pandas.merge_ordered#

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')[source]#

Perform a merge for ordered data with optional filling/interpolation.

Designed for ordered data like time series data. Optionallyperform group-wise merge (see examples).

Parameters:

leftDataFrame or named Series

rightDataFrame or named Series

onlabel or list

Field names to join on. Must be found in both DataFrames.

left_onlabel or list, or array-like

Field names to join on in left DataFrame. Can be a vector or list ofvectors of the length of the DataFrame to use a particular vector asthe join key instead of columns.

right_onlabel or list, or array-like

Field names to join on in right DataFrame or vector/list of vectors perleft_on docs.

left_bycolumn name or list of column names

Group left DataFrame by group columns and merge piece by piece withright DataFrame. Must be None if either left or right are a Series.

right_bycolumn name or list of column names

Group right DataFrame by group columns and merge piece by piece withleft DataFrame. Must be None if either left or right are a Series.

fill_method{‘ffill’, None}, default None

Interpolation method for data.

suffixeslist-like, default is (“_x”, “_y”)

A length-2 sequence where each element is optionally a stringindicating the suffix to add to overlapping column names inleft andright respectively. Pass a value ofNone insteadof a string to indicate that the column name fromleft orright should be left as-is, with no suffix. At least one of thevalues must not be None.

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).

Returns:

DataFrame

The merged DataFrame output type will be the same as‘left’, if it is a subclass of DataFrame.

See also

merge

Merge with a database-style join.

merge_asof

Merge on nearest keys.

Examples

>>>frompandasimportmerge_ordered>>>df1=pd.DataFrame(...{..."key":["a","c","e","a","c","e"],..."lvalue":[1,2,3,1,2,3],..."group":["a","a","a","b","b","b"]...}...)>>>df1  key  lvalue group0   a       1     a1   c       2     a2   e       3     a3   a       1     b4   c       2     b5   e       3     b

>>>df2=pd.DataFrame({"key":["b","c","d"],"rvalue":[1,2,3]})>>>df2  key  rvalue0   b       11   c       22   d       3

>>>merge_ordered(df1,df2,fill_method="ffill",left_by="group")  key  lvalue group  rvalue0   a       1     a     NaN1   b       1     a     1.02   c       2     a     2.03   d       2     a     3.04   e       3     a     3.05   a       1     b     NaN6   b       1     b     1.07   c       2     b     2.08   d       2     b     3.09   e       3     b     3.0