Merge, join, and concatenate — pandas 0.20.3 documentation
Merge, join, and concatenate
pandas provides various facilities for easily combining together Series,
DataFrame, and Panel objects with various kinds of set logic for the indexes
and relational algebra functionality in the case of join / merge-type
operations.
Concatenating objects
The concat function (in the main pandas namespace) does all of the heavy
lifting of performing concatenation operations along an axis while performing
optional set logic (union or intersection) of the indexes (if any) on the other
axes. Note that I say “if any” because there is only a single possible axis of
concatenation for Series.
Before diving into all of the details of concat and what it can do, here is
a simple example:
In [1]: df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, 3])
In [2]: df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
'B': ['B4', 'B5', 'B6', 'B7'],
'C': ['C4', 'C5', 'C6', 'C7'],
'D': ['D4', 'D5', 'D6', 'D7']},
index=[4, 5, 6, 7])
In [3]: df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
'B': ['B8', 'B9', 'B10', 'B11'],
'C': ['C8', 'C9', 'C10', 'C11'],
'D': ['D8', 'D9', 'D10', 'D11']},
index=[8, 9, 10, 11])
In [4]: frames = [df1, df2, df3]
In [5]: result = pd.concat(frames)
Like its sibling function on ndarrays, numpy.concatenate, pandas.concat
takes a list or dict of homogeneously-typed objects and concatenates them with
some configurable handling of “what to do with the other axes”:
pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False,
copy=True)
objs : a sequence or mapping of Series, DataFrame, or Panel objects. If a
dict is passed, the sorted keys will be used as the keys argument, unless
it is passed, in which case the values will be selected (see below). Any None
objects will be dropped silently unless they are all None in which case a
ValueError will be raised.
axis : {0, 1, ...}, default 0. The axis to concatenate along.
join : {‘inner’, ‘outer’}, default ‘outer’. How to handle indexes on
other axis(es). Outer for union and inner for intersection.
ignore_index : boolean, default False. If True, do not use the index
values on the concatenation axis. The resulting axis will be labeled 0, ...,
n - 1. This is useful if you are concatenating objects where the
concatenation axis does not have meaningful indexing information. Note
the index values on the other axes are still respected in the join.
join_axes : list of Index objects. Specific indexes to use for the other
n - 1 axes instead of performing inner/outer set logic.
keys : sequence, default None. Construct hierarchical index using the
passed keys as the outermost level. If multiple levels passed, should
contain tuples.
levels : list of sequences, default None. Specific levels (unique values)
to use for constructing a MultiIndex. Otherwise they will be inferred from the
names : list, default None. Names for the levels in the resulting
hierarchical index.
verify_integrity : boolean, default False. Check whether the new
concatenated axis contains duplicates. This can be very expensive relative
to the actual data concatenation.
copy : boolean, default True. If False, do not copy data unnecessarily.
Without a little bit of context and example many of these arguments don’t make
much sense. Let’s take the above example. Suppose we wanted to associate
specific keys with each of the pieces of the chopped up DataFrame. We can do
this using the keys argument:
In [6]: result = pd.concat(frames, keys=['x', 'y', 'z'])
As you can see (if you’ve read the rest of the documentation), the resulting
object’s index has a . This
means that we can now do stuff like select out each chunk by key:
In [7]: result.loc['y']
It’s not a stretch to see how this can be very useful. More detail on this
functionality below.
It is worth noting however, that concat (and therefore append) makes
a full copy of the data, and that constantly reusing this function can
create a significant performance hit. If you need to use the operation over
several datasets, use a list comprehension.
frames = [ process_your_file(f) for f in files ]
result = pd.concat(frames)
Set logic on the other axes
When gluing together multiple DataFrames (or Panels or...), for example, you
have a choice of how to handle the other axes (other than the one being
concatenated). This can be done in three ways:
Take the (sorted) union of them all, join='outer'. This is the default
option as it results in zero information loss.
Take the intersection, join='inner'.
