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pandas.merge_ordered(left, right, on=None, left_on=None, right_on=None, left_by=None, right_by=None, fill_method=None, suffixes=('_x', '_y'), how='outer')
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Perform merge with optional filling/interpolation designed for ordered data like time series data. Optionally perform group-wise merge (see examples)
Parameters: left : DataFrame
right : DataFrame
on : label or list
Field names to join on. Must be found in both DataFrames.
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
left_by : column name or list of column names
Group left DataFrame by group columns and merge piece by piece with right DataFrame
right_by : column name or list of column names
Group right DataFrame by group columns and merge piece by piece with left DataFrame
fill_method : {?ffill?, None}, default None
Interpolation method for data
suffixes : 2-length sequence (tuple, list, ...)
Suffix to apply to overlapping column names in the left and right side, respectively
how : {?left?, ?right?, ?outer?, ?inner?}, default ?outer?
- left: use only keys from left frame (SQL: left outer join)
- right: use only keys from right frame (SQL: right outer join)
- outer: use union of keys from both frames (SQL: full outer join)
- inner: use intersection of keys from both frames (SQL: inner join)
New in version 0.19.0.
Returns: merged : DataFrame
The output type will the be same as ?left?, if it is a subclass of DataFrame.
See also
Examples
>>> A >>> B key lvalue group key rvalue 0 a 1 a 0 b 1 1 c 2 a 1 c 2 2 e 3 a 2 d 3 3 a 1 b 4 c 2 b 5 e 3 b
>>> ordered_merge(A, B, fill_method='ffill', left_by='group') key lvalue group rvalue 0 a 1 a NaN 1 b 1 a 1 2 c 2 a 2 3 d 2 a 3 4 e 3 a 3 5 f 3 a 4 6 a 1 b NaN 7 b 1 b 1 8 c 2 b 2 9 d 2 b 3 10 e 3 b 3 11 f 3 b 4
pandas.merge_ordered()
2017-01-12 04:50:28
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