Simply concatenated both the tables based on their index. OTHER TYPES OF JOINS & CONCATENATION IN PANDAS PYTHON Join based on Index in pandas python (Row index) : Right_join_df= pd.merge(df1, df2, on='Customer_id', how='right') Return all rows from the right table, and any rows with matching keys from the left table. Left_join_df= pd.merge(df1, df2, on='Customer_id', how='left') Return all rows from the left table, and any rows with matching keys from the right table.When there is no Matching from right table NaN will be returned # left join in python Outer_join_df=pd.merge(df1, df2, on='Customer_id', how='outer') Returns all rows from both tables, join records from the left which have matching keys in the right table.When there is no Matching from any table NaN will be returned # outer join in python pandas Inner_join_df= pd.merge(df1, df2, on='Customer_id', how='inner') Return only the rows in which the left table have matching keys in the right table #inner join in python pandas Lets try different Merge or join operation with an example: Create dataframe: import pandas as pdĭ1 = Right Join or Right outer join :To include all the rows of your data frame y and only those from x that match, specify how= ‘right’.Left Join or Left outer join :To include all the rows of your data frame x and only those from y that match, specify how= ‘left’.Outer Join or Full outer join :To keep all rows from both data frames, specify how= ‘outer’.Inner Join or Natural join: To keep only rows that match from the data frames, specify the argument how= ‘inner’.UNDERSTANDING THE DIFFERENT TYPES OF JOIN OR MERGE IN PANDAS: Merge() Function in pandas is similar to database join operation in SQL. The data frames must have same column names on which the merging happens. how – type of join needs to be performed – ‘left’, ‘right’, ‘outer’, ‘inner’, Default is inner join Must be found in both the left and right DataFrame objects. Left_df – Dataframe1 right_df– Dataframe2. Merge(left_df, right_df, on=’Customer_id’, how=’inner’)
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