Use a specific index (in the case of DataFrame) or indexes (in the case of
Panel or future higher dimensional objects), i.e. the join_axes argument
Here is a example of each of these methods. First, the default join='outer'
In [8]: df4 = pd.DataFrame({'B': ['B2', 'B3', 'B6', 'B7'],
'D': ['D2', 'D3', 'D6', 'D7'],
'F': ['F2', 'F3', 'F6', 'F7']},
index=[2, 3, 6, 7])
In [9]: result = pd.concat([df1, df4], axis=1)
Note that the row indexes have been unioned and sorted. Here is the same thing
with join='inner':
In [10]: result = pd.concat([df1, df4], axis=1, join='inner')
Lastly, suppose we just wanted to reuse the exact index from the original
DataFrame:
In [11]: result = pd.concat([df1, df4], axis=1, join_axes=[df1.index])
Concatenating using append
A useful shortcut to concat are the append instance methods on Series
and DataFrame. These methods actually predated concat. They concatenate
along axis=0, namely the index:
In [12]: result = df1.append(df2)
In the case of DataFrame, the indexes must be disjoint but the columns do not
need to be:
In [13]: result = df1.append(df4)
append may take multiple objects to concatenate:
In [14]: result = df1.append([df2, df3])
Unlike list.append method, which appends to the original list and
returns nothing, append here does not modify df1 and
returns its copy with df2 appended.
Ignoring indexes on the concatenation axis
For DataFrames which don’t have a meaningful index, you may wish to append them
and ignore the fact that they may have overlapping indexes:
To do this, use the ignore_index argument:
In [15]: result = pd.concat([df1, df4], ignore_index=True)
This is also a valid argument to DataFrame.append:
In [16]: result = df1.append(df4, ignore_index=True)
Concatenating with mixed ndims
You can concatenate a mix of Series and DataFrames. The
Series will be transformed to DataFrames with the column name as
the name of the Series.
In [17]: s1 = pd.Series(['X0', 'X1', 'X2', 'X3'], name='X')
In [18]: result = pd.concat([df1, s1], axis=1)
If unnamed Series are passed they will be numbered consecutively.
In [19]: s2 = pd.Series(['_0', '_1', '_2', '_3'])
In [20]: result = pd.concat([df1, s2, s2, s2], axis=1)
Passing ignore_index=True will drop all name references.
In [21]: result = pd.concat([df1, s1], axis=1, ignore_index=True)
More concatenating with group keys
A fairly common use of the keys argument is to override the column names when creating a new DataFrame based on existing Series.
Notice how the default behaviour consists on letting the resulting DataFrame inherits the parent Series’ name, when these existed.
In [22]: s3 = pd.Series([0, 1, 2, 3], name='foo')
In [23]: s4 = pd.Series([0, 1, 2, 3])
In [24]: s5 = pd.Series([0, 1, 4, 5])
In [25]: pd.concat([s3, s4, s5], axis=1)
Through the keys argument we can override the existing column names.
In [26]: pd.concat([s3, s4, s5], axis=1, keys=['red','blue','yellow'])
Let’s consider now a variation on the very first example presented:
In [27]: result = pd.concat(frames, keys=['x', 'y', 'z'])
You can also pass a dict to concat in which case the dict keys will be used
for the keys argument (unless other keys are specified):
In [28]: pieces = {'x': df1, 'y': df2, 'z': df3}
In [29]: result = pd.concat(pieces)
In [30]: result = pd.concat(pieces, keys=['z', 'y'])
The MultiIndex created has levels that are constructed from the passed keys and
the index of the DataFrame pieces:
In [31]: result.index.levels
Out[31]: FrozenList([['z', 'y'], [4, 5, 6, 7, 8, 9, 10, 11]])
If you wish to specify other levels (as will occasionally be the case), you can
do so using the levels argument:
In [32]: result = pd.concat(pieces, keys=['x', 'y', 'z'],
levels=[['z', 'y', 'x', 'w']],
names=['group_key'])
In [33]: result.index.levels
Out[33]: FrozenList([['z', 'y', 'x', 'w'], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])
Yes, this is fairly esoteric, but is actually necessary for implementing things
like GroupBy where the order of a categorical variable is meaningful.
