right : DataFrame

how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’

• left: use only keys from left frame, similar to a SQL left outer join; preserve key order
• right: use only keys from right frame, similar to a SQL right outer join; preserve key order
• outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically
• inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys

on : label or list

Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.

left_on : label or list, or array-like

Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.

right_on : label or list, or array-like

Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.

left_index : boolean, 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_index : boolean, default False

Use the index from the right DataFrame as the join key. Same caveats as left_index

sort : boolean, default False

Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword)

suffixes : 2-length sequence (tuple, list, …)

Suffix to apply to overlapping column names in the left and right side, respectively

copy : boolean, default True

If False, do not copy data unnecessarily

indicator : boolean 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.

validate : string, default None

If specified, checks if merge is of specified type.

• “one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
• “one_to_many” or “1:m”: check if merge keys are unique in left dataset.
• “many_to_one” or “m:1”: check if merge keys are unique in right dataset.
• “many_to_many” or “m:m”: allowed, but does not result in checks.

New in version 0.21.0.

merged : DataFrame

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

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import numpy as np

import pandas as pd

loanstats=pd.DataFrame(pd.read_excel('loanStats.xlsx'))

member_grade=pd.DataFrame(pd.read_excel('member_grade.xlsx'))

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loan_inner=pd.merge(loanstats,member_grade,how='inner')

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loan_left=pd.merge(loanstats,member_grade,how='left')

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loan_right=pd.merge(loanstats,member_grade,how='right')

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loan_outer=pd.merge(loanstats,member_grade,how='outer')

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loan_left=pd.merge(loanstats,member_grade,how='left')