Vlookup is essentially used for vertically arranged data. Vlookup is an operation used to merge 2 different data tables based on some condition where there must be at least 1 common attribute(column) between the two tables. After performing this operation we get a table consisting of all the data from both the tables for which the data is matched.
We can use merge() function to perform Vlookup in pandas. The merge function does the same job as the Join in SQL We can perform the merge operation with respect to table 1 or table 2.There can be different ways of merging the 2 tables.
Syntax: dataframe.merge(dataframe1, dataframe2, how, on, copy, indicator, suffixes, validate)
Parameters:
datafram1: dataframe object to be merged with.
dataframe2: dataframe object to be merged.
how: {left, right, inner, outer} specifies how merging will be done
on: specifies column or index names used for performing join.
suffixes: suffix used for overlapping columns.For exception use values (False, False).
validate: If specified, checks the kind of merging.The type of merge could be (one-one, one-many, many-one, many-many).
Let’s consider 2 tables on which the operation is to be performed. 1st table consists of the information of students and 2nd column consists of the information of the respective Courses they are enrolled in. The below code tells the information contained in both the tables.
Python3
# import pandasimport pandas as pd # read csv datadf1 = pd.read_csv('Student_data.csv')df2 = pd.read_csv('Course_enrolled.csv') print(df1)print(df2) |
Output
Performing a Vlook on different types of Joins
- Inner join: Inner join produces an output data frame of only those rows for which the condition is satisfied in both the rows. To perform inner join you may specify inner as a keyword in how.
Example:
Python3
# import pandasimport pandas as pd # read csv datadf1 = pd.read_csv('Student_data.csv')df2 = pd.read_csv('Course_enrolled.csv') inner_join = pd.merge(df1, df2, on ='Name', how ='inner')inner_join |
- Output
- Left join: Left join operation provides all the rows from 1st dataframe and matching rows from the 2nd dataframe. If the rows are not matched in the 2nd dataframe then they will be replaced by NaN.
Example:
Python3
# import pandasimport pandas as pd # read csv datadf1 = pd.read_csv('Student_data.csv')df2 = pd.read_csv('Course_enrolled.csv') Left_join = pd.merge(df1, df2, on ='Name', how ='left')Left_join |
- Output:
- Right join: Right join is somewhat similar to left join in which the output dataframe will consist of all the rows from the 2nd dataframe and matching rows from the 1st dataframe. If the rows are not matched in 1st row then they will be replaced by NaN
Python3
# import pandasimport pandas as pd # read csv datadf1 = pd.read_csv('Student_data.csv')df2 = pd.read_csv('Course_enrolled.csv') Right_join = pd.merge(df1, df2, on ='Name', how ='right')Right_join |
- Output
- Outer join: Outer join provides the output dataframe consisting of rows from both the dataframes. Values will be shown if rows are matched otherwise NaN will be shown for rows that do not match.
Example:
Python3
# import pandasimport pandas as pd # read csv datadf1 = pd.read_csv('Student_data.csv')df2 = pd.read_csv('Course_enrolled.csv') Outer_join = pd.merge(df1, df2, on ='Name', how ='outer')Outer_join |
- Output