Appending rows to a DataFrame
While not especially efficient (since a new object must be created), you can
append a single row to a DataFrame by passing a Series or dict to append,
which returns a new DataFrame as above.
In [34]: s2 = pd.Series(['X0', 'X1', 'X2', 'X3'], index=['A', 'B', 'C', 'D'])
In [35]: result = df1.append(s2, ignore_index=True)
You should use ignore_index with this method to instruct DataFrame to
discard its index. If you wish to preserve the index, you should construct an
appropriately-indexed DataFrame and append or concatenate those objects.
You can also pass a list of dicts or Series:
In [36]: dicts = [{'A': 1, 'B': 2, 'C': 3, 'X': 4},
{'A': 5, 'B': 6, 'C': 7, 'Y': 8}]
In [37]: result = df1.append(dicts, ignore_index=True)
Database-style DataFrame joining/merging
pandas has full-featured, high performance in-memory join operations
idiomatically very similar to relational databases like SQL. These methods
perform significantly better (in some cases well over an order of magnitude
better) than other open source implementations (like base::merge.data.frame
in R). The reason for this is careful algorithmic design and internal layout of
the data in DataFrame.
for some advanced strategies.
Users who are familiar with SQL but new to pandas might be interested in a
pandas provides a single function, merge, as the entry point for all
standard database join operations between DataFrame objects:
pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=True,
suffixes=('_x', '_y'), copy=True, indicator=False)
left: A DataFrame object
right: Another DataFrame object
on: Columns (names) to join on. Must be found in both the left and
right DataFrame objects. If not passed and left_index and
right_index are False, the intersection of the columns in the
DataFrames will be inferred to be the join keys
left_on: Columns from the left DataFrame to use as keys. Can either be
column names or arrays with length equal to the length of the DataFrame
right_on: Columns from the right DataFrame to use as keys. Can either be
column names or arrays with length equal to the length of the DataFrame
left_index: If True, use the index (row labels) from the left
DataFrame as its join key(s). In the case of a DataFrame with a MultiIndex
(hierarchical), the number of levels must match the number of join keys
from the right DataFrame
right_index: Same usage as left_index for the right DataFrame
how: One of 'left', 'right', 'outer', 'inner'. Defaults
to inner. See below for more detailed description of each method
sort: Sort the result DataFrame by the join keys in lexicographical
order. Defaults to True, setting to False will improve performance
substantially in many cases
suffixes: A tuple of string suffixes to apply to overlapping
columns. Defaults to ('_x', '_y').
copy: Always copy data (default True) from the passed DataFrame
objects, even when reindexing is not necessary. Cannot be avoided in many
cases but may improve performance / memory usage. The cases where copying
can be avoided are somewhat pathological but this option is provided
nonetheless.
indicator: Add a column to the output DataFrame called _merge
with information on the source of each row. _merge is Categorical-type
and takes on a value of left_only for observations whose merge key
only appears in 'left' DataFrame, right_only for observations whose
merge key only appears in 'right' DataFrame, and both if the
observation’s merge key is found in both.
New in version 0.17.0.
The return type will be the same as left. If left is a DataFrame
and right is a subclass of DataFrame, the return type will still be
DataFrame.
merge is a function in the pandas namespace, and it is also available as a
DataFrame instance method, with the calling DataFrame being implicitly
considered the left object in the join.
The related DataFrame.join method, uses merge internally for the
index-on-index (by default) and column(s)-on-index join. If you are joining on
index only, you may wish to use DataFrame.join to save yourself some typing.
Brief primer on merge methods (relational algebra)
Experienced users of relational databases like SQL will be familiar with the
terminology used to describe join operations between two SQL-table like
structures (DataFrame objects). There are several cases to consider which are
very important to understand:
one-to-one joins: for example when joining two DataFrame objects on
their indexes (which must contain unique values)
many-to-one joins: for example when joining an index (unique) to one or
more columns in a DataFrame
many-to-many joins: joining columns on columns.
