pandas.merge_ordered — pandas 1.0.5 documentation (original) (raw)

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: str = 'outer') → ’DataFrame’[source]¶

Perform merge with optional filling/interpolation.

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

Parameters

leftDataFrame

rightDataFrame

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 of vectors of the length of the DataFrame to use a particular vector as the 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 per left_on docs.

left_bycolumn name or list of column names

Group left DataFrame by group columns and merge piece by piece with right DataFrame.

right_bycolumn 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.

suffixesSequence, default is (“_x”, “_y”)

A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names inleft and right respectively. Pass a value of None instead of a string to indicate that the column name from left orright should be left as-is, with no suffix. At least one of the values must not be None.

Changed in version 0.25.0.

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 the be same as ‘left’, if it is a subclass of DataFrame.

Examples

A key lvalue group 0 a 1 a 1 c 2 a 2 e 3 a 3 a 1 b 4 c 2 b 5 e 3 b

B Key rvalue 0 b 1 1 c 2 2 d 3

merge_ordered(A, B, fill_method='ffill', left_by='group') group key lvalue rvalue 0 a a 1 NaN 1 a b 1 1.0 2 a c 2 2.0 3 a d 2 3.0 4 a e 3 3.0 5 b a 1 NaN 6 b b 1 1.0 7 b c 2 2.0 8 b d 2 3.0 9 b e 3 3.0