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Pandas: Data Manipulation - merge_ordered() function

merge_ordered() function

Perform merge with optional filling/interpolation designed for ordered data like time series data. Optionally perform group-wise merge.

Syntax:

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')

Parameters:

Name Description Type Default Value Required / Optional
left DataFrame   Required
right DataFrame   Required
on Field names to join on. Must be found in both DataFrames. label or list   Required
left_on 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. label or list, or array-like Optional
right_on Field names to join on in right DataFrame or vector/list of vectors per left_on docs label or list, or array-like Optional
left_by Group left DataFrame by group columns and merge piece by piece with right DataFrame column name or list of column names Optional
right_by Group right DataFrame by group columns and merge piece by piece with left DataFrame. column name or list of column names Optional
fill_method Interpolation method for data. {‘ffill’, None} Default: None Optional
suffixes A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None. Sequence Default: (“_x”, “_y”) Optional
how
  • 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)
{‘left’, ‘right’, ‘outer’, ‘inner’}, Default: ‘outer’ Optional

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

Example:


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