length sequence (tuple, list, ...)
        Suffix to apply to overlapping column names in the left and right
        side, respectively.
    tolerance : int or Timedelta, optional, default None
        Select asof tolerance within this range; must be compatible
        with the merge index.
    allow_exact_matches : bool, default True

        - If True, allow matching with the same 'on' value
          (i.e. less-than-or-equal-to / greater-than-or-equal-to)
        - If False, don't match the same 'on' value
          (i.e., strictly less-than / strictly greater-than).

    direction : 'backward' (default), 'forward', or 'nearest'
        Whether to search for prior, subsequent, or closest matches.

    Returns
    -------
    DataFrame

    See Also
    --------
    merge : Merge with a database-style join.
    merge_ordered : Merge with optional filling/interpolation.

    Examples
    --------
    >>> left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
    >>> left
        a left_val
    0   1        a
    1   5        b
    2  10        c

    >>> right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
    >>> right
       a  right_val
    0  1          1
    1  2          2
    2  3          3
    3  6          6
    4  7          7

    >>> pd.merge_asof(left, right, on="a")
        a left_val  right_val
    0   1        a          1
    1   5        b          3
    2  10        c          7

    >>> pd.merge_asof(left, right, on="a", allow_exact_matches=False)
        a left_val  right_val
    0   1        a        NaN
    1   5        b        3.0
    2  10        c        7.0

    >>> pd.merge_asof(left, right, on="a", direction="forward")
        a left_val  right_val
    0   1        a        1.0
    1   5        b        6.0
    2  10        c        NaN

    >>> pd.merge_asof(left, right, on="a", direction="nearest")
        a left_val  right_val
    0   1        a          1
    1   5        b          6
    2  10        c          7

    We can use indexed DataFrames as well.

    >>> left = pd.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10])
    >>> left
       left_val
    1         a
    5         b
    10        c

    >>> right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
    >>> right
       right_val
    1          1
    2          2
    3          3
    6          6
    7          7

    >>> pd.merge_asof(left, right, left_index=True, right_index=True)
       left_val  right_val
    1         a          1
    5         b          3
    10        c          7

    Here is a real-world times-series example

    >>> quotes = pd.DataFrame(
    ...     {
    ...         "time": [
    ...             pd.Timestamp("2016-05-25 13:30:00.023"),
    ...             pd.Timestamp("2016-05-25 13:30:00.023"),
    ...             pd.Timestamp("2016-05-25 13:30:00.030"),
    ...             pd.Timestamp("2016-05-25 13:30:00.041"),
    ...             pd.Timestamp("2016-05-25 13:30:00.048"),
    ...             pd.Timestamp("2016-05-25 13:30:00.049"),
    ...             pd.Timestamp("2016-05-25 13:30:00.072"),
    ...             pd.Timestamp("2016-05-25 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]
    ...     }
    ... )
    >>> quotes
                         time ticker     bid     ask
    0 2016-05-25 13:30:00.023   GOOG  720.50  720.93
    1 2016-05-25 13:30:00.023   MSFT   51.95   51.96
    2 2016-05-25 13:30:00.030   MSFT   51.97   51.98
    3 2016-05-25 13:30:00.041   MSFT   51.99   52.00
    4 2016-05-25 13:30:00.048   GOOG  720.50  720.93
    5 2016-05-25 13:30:00.049   AAPL   97.99   98.01
    6 2016-05-25 13:30:00.072   GOOG  720.50  720.88
    7 2016-05-25 13:30:00.075   MSFT   52.01   52.03

    >>> trades = pd.DataFrame(
    ...        {
    ...            "time": [
    ...                pd.Timestamp("2016-05-25 13:30:00.023"),
    ...                pd.Timestamp("2016-05-25 13:30:00.038"),
    ...                pd.Timestamp("2016-05-25 13:30:00.048"),
    ...                pd.Timestamp("2016-05-25 13:30:00.048"),
    ...                pd.Timestamp("2016-05-25 13:30:00.048")
    ...            ],
    ...            "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
    ...            "price": [51.95, 51.95, 720.77, 720.92, 98.0],
    ...            "quantity": [75, 155, 100, 100, 100]
    ...        }
    ...    )
    >>> trades
                         time ticker   price  quantity
    0 2016-05-25 13:30:00.023   MSFT   51.95        75
    1 2016-05-25 13:30:00.038   MSFT   51.95       155
    2 2016-05-25 13:30:00.048   GOOG  720.77       100
    3 2016-05-25 13:30:00.048   GOOG  720.92       100
    4 2016-05-25 13:30:00.048   AAPL   98.00       100

    By default we are taking the asof of the quotes

    >>> pd.merge_asof(trades, quotes, on="time", by="ticker")
                         time ticker   price  quantity     bid     ask
    0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
    1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN

    We only asof within 2ms between the quote time and the trade time

    >>> pd.merge_asof(
    ...     trades, quotes, on="time", by="ticker", tolerance=pd.Timedelta("2ms")
    ... )
                         time ticker   price  quantity     bid     ask
    0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
    1 2016-05-25 13:30:00.038   MSFT   51.95       155     NaN     NaN
    2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
    3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN

    We only asof within 10ms between the quote time and the trade time
    and we exclude exact matches on time. However *prior* data will
    propagate forward

    >>> pd.merge_asof(
    ...     trades,
    ...     quotes,
    ...     on="time",
    ...     by="ticker",
    ...     tolerance=pd.Timedelta("10ms"),
    ...     allow_exact_matches=False
    ... )
                         time ticker   price  quantity     bid     ask
    0 2016-05-25 13:30:00.023   MSFT   51.95        75     NaN     NaN
    1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
    2 2016-05-25 13:30:00.048   GOOG  720.77       100     NaN     NaN
    3 2016-05-25 13:30:00.048   GOOG  720.92       100     NaN     NaN
    4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
    