ENH: add pd.asof_merge #13358

jreback · 2016-06-03T19:34:49Z

Here is a notebook of example usage and timings

jreback · 2016-06-03T19:38:30Z

API question. now we have pd.merge (already existing), pd.ordered_merge (already existing) and pd.asof_merge. Makes sense to keep them separate as the take somewhat different args, but this IS adding to the namespace a bit.

chrisaycock · 2016-06-03T19:47:52Z

pandas/tools/merge.py

+                raise MergeError("incompatible merge keys, "
+                                 "must be the same type")
+
+        # no duplicates on rhs join keys


It would be nice if the function could handle duplicates automatically.

its easy, my only question is this too magical. IOW would a user expect to get the dups? (e.g. it will work right now w/dups)

ok, fixed to auto-deduplicate right if needed (all internal now).

codecov-io · 2016-06-03T20:02:26Z

Current coverage is 84.32%

Merging #13358 into master will increase coverage by 0.03%

@@             master     #13358   diff @@
==========================================
  Files           138        138          
  Lines         50929      51058   +129   
  Methods           0          0          
  Messages          0          0          
  Branches          0          0          
==========================================
+ Hits          42926      43055   +129   
  Misses         8003       8003          
  Partials          0          0

Powered by Codecov. Last updated by 9670b31...4592fa2

tacaswell · 2016-06-03T20:54:11Z

I think adding to the name space (with in reason) is better than shoe horning partially overlapping APIs together. Having args/kwargs which are not used / disallowed / have different meaning based on the value of other input should be avoided.

chrisaycock · 2016-06-03T21:12:56Z

pandas/tools/merge.py

+            try:
+                rhs = right.iloc[rby.indices[key]]
+            except KeyError:
+                # key doesn't exist in left


Why wouldn't the key exist in the left if the failure is from a lookup on the right?

missing ticker for a quotes (looks like I am bugging on it ATM

In [1]: trades = pd.read_csv('pandas/tools/tests/data/trades.csv') In [2]: trades.time = pd.to_datetime(trades.time) In [3]: quotes = pd.read_csv('pandas/tools/tests/data/quotes.csv') In [4]: quotes.time = pd.to_datetime(quotes.time) In [5]: pd.asof_merge(trades, quotes, on='time', by='ticker') Out[5]: time ticker price quantity marketCenter bid ask 0 2016-05-25 13:30:00.023 MSFT 51.95 75 NASDAQ 51.95 51.95 1 2016-05-25 13:30:00.038 MSFT 51.95 155 NASDAQ 51.95 51.95 2 2016-05-25 13:30:00.048 GOOG 720.77 100 NASDAQ 720.50 720.93 3 2016-05-25 13:30:00.048 GOOG 720.92 100 NASDAQ 720.50 720.93 4 2016-05-25 13:30:00.048 GOOG 720.93 200 NASDAQ 720.50 720.93 5 2016-05-25 13:30:00.048 GOOG 720.93 300 NASDAQ 720.50 720.93 6 2016-05-25 13:30:00.048 GOOG 720.93 600 NASDAQ 720.50 720.93 7 2016-05-25 13:30:00.048 GOOG 720.93 44 NASDAQ 720.50 720.93 8 2016-05-25 13:30:00.074 AAPL 98.67 478343 NASDAQ NaN NaN 9 2016-05-25 13:30:00.075 AAPL 98.67 478343 NASDAQ 98.55 98.56 10 2016-05-25 13:30:00.075 AAPL 98.66 6 NASDAQ 98.55 98.56 11 2016-05-25 13:30:00.075 AAPL 98.65 30 NASDAQ 98.55 98.56 12 2016-05-25 13:30:00.075 AAPL 98.65 75 NASDAQ 98.55 98.56 13 2016-05-25 13:30:00.075 AAPL 98.65 20 NASDAQ 98.55 98.56 14 2016-05-25 13:30:00.075 AAPL 98.65 35 NASDAQ 98.55 98.56 15 2016-05-25 13:30:00.075 AAPL 98.65 10 NASDAQ 98.55 98.56 16 2016-05-25 13:30:00.075 AAPL 98.55 6 ARCA 98.55 98.56 17 2016-05-25 13:30:00.075 AAPL 98.55 6 ARCA 98.55 98.56 18 2016-05-25 13:30:00.076 AAPL 98.56 1000 ARCA 98.55 98.56 19 2016-05-25 13:30:00.076 AAPL 98.56 200 ARCA 98.55 98.56 20 2016-05-25 13:30:00.076 AAPL 98.56 300 ARCA 98.55 98.56 21 2016-05-25 13:30:00.076 AAPL 98.56 400 ARCA 98.55 98.56 22 2016-05-25 13:30:00.076 AAPL 98.56 600 ARCA 98.55 98.56 23 2016-05-25 13:30:00.076 AAPL 98.56 200 ARCA 98.55 98.56 24 2016-05-25 13:30:00.078 MSFT 51.95 783 NASDAQ 51.92 51.95 25 2016-05-25 13:30:00.078 MSFT 51.95 100 NASDAQ 51.92 51.95 26 2016-05-25 13:30:00.078 MSFT 51.95 100 NASDAQ 51.92 51.95 In [6]: pd.asof_merge(trades, quotes[quotes.ticker!='MSFT'], on='time', by='ticker') IndexError: indices are out-of-bounds

