pandasMerge, join, and concatenate


Syntax

  • DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False)

  • Merge DataFrame objects by performing a database-style join operation by columns or indexes.

  • If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on.

Parameters

ParametersExplanation
rightDataFrame
how{‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’
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_indexboolean, default False. Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels
right_indexboolean, default False. Use the index from the right DataFrame as the join key. Same caveats as left_index
sortboolean, default Fals. Sort the join keys lexicographically in the result DataFrame
suffixes2-length sequence (tuple, list, ...). Suffix to apply to overlapping column names in the left and right side, respectively
copyboolean, default True. If False, do not copy data unnecessarily
indicatorboolean or string, default False. If True, adds a column to output DataFrame called “_merge” with information on the source of each row. If string, column with information on source of each row will be added to output DataFrame, and column will be named value of string. Information column 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.