When joining columns on columns (potentially a many-to-many join), any
indexes on the passed DataFrame objects will be discarded.
It is worth spending some time understanding the result of the many-to-many
join case. In SQL / standard relational algebra, if a key combination appears
more than once in both tables, the resulting table will have the Cartesian
product of the associated data. Here is a very basic example with one unique
key combination:
In [38]: left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
In [39]: right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
In [40]: result = pd.merge(left, right, on='key')
Here is a more complicated example with multiple join keys:
In [41]: left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
'key2': ['K0', 'K1', 'K0', 'K1'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
In [42]: right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
'key2': ['K0', 'K0', 'K0', 'K0'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
In [43]: result = pd.merge(left, right, on=['key1', 'key2'])
The how argument to merge specifies how to determine which keys are to
be included in the resulting table. If a key combination does not appear in
either the left or right tables, the values in the joined table will be
NA. Here is a summary of the how options and their SQL equivalent names:
LEFT OUTER JOIN
Use keys from left frame only
RIGHT OUTER JOIN
Use keys from right frame only
FULL OUTER JOIN
Use union of keys from both frames
INNER JOIN
Use intersection of keys from both frames
In [44]: result = pd.merge(left, right, how='left', on=['key1', 'key2'])
In [45]: result = pd.merge(left, right, how='right', on=['key1', 'key2'])
In [46]: result = pd.merge(left, right, how='outer', on=['key1', 'key2'])
In [47]: result = pd.merge(left, right, how='inner', on=['key1', 'key2'])
Here is another example with duplicate join keys in DataFrames:
In [48]: left = pd.DataFrame({'A' : [1,2], 'B' : [2, 2]})
In [49]: right = pd.DataFrame({'A' : [4,5,6], 'B': [2,2,2]})
In [50]: result = pd.merge(left, right, on='B', how='outer')
Joining / merging on duplicate keys can cause a returned frame that is the multiplication of the row dimensions,
may result in memory overflow. It is the user’ s responsibility to manage duplicate values in keys before joining large DataFrames.
The merge indicator
New in version 0.17.0.
merge now accepts the argument indicator. If True, a Categorical-type column called _merge will be added to the output object that takes on values:
Merge key only in 'left' frame
Merge key only in 'right' frame
right_only
Merge key in both frames
In [51]: df1 = pd.DataFrame({'col1': [0, 1], 'col_left':['a', 'b']})
In [52]: df2 = pd.DataFrame({'col1': [1, 2, 2],'col_right':[2, 2, 2]})
In [53]: pd.merge(df1, df2, on='col1', how='outer', indicator=True)
col1 col_left
right_only
right_only
The indicator argument will also accept string arguments, in which case the indicator function will use the value of the passed string as the name for the indicator column.
In [54]: pd.merge(df1, df2, on='col1', how='outer', indicator='indicator_column')
col1 col_left
col_right indicator_column
right_only
right_only
Merge Dtypes
New in version 0.19.0.
Merging will preserve the dtype of the join keys.
In [55]: left = pd.DataFrame({'key': [1], 'v1': [10]})
In [56]: left
In [57]: right = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]})
In [58]: right
We are able to preserve the join keys
In [59]: pd.merge(left, right, how='outer')
In [60]: pd.merge(left, right, how='outer').dtypes
dtype: object
Of course if you have missing values that are introduced, then the
resulting dtype will be upcast.
In [61]: pd.merge(left, right, how='outer', on='key')
In [62]: pd.merge(left, right, how='outer', on='key').dtypes
dtype: object
New in version 0.20.0.
Merging will preserve category dtypes of the mergands. See also the section on
The left frame.