In [7]: quotes[quotes.ticker!='MSFT'] Out[7]: time ticker bid ask 0 2016-05-25 13:30:00.023 GOOG 720.50 720.93 3 2016-05-25 13:30:00.048 GOOG 720.50 720.93 4 2016-05-25 13:30:00.048 GOOG 720.50 720.93 5 2016-05-25 13:30:00.048 GOOG 720.50 720.93 6 2016-05-25 13:30:00.048 GOOG 720.50 720.93 7 2016-05-25 13:30:00.072 GOOG 720.50 720.88 8 2016-05-25 13:30:00.075 AAPL 98.55 98.56 9 2016-05-25 13:30:00.076 AAPL 98.55 98.56 10 2016-05-25 13:30:00.076 AAPL 98.55 98.56 11 2016-05-25 13:30:00.076 AAPL 98.55 98.56

We should have NaN in the resulting DataFrame when the right misses a key, just like in a regular left merge().

(This should be part of the regression tests as well, and I totally forgot about it earlier when I came-up with examples. Sorry about that.)

In [5]: pd.asof_merge(trades, quotes[quotes.ticker!='MSFT'], on='time', by='ticker') Out[5]: time ticker price quantity marketCenter bid ask 0 2016-05-25 13:30:00.023 MSFT 51.95 75 NASDAQ NaN NaN 1 2016-05-25 13:30:00.038 MSFT 51.95 155 NASDAQ NaN NaN 2 2016-05-25 13:30:00.048 GOOG 720.77 100 NASDAQ 720.50 720.93 3 2016-05-25 13:30:00.048 GOOG 720.92 100 NASDAQ 720.50 720.93 4 2016-05-25 13:30:00.048 GOOG 720.93 200 NASDAQ 720.50 720.93 5 2016-05-25 13:30:00.048 GOOG 720.93 300 NASDAQ 720.50 720.93 6 2016-05-25 13:30:00.048 GOOG 720.93 600 NASDAQ 720.50 720.93 7 2016-05-25 13:30:00.048 GOOG 720.93 44 NASDAQ 720.50 720.93 8 2016-05-25 13:30:00.074 AAPL 98.67 478343 NASDAQ NaN NaN 9 2016-05-25 13:30:00.075 AAPL 98.67 478343 NASDAQ 98.55 98.56 10 2016-05-25 13:30:00.075 AAPL 98.66 6 NASDAQ 98.55 98.56 11 2016-05-25 13:30:00.075 AAPL 98.65 30 NASDAQ 98.55 98.56 12 2016-05-25 13:30:00.075 AAPL 98.65 75 NASDAQ 98.55 98.56 13 2016-05-25 13:30:00.075 AAPL 98.65 20 NASDAQ 98.55 98.56 14 2016-05-25 13:30:00.075 AAPL 98.65 35 NASDAQ 98.55 98.56 15 2016-05-25 13:30:00.075 AAPL 98.65 10 NASDAQ 98.55 98.56 16 2016-05-25 13:30:00.075 AAPL 98.55 6 ARCA 98.55 98.56 17 2016-05-25 13:30:00.075 AAPL 98.55 6 ARCA 98.55 98.56 18 2016-05-25 13:30:00.076 AAPL 98.56 1000 ARCA 98.55 98.56 19 2016-05-25 13:30:00.076 AAPL 98.56 200 ARCA 98.55 98.56 20 2016-05-25 13:30:00.076 AAPL 98.56 300 ARCA 98.55 98.56 21 2016-05-25 13:30:00.076 AAPL 98.56 400 ARCA 98.55 98.56 22 2016-05-25 13:30:00.076 AAPL 98.56 600 ARCA 98.55 98.56 23 2016-05-25 13:30:00.076 AAPL 98.56 200 ARCA 98.55 98.56 24 2016-05-25 13:30:00.078 MSFT 51.95 783 NASDAQ NaN NaN 25 2016-05-25 13:30:00.078 MSFT 51.95 100 NASDAQ NaN NaN 26 2016-05-25 13:30:00.078 MSFT 51.95 100 NASDAQ NaN NaN

