Concatenating CSV files using Pandas module

02 Dec, 2020
Using pandas we can perform various operations on CSV files such as appending, updating, concatenating, etc. In this article, we are going to concatenate two CSV files using pandas module.
Suppose we have one .csv file named Employee.csv which contains some records and it is as below:
Employee.csv
There is another .csv file named Updated.csv which contains new records as well as few records from Employee.csv file but with updated information. The file is given below:
Updated.csv
We can see that the first five records in Updated.csv are new and the rest have updated information. For instance, the salaries of Louis and Diane are changed, email_id of Joe is different and so on.
The aim of this article is to add new records and update the information of existing records from Updated.csv file into Employee.csv. 
Note: No two employees can have same emp_id.
Approach: Whenever it comes down to manipulate data using python we make use of Dataframes. The below approach has been used. 
Read Employee.csv and create a dataframe, say, employee_df..
Similarly, read Updated.csv and from a dataframe, say, updated_df.
Concatenate updated_df to employee_df and remove duplicates using emp_id as primary key.
Create a new .csv file named Updated_Employees.csv containing all the old, new as well as updated records.
Example 1:
Python3
#import pandasimport pandas as pd  # read Employee fileemployee_df = pd.read_csv('Employee.csv')  # print employee recordsprint(employee_df)  # read Updated fileupdated_df = pd.read_csv('Updated.csv')  # print updated recordsprint(updated_df)  # form new dataframe by combining both employee_df and updated_df# concat method appends records of updated_df to employee_df# drop_duplicates method drop rows having same emp_id keeping # only the latest insertions# resets the index to 0final_dataframe = pd.concat([employee_df, updated_df]).drop_duplicates(    subset='emp_id', keep='last').reset_index(drop=True)  # print old,new and updates recordsprint(final_dataframe)  # export all records to a new csv filefinal_dataframe.to_csv(    'Updated_Employees.csv', index=False)
Output:
employee_df
updated_df
final_dataframe
Below is the image of Updated_Employee.csv has been provided.
Updated_Employees.csv
Example:
Below are the two CSV files which are going to be concatenated:
gfg3.csv
gfg2.csv
Now executing the below program to concatenate the above CSV files.
Python3
#import pandasimport pandas as pd  # read Employee filedf1 = pd.read_csv('gfg1.csv')  # print employee recordsprint('\ngfg1.csv:\n', df1)  # read Updated filedf2 = pd.read_csv('gfg2.csv')  # print updated recordsprint('\ngfg2.csv:\n', df2)  # form new dataframe by combining both employee_df# and updated_df concat method appends records of# updated_df to employee_df drop_duplicates method # drop rows having same emp_id keeping only the# latest insertions resets the index to 0final_df = pd.concat([df1, df2]).drop_duplicates(    subset='ORGANIZATION').reset_index(drop=True)  # print old,new and updates recordsprint('\ngfg3.csv:\n', final_df)  # export all records to a new csv filefinal_df.to_csv(    'gfg3.csv', index=False)
Output:
Below is the image of gfg3.csv:
gfg3.csv
Python pandas-io
Python-pandas
Python
Writing code in comment?
Please use ide.geeksforgeeks.org,
generate link and share the link here.