Python: How to append a new row to an existing csv file?

This tutorial will help you learn how to append a new row to an existing CSV file using some CSV modules like reader/writer and the most famous DictReader/DictWriter classes. Moreover, you can also get enough knowledge on all python concepts by visiting our provided tutorials.

How to Append a new row to an existing csv file?

There are multiple ways in Python by which we can append rows into the CSV file. But here we will discuss two effective methods. Before going to learn those two methods, we have to follow the standard step which is explained ahead.

The basic step to proceed in this is to have a CSV file. For instance, here we have a CSV file named students.csv having the following contents:

Id,Name,Course,City,Session
21,Mark,Python,London,Morning
22,John,Python,Tokyo,Evening
23,Sam,Python,Paris,Morning

For reading and writing CSV files python provides a CSV module. There are two different classes for writing CSV files that is writer and DictWriter.

We can append the rows in a CSV file by either of them but some solutions are better than the other. We will see it in the next section.

Do Refer:

Append a list as a new row to an old CSV file using csv.writer()

A writer class is in the CSV module which writes the rows in existing CSV files.

Let’s take a list of strings:

# List of strings
row_contents = [32,'Shaun','Java','Tokyo','Morning']

To add this list to an existing CSV file, we have to follow certain steps:

  • Import CSV module’s writer class.
  • Open our csv file in append mode and create a file object.
  • Pass this file object to the csv.writer(), we can get a writer class object.
  • This writer object has a function writerow(), pass the list to it and it will add the list’s contents as a new row in the associated csv file.
  • A new row is added in the csv file, now close the file object.

By following the above steps, the list will be appended as a row in the CSV file as it is a simple process.

from csv import writer

def append_list_as_row(file_name, list_of_elem):
    # Open file in append mode
    with open(file_name, 'a+', newline='') as write_obj:
        # Create a writer object from csv module
        csv_writer = writer(write_obj)
        # Add contents of list as last row in the csv file
        csv_writer.writerow(list_of_elem)

Another Code:

Append a list as new row to an old csv file using csv.writer()

We can see that the list has been added.

Appending a row to csv with missing entries?

Suppose we have a list that does not contain all the values and we have to append it into the CSV file.

Suppose the list is:

list = [33, ‘Sahil’, ‘Morning’]

Example:

# A list with missing entries
row_contents = [33, 'Sahil', 'Morning']
# Appending a row to csv with missing entries
append_list_as_row('students.csv', row_contents)

some entries are missing in the list

Output:

output of missing files

We can see the data get appended at the wrong positions as the session got appended at the course.

csv’s writer class has no functionality to check if any of the intermediate column values are missing in the list or if they are in the correct order. It will just add the items in the list as column values of the last row in sequential order.

Therefore while adding a list as a row using csv.writer() we need to make sure that all elements are provided and are in the correct order.

If any element is missing like in the above example, then we should pass empty strings in the list like this,

row_contents = [33, 'Sahil', '' , '', 'Morning']

Since we have a huge amount of data in the CSV file, adding the empty strings in all of that will be a hectic task.

To save us from hectic work, the CSV provided us with the DictWriter class.

Append a dictionary as a row to an existing csv file using DictWriter in python

As the name suggests, we can append a dictionary as a row to an existing CSV file using DictWriter in Python. Let’s see how we can use them.

Suppose, we have a dictionary-like below,

{'Id': 81,'Name': 'Sachin','Course':'Maths','City':'Mumbai','Session':'Evening'}

We can see that the keys are the columns of the CSV and the values will be the ones we will provide.

To append it, we have to follow some steps given below:

  • import csv module’s DictWriter class,
  • Open our csv file in append mode and create a file object,
  • Pass the file object & a list of csv column names to the csv.DictWriter(), we can get a DictWriter class object
  • This DictWriter object has a function writerow() that accepts a dictionary. pass our dictionary to this function, it adds them as a new row in the associated csv file,
  • A new line is added in the csv file, now close the file object,

The above steps will append our dictionary as a new row in the csv. To make our life easier, we have created a separate function that performs the above steps,

Code:

from csv import DictWriter
def append_dict_as_row(file_name, dict_of_elem, field_names):
    # Open file in append mode
    with open(file_name, 'a+', newline='') as write_obj:
        # Create a writer object from csv module
        dict_writer = DictWriter(write_obj, fieldnames=field_names)
        # Add dictionary as wor in the csv
        dict_writer.writerow(dict_of_elem)

Append a dictionary as a row to an existing csv file using DictWriter in python

Output:

output of appending the dict

We can see that it added the row successfully. We can also consider this thought that what if our dictionary will have any missing entries? Or the items are in a different order?

The advantage of using DictWriter is that it will automatically handle the sort of things and columns with missing entries will remain empty. Let’s check an example:

field_names = ['Id','Name','Course','City','Session']
row_dict = {'Id': 81,'Name': 'Sachin','Course':'Maths','City':'Mumbai','Session':'Evening'}
# Append a dict as a row in csv file
append_dict_as_row('students.csv', row_dict, field_names)

Output:

column with missing entries will remain empty

We can see this module has its wonders.

Hope this article was useful and informative for you.