#4588 reared its ugly head, so had to 'fix' it.

chris-b1 · 2016-06-03T23:19:04Z

pandas/tools/merge.py

+    tolerance: integer or Timedelta, optional, default None
+        select asof tolerance within this range, must be compatible
+        to the merge index.
+    strict: boolean, default False


For what it's worth, I found this keyword name counter-intuitive. More verbose, but would something like allow_exact_matches=True be better?

"strict" means "strictly less-than", as opposed to "less-than-or-equal-to". I agree that the parameter's explanation could be clearer.

how about one of these:

strict='less_than_or_equal' | 'less_than'

match='less_than_or_equal' | 'less_than'

match_less_that_or_equal=True | False

allow_exact_matches=True | False

anyone have thoughts on strict -> new name? (from above)

jorisvandenbossche · 2016-06-06T12:43:12Z

@jreback Just a general comment: for me (as a not financial guy :-)), the current explanation of the method (and also the examples) is not really clarifying on what it exactly does .. (at a first read, without looking at the code, which a user should not have to do). So we will have to do a better job explaining it I think.

Some things I was thinking:

'asof' is not a well-known term I think, so we need to explain it here as well (you should not have to look at the .asof() docstring to know what it means.
maybe a more dummy example would also help? (the current example is rather detailed (milliseconds, ..), making it more difficult to grasp in an glance what it does) But can certainly keep the current example as an actual practical example!

Will take a more detailed look myself later.

wesm · 2016-06-08T22:17:54Z

@jreback I think this needs to be a new API. I think adding asof_merge to the pandas NS is fine. see also http://code.kx.com/wiki/Reference/aj

jreback · 2016-06-09T13:08:31Z

Ok taking @chrisaycock suggestion, changed strict -> allow_exact_matches, much more clear.

Here's the new signature.

Signature: pd.asof_merge(left, right, on=None, left_on=None, right_on=None, by=None, suffixes=('_x', '_y'), tolerance=None, allow_exact_matches=True)
Docstring:
Perform an asof merge. This by definition will fill the right data with
the asof the left data. The data must be ordered according to the 'on'
(typically time). Optionally perform group-wise merge.

Asof means return the last good (non-NaN) value in object for the date in
question. This is conceptually very close to a left-outer join with
forward filling.

