How to append a list as a row to a Pandas DataFrame in Python?

Last Updated : 02 Mar, 2023

In this article, We are going to see how to append a list as a row to a pandas dataframe in Python. It can be done in three ways:

Using loc[]
Using iloc[]
Using append()

Append list using loc[] methods

Pandas DataFrame.loc attribute access a group of rows and columns by label(s) or a boolean array in the given DataFrame.

Let's append the list with step-wise:

Step 1: Create a simple dataframe using the list.

Python3

import pandas as pd

# List
Person = [ ['Satyam', 21, 'Patna' , 'India' ],
            ['Anurag', 23, 'Delhi' , 'India' ],
            ['Shubham', 27, 'Coimbatore' , 'India' ]]

#Create a DataFrame object
df = pd.DataFrame(Person, 
                  columns = ['Name' , 'Age', 'City' , 'Country']) 

# display
display(df)

Output:

Step 2: Using loc to append the new list to a data frame.

Python3

# New list for append into df
list = ["Saurabh", 23, "Delhi", "india"]

# using loc methods
df.loc[len(df)] = list

# display
display(df)

Output:

Append list using iloc[] methods

Pandas DataFrame.iloc method access integer-location based indexing for selection by position.

Example:

Python3

# import module
import pandas as pd

# List
Person = [ ['Satyam', 21, 'Patna' , 'India' ],
            ['Anurag', 23, 'Delhi' , 'India' ],
            ['Shubham', 27, 'Coimbatore' , 'India' ],
            ["Saurabh", 23, "Delhi", "india"]]

#Create a DataFrame object
df = pd.DataFrame(Person, 
                  columns = ['Name' , 'Age', 'City' , 'Country']) 

# new list to append into df
list = ['Ujjawal', 22, 'Fathua', 'India']

# using iloc
df.iloc[2] = list

# display
display(df)

Output:

Note - It is used for location-based indexing so it works for only the existing index and replaces the row element.

Append list using append() methods

Pandas dataframe.append() function is used to append rows of other dataframe to the end of the given dataframe, returning a new dataframe object.

Example:

Python3

# import module
import pandas as pd

# List
Person = [ ['Satyam', 21, 'Patna' , 'India' ],
            ['Anurag', 23, 'Delhi' , 'India' ],
            ['Shubham', 27, 'Coimbatore' , 'India' ]]

#Create a DataFrame object
df = pd.DataFrame(Person, 
                  columns = ['Name' , 'Age', 'City' , 'Country']) 

# new list to append into df
list = [["Manjeet", 25, "Delhi", "india"]]

# using append
df = df.append(pd.DataFrame( list, 
               columns=[ 'Name', 'Age', 'City', 'Country']),
               ignore_index = True)

# display df
display(df)

Output:

Time complexity:

Appending a dataframe to another dataframe is a constant time operation, as it requires only the addition of one row (or multiple rows) to the existing dataframe. Hence the time complexity of this operation is O(1).

Space complexity:

The space complexity of this operation is also O(1), as it only requires the addition of one row of data to the existing dataframe.