In [63]: X = pd.Series(np.random.choice(['foo', 'bar'], size=(10,)))
In [64]: X = X.astype('category', categories=['foo', 'bar'])
In [65]: left = pd.DataFrame({'X': X,
'Y': np.random.choice(['one', 'two', 'three'], size=(10,))})
In [66]: left
In [67]: left.dtypes
dtype: object
The right frame.
In [68]: right = pd.DataFrame({'X': pd.Series(['foo', 'bar']).astype('category', categories=['foo', 'bar']),
'Z': [1, 2]})
In [69]: right
In [70]: right.dtypes
dtype: object
The merged result
In [71]: result = pd.merge(left, right, how='outer')
In [72]: result
In [73]: result.dtypes
dtype: object
The category dtypes must be exactly the same, meaning the same categories and the ordered attribute.
Otherwise the result will coerce to object dtype.
Merging on category dtypes that are the same can be quite performant compared to object dtype merging.
Joining on index
DataFrame.join is a convenient method for combining the columns of two
potentially differently-indexed DataFrames into a single result DataFrame. Here
is a very basic example:
In [74]: left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=['K0', 'K1', 'K2'])
In [75]: right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
'D': ['D0', 'D2', 'D3']},
index=['K0', 'K2', 'K3'])
In [76]: result = left.join(right)
In [77]: result = left.join(right, how='outer')
In [78]: result = left.join(right, how='inner')
The data alignment here is on the indexes (row labels). This same behavior can
be achieved using merge plus additional arguments instructing it to use the
In [79]: result = pd.merge(left, right, left_index=True, right_index=True, how='outer')
In [80]: result = pd.merge(left, right, left_index=True, right_index=True, how='inner');
Joining key columns on an index
join takes an optional on argument which may be a column or multiple
column names, which specifies that the passed DataFrame is to be aligned on
that column in the DataFrame. These two function calls are completely
equivalent:
left.join(right, on=key_or_keys)
pd.merge(left, right, left_on=key_or_keys, right_index=True,
how='left', sort=False)
Obviously you can choose whichever form you find more convenient. For
many-to-one joins (where one of the DataFrame’s is already indexed by the join
key), using join may be more convenient. Here is a simple example:
In [81]: left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'key': ['K0', 'K1', 'K0', 'K1']})
In [82]: right = pd.DataFrame({'C': ['C0', 'C1'],
'D': ['D0', 'D1']},
index=['K0', 'K1'])
In [83]: result = left.join(right, on='key')
In [84]: result = pd.merge(left, right, left_on='key', right_index=True,
how='left', sort=False);
To join on multiple keys, the passed DataFrame must have a MultiIndex:
In [85]: left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'key1': ['K0', 'K0', 'K1', 'K2'],
'key2': ['K0', 'K1', 'K0', 'K1']})
In [86]: index = pd.MultiIndex.from_tuples([('K0', 'K0'), ('K1', 'K0'),
('K2', 'K0'), ('K2', 'K1')])
In [87]: right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index)
Now this can be joined by passing the two key column names:
In [88]: result = left.join(right, on=['key1', 'key2'])
The default for DataFrame.join is to perform a left join (essentially a
“VLOOKUP” operation, for Excel users), which uses only the keys found in the
calling DataFrame. Other join types, for example inner join, can be just as
easily performed:
In [89]: result = left.join(right, on=['key1', 'key2'], how='inner')
As you can see, this drops any rows where there was no match.
Joining a single Index to a Multi-index
New in version 0.14.0.
You can join a singly-indexed DataFrame with a level of a multi-indexed DataFrame.
The level will match on the name of the index of the singly-indexed frame against
a level name of the multi-indexed frame.
In [90]: left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=pd.Index(['K0', 'K1', 'K2'], name='key'))
In [91]: index = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),
('K2', 'Y2'), ('K2', 'Y3')],
names=['key', 'Y'])
In [92]: right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index)
In [93]: result = left.join(right, how='inner')
This is equivalent but less verbose and more memory efficient / faster than this.
In [94]: result = pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner').set_index(['key','Y'])
Joining with two multi-indexes
This is not Implemented via join at-the-moment, however it can be done using the following.