.. versionadded 0.18.2

Parameters
----------
left: DataFrame
right: DataFrame
    Data will be de-duplicated on the [by, on] combination if needed.
on: label or list
    Field names to join on. Must be found in both DataFrames.
    The data MUST be ordered. Furthermore this must be a datetimelike or
    integer-index.
left_on: label or list, or array-like
    Field names to join on in left DataFrame. Can be a vector or list of
    vectors of the length of the DataFrame to use a particular vector as
    the join key instead of columns
right_on: label or list, or array-like
    Field names to join on in right DataFrame or vector/list of vectors per
    left_on docs
by: column name or list of column names
    Group both the left and right DataFrames by the group columns; perform
    the merge operation on these pieces and recombine.
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
    to the merge index.
allow_exact_matches: boolean, default True
    If True, allow matching the same 'on' value (e.g. same time)
    If False, don't match the same 'on' value
        (e.g. don't match the same time)

Returns
-------
merged : DataFrame

jreback · 2016-06-09T13:08:40Z

any other comments?

chrisaycock · 2016-06-09T17:54:59Z

What about this for the help statement?

Perform an asof merge. This is similar to a left-join except that we match on
nearest key rather than equal keys.

For each row in the left DataFrame, we select the last row in the right DataFrame
whose 'on' key is less than the left's key. Both DataFrames must be sorted by the
key.

Optionally perform group-wise merge. This matches the 'by' key equally, in
addition to the nearest match on the 'on' key.


Parameters
----------
left: DataFrame
right: DataFrame
    Data will be de-duplicated on the [by, on] combination if needed.
on: label or list
    Field names to join on. Must be found in both DataFrames.
    The data MUST be ordered. Furthermore this must be a datetimelike or
    integer-index.
left_on: label or list, or array-like
    Field names to join on in left DataFrame. Can be a vector or list of
    vectors of the length of the DataFrame to use a particular vector as
    the join key instead of columns
right_on: label or list, or array-like
    Field names to join on in right DataFrame or vector/list of vectors per
    left_on docs
by: column name or list of column names
    Group both the left and right DataFrames by the group columns; perform
    the merge operation on these pieces and recombine.
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
    to the merge index.
allow_exact_matches: boolean, default True
    If True, allow matching the same 'on' value (i.e. less-than-or-equal-to)
    If False, don't match the same 'on' value
        (i.e., stricly less-than)

jreback · 2016-06-09T18:55:17Z

updated according to @chrisaycock doc-string

TomAugspurger · 2016-06-09T18:57:02Z

doc/source/merging.rst

+
+Similar to an ``pd.ordered_merge()``, an ``pd.asof_merge()`` allows one to propogate forward
+values that are missing. ``pd.asof_merge()`` allows some specific options on this propogation


This propagates forward values that are not missing, right? Also, not sure what the second sentence is saying.

I rewrote basically using the new doc-string.

chris-b1 · 2016-06-09T19:37:40Z

To @jorisvandenbossche's point, how about adding a smaller context-free example that makes it a little easier to see exactly what's going on. Something like this: (edit: changed 'right_val' to numbers so it's easier to match up the rows)

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

pd.asof_merge(a, b, on='a')

It might also be good to show an ordered_merge on the same set of data so you can see how they are different?

jreback · 2016-06-09T19:42:13Z

sure maybe just in the actual docs / or in doc-string as well?

chris-b1 · 2016-06-09T19:43:25Z

yeah, I guess was just thinking about the docs

jorisvandenbossche · 2016-06-12T14:12:44Z

pandas/tools/merge.py

+    allow_exact_matches : boolean, default True
+
+        - If True, allow matching the same 'on' value
+            (i.e. less-than-or-equal-to)


Can you align the (i.e. with If (otherwise sphinx will complain about unexpected indentation). Same for the lines below (and for check_duplicates)

oh, sure. that makes sense.

