Inner Join
An inner join requires two data set columns to be the same to fetch the common row data values or data from the data table. In simple words, and returns a data frame or values with only those rows in the data frame that have common characteristics and behavior desired by the user. This is similar to the intersection of two sets in mathematics. In short, we can say that Inner Join on column Id will return columns from both the tables and only the matching records:
Inner Join
Example:
Suppose two companies are conducting an inter-company cricket tournament and the employees who have participated gave their names in the data set table. Now in the table, we have two or more similar Id. Now we have two sets of data tables. And we want the data of all the employees with the same ID in two different companies so that we can easily differentiate the same id in different companies. In such a scenario we will use the inner join concept to get all the details of such employees.
Python3
# importing pandas as pds import pandas as pds # Creating dataframe for the data_set first data_Set1 = pds.DataFrame() # Creating data list for the table 1 # here Id 101 and 102 will be same like # in data set 2 schema = {'Id': [101, 102, 106, 112], 'DATA 1': ['Abhilash', 'Raman', 'Pratap', 'James']} data_Set1 = pds.DataFrame(schema) print("Data Set-1 \n", data_Set1, "\n") # Creating dataframe data_set second data_Set2 = pds.DataFrame() # Creating data list for the table 2 # here Id 101 and 102 will be same like # in data set 1 schema = {'Id': [101, 102, 109, 208], 'DATA 2': ['Abhirav', 'Abhigyan', 'John', 'Peter']} data_Set2 = pds.DataFrame(schema) print("Data Set-2 \n", data_Set2, "\n") # inner join in python inner_join = pds.merge(data_Set1, data_Set2, on='Id', how='inner') # display dataframe pds.DataFrame(inner_join) |
Output:
Python Pandas – Difference between INNER JOIN and LEFT SEMI JOIN
In this article, we see the difference between INNER JOIN and LEFT SEMI JOIN.
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