pandas.merge_asof¶
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pandas.merge_asof(left, right, on=None, left_on=None, right_on=None, left_index=False, right_index=False, by=None, left_by=None, right_by=None, suffixes=('_x', '_y'), tolerance=None, allow_exact_matches=True, direction='backward')[source]¶ Perform an asof merge. This is similar to a left-join except that we match on nearest key rather than equal keys.
Both DataFrames must be sorted by the key.
For each row in the left DataFrame:
- A “backward” search selects the last row in the right DataFrame whose ‘on’ key is less than or equal to the left’s key.
- A “forward” search selects the first row in the right DataFrame whose ‘on’ key is greater than or equal to the left’s key.
- A “nearest” search selects the row in the right DataFrame whose ‘on’ key is closest in absolute distance to the left’s key.
The default is “backward” and is compatible in versions below 0.20.0. The direction parameter was added in version 0.20.0 and introduces “forward” and “nearest”.
Optionally match on equivalent keys with ‘by’ before searching with ‘on’.
New in version 0.19.0.
Parameters: - left : DataFrame
- right : DataFrame
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 : boolean
Use the index of the left DataFrame as the join key.
New in version 0.19.2.
right_index : boolean
Use the index of the right DataFrame as the join key.
New in version 0.19.2.
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.
New in version 0.19.2.
right_by : column name
Field names to match on in the right DataFrame.
New in version 0.19.2.
suffixes : 2-length sequence (tuple, list, …)
Suffix to apply to overlapping column names in the left and right side, respectively.
tolerance : integer or Timedelta, optional, default None
Select asof tolerance within this range; must be compatible with the merge index.
allow_exact_matches : boolean, 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., stricly less-than / strictly greater-than)
direction : ‘backward’ (default), ‘forward’, or ‘nearest’
Whether to search for prior, subsequent, or closest matches.
New in version 0.20.0.
Returns: - merged : DataFrame
See also
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 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 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