jreback · 2016-06-14T15:18:05Z

any final issues? @jorisvandenbossche @TomAugspurger

jorisvandenbossche · 2016-06-14T22:47:15Z

doc/source/whatsnew/v0.18.2.txt

+
+A long-time requested feature has been added thru the ``pd.merge_asof()`` function, to
+support asof style joining of time-series. (:issue:`1870`). Full documentaion are
+:ref:`here <mergeing.merge_asof>`


mergeing -> merging

jorisvandenbossche · 2016-06-15T00:37:26Z

@jreback I added another bunch of doc comments (they are not critical, I can also do a PR later if you want)

But I wanted to come back to the duplicate handling for a moment. In case of only on is specified (so no by), it depends on if it is an 'equal' match or an 'less than' match for how duplicates (in the 'on' key column) in the right dataframe are handled:

In [1]: df1 = pd.DataFrame({'key':[1,1,3], 'left_val':[1,2,3]})
   ...: df2 = pd.DataFrame({'key':[1,3,3], 'right_val':[1,2,3]})
   ...:

In [2]: pd.merge_asof(df1, df2, on='key')
Out[2]:
   key  left_val  right_val
0    1         1          1
1    1         2          1
2    3         3          2
3    3         3          3

In [3]: df1 = pd.DataFrame({'key':[1,1,3], 'left_val':[1,2,3]})
   ...: df2 = pd.DataFrame({'key':[1,2,2], 'right_val':[1,2,3]})

In [4]: pd.merge_asof(df1, df2, on='key')
Out[4]:
   key  left_val  right_val
0    1         1          1
1    1         2          1
2    3         3          3

So in case of an exact match, you get both duplicates (consistent with merge), but if you have a inexact match, only the last row of the duplicates is used. This seems a bit inconsistent to me.

(stressing again that I do not know the financial application of this method, but just look at it based on my understanding of the general concept of an asof join, so I don't know what the 'logical' way is to treat duplicates. I just notice that it is inconsistent with merge/merge_ordered)

ENH: add DataFrame.asof ENH: add pd.asof_merge closes pandas-dev#1870

jreback · 2016-06-15T01:37:25Z

your example of the duplicates is correct. This is exactly the purpose of an asof merge, NOT to duplicate (pun!) the point of merge. An asof-merge will get you exactly 1 value for each of the left. That's why duplicates are a bit odd, you normally don't have them.
Your example is not quite right, you are now emulating an inner join (as the how is implicit), but a how='left'.

In [5]: pd.merge_asof(df1, df2, on='key')
Out[5]: 
   key  left_val  right_val
0    1         1          1
1    1         2          1
2    3         3          3

In [6]: pd.merge(df1, df2, on='key', how='left')
Out[6]: 
   key  left_val  right_val
0    1         1        1.0
1    1         2        1.0
2    3         3        NaN

I added a test with your examples in any event (as well as updated the docs).

jreback · 2016-06-16T12:34:41Z

@jorisvandenbossche ready to go.

jorisvandenbossche · 2016-06-16T23:41:46Z

(sorry for the late answer)
OK, I see the logic in that (and the consistency with a left merge -> only the NaN is filled), but on the other hand, the "nearest" and "last row" in the docstring ("match on nearest key rather than equal keys." and "we select the last row in the right DataFrame whose 'on' key is less than or equal to the left's key.") does not really apply on the 'equal' case (not the last row, but all rows but equal key are selected), only on the 'less than' case.