In [95]: index = pd.MultiIndex.from_tuples([('K0', 'X0'), ('K0', 'X1'),
('K1', 'X2')],
names=['key', 'X'])
In [96]: left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=index)
In [97]: result = pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner').set_index(['key','X','Y'])
Overlapping value columns
The merge suffixes argument takes a tuple of list of strings to append to
overlapping column names in the input DataFrames to disambiguate the result
In [98]: left = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'v': [1, 2, 3]})
In [99]: right = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'v': [4, 5, 6]})
In [100]: result = pd.merge(left, right, on='k')
In [101]: result = pd.merge(left, right, on='k', suffixes=['_l', '_r'])
DataFrame.join has lsuffix and rsuffix arguments which behave
similarly.
In [102]: left = left.set_index('k')
In [103]: right = right.set_index('k')
In [104]: result = left.join(right, lsuffix='_l', rsuffix='_r')
Joining multiple DataFrame or Panel objects
A list or tuple of DataFrames can also be passed to DataFrame.join to join
them together on their indexes. The same is true for Panel.join.
In [105]: right2 = pd.DataFrame({'v': [7, 8, 9]}, index=['K1', 'K1', 'K2'])
In [106]: result = left.join([right, right2])
Merging together values within Series or DataFrame columns
Another fairly common situation is to have two like-indexed (or similarly
indexed) Series or DataFrame objects and wanting to “patch” values in one
object from values for matching indices in the other. Here is an example:
In [107]: df1 = pd.DataFrame([[np.nan, 3., 5.], [-4.6, np.nan, np.nan],
[np.nan, 7., np.nan]])
In [108]: df2 = pd.DataFrame([[-42.6, np.nan, -8.2], [-5., 1.6, 4]],
index=[1, 2])
For this, use the combine_first method:
In [109]: result = df1.combine_first(df2)
Note that this method only takes values from the right DataFrame if they are
missing in the left DataFrame. A related method, update, alters non-NA
values inplace:
In [110]: df1.update(df2)
Merging AsOf
New in version 0.19.0.
is similar to an ordered 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 an asof merge can perform a group-wise merge. This matches the by key equally,
in addition to the nearest match on the on key.
F we might have trades and quotes and we want to asof merge them.
In [114]: trades = pd.DataFrame({
'time': pd.to_datetime([':30:00.023',
':30:00.038',
':30:00.048',
':30:00.048',
':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'])
In [115]: quotes = pd.DataFrame({
'time': pd.to_datetime([':30:00.023',
':30:00.023',
':30:00.030',
':30:00.041',
':30:00.048',
':30:00.049',
':30:00.072',
':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'])
In [116]: trades
time ticker
13:30:00.023
13:30:00.038
13:30:00.048
13:30:00.048
13:30:00.048
In [117]: quotes
time ticker
13:30:00.023
13:30:00.023
13:30:00.030
13:30:00.041
13:30:00.048
13:30:00.049
13:30:00.072
13:30:00.075
By default we are taking the asof of the quotes.
In [118]: pd.merge_asof(trades, quotes,
on='time',
by='ticker')
time ticker
13:30:00.023
13:30:00.038
13:30:00.048
13:30:00.048
13:30:00.048
We only asof within 2ms betwen the quote time and the trade time.
In [119]: pd.merge_asof(trades, quotes,
on='time',
by='ticker',
tolerance=pd.Timedelta('2ms'))
time ticker
13:30:00.023
13:30:00.038
13:30:00.048
13:30:00.048
13:30:00.048
We only asof within 10ms betwen the quote time and the trade time and we exclude exact matches on time.
Note that though we exclude the exact matches (of the quotes), prior quotes DO propogate to that point
In [120]: pd.merge_asof(trades, quotes,
on='time',
by='ticker',
tolerance=pd.Timedelta('10ms'),
allow_exact_matches=False)
time ticker
13:30:00.023
13:30:00.038
13:30:00.048
13:30:00.048
13:30:00.048}