Further, I have another question on the duplicate. There is also a difference in handling of duplicates when using by or not (using the same example as above, but with a 'by' column (consisting of one value for simplicity of showing)):

In [1]: df1 = pd.DataFrame({'key':[1,1,3], 'by':['a']*3, 'left_val':[1,2,3]})
   ...: df2 = pd.DataFrame({'key':[1,3,3], 'by':['a']*3, 'right_val':[1,2,3]})
   ...:

In [2]: pd.merge_asof(df1, df2, on='key')
Out[2]:
  by_x  key  left_val by_y  right_val
0    a    1         1    a          1
1    a    1         2    a          1
2    a    3         3    a          2
3    a    3         3    a          3

In [3]: pd.merge_asof(df1, df2, on='key', by='by')
Out[3]:
  by  key  left_val  right_val
0  a    1         1          1
1  a    1         2          1
2  a    3         3          3

So when not using by, duplicate equal keys in the right frame are all used in the result, while when using by, only the last row is used.

jorisvandenbossche · 2016-06-16T23:43:29Z

(BTW, it's OK for me to merge this version while I further ask questions, also easier to test when in master and to further clean-up docs)

jorisvandenbossche · 2016-06-16T23:52:28Z

Using a slightly other example (combining both cases - duplicate equal and non-equal keys in the right frame - in one dataframe:

In [4]: df1 = pd.DataFrame({'key':[1,1,3,5], 'by':['a']*4, 'left_val':[1,2,3,4]})
   ...: df2 = pd.DataFrame({'key':[1,2,2,5,5], 'by':['a']*5, 'right_val':[1,2,3,4,5]})
   ...:

In [5]: pd.merge_asof(df1, df2, on='key')
Out[5]:
  by_x  key  left_val by_y  right_val
0    a    1         1    a          1
1    a    1         2    a          1
2    a    3         3    a          3
3    a    5         4    a          4
4    a    5         4    a          5

In [6]: pd.merge_asof(df1, df2, on='key', by='by')
Out[6]:
  by  key  left_val  right_val
0  a    1         1          1
1  a    1         2          1
2  a    3         3          3
3  a    5         4          5

In [7]: pd.merge_asof(df1, df2, on='key', by='by', check_duplicates=False)
Out[7]:
  by  key  left_val  right_val
0  a    1         1          1
1  a    1         2          1
2  a    3         3          3
3  a    5         4          4
4  a    5         4          5

So actually (in this case), the duplicate=False (when having duplicates, which is not the purpose of this keyword as following the docs you should use it when you are certain there are no duplicates) gives you similar behaviour as when no by is used.

jorisvandenbossche · 2016-06-16T23:58:05Z

@jreback OK, I just see you actually changed this (check_duplicates is now also used without by), sorry, I didn't update my branch for testing ...
So ignore all my comments from above about inconsistency between using by or not!

jreback · 2016-06-17T00:10:57Z

ok merged, will see about docs and do a followup probably

closes #13358

jreback added Enhancement Datetime Datetime data dtype Reshaping Concat, Merge/Join, Stack/Unstack, Explode labels Jun 3, 2016

jreback added this to the 0.18.2 milestone Jun 3, 2016

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jreback changed the title ~~ENH: add pd.asof_merge, closes #1870~~ ENH: add pd.asof_merge Jun 3, 2016

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TST: reorg tests/series/test_timeseries -> test_asof

4592fa2

ENH: add DataFrame.asof ENH: add pd.asof_merge closes pandas-dev#1870

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jreback closed this in 6d8c04c Jun 17, 2016

jreback mentioned this pull request Jun 17, 2016

Followup to #13358 asof-join #13469

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jreback added a commit that referenced this pull request Jun 17, 2016

DOC: follow on merge_asof

013c2ce

closes #13358

jreback mentioned this pull request Aug 11, 2016

API: Expanded resample #13961

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topper-123 mentioned this pull request Nov 23, 2017

CLN/DEPR: remove pd.ordered_merge #18459

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DEPR: deprecations log for removed issues #13777

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		Similar to an ``pd.ordered_merge()``, an ``pd.asof_merge()`` allows one to propogate forward
		values that are missing. ``pd.asof_merge()`` allows some specific options on this propogation

ENH: add pd.asof_merge #13358

ENH: add pd.asof_merge #13358

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Current coverage is 84.32%

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