How to append a list to a Pandas DataFrame using loc in Python?

The DataFrame.loc accessor is used to access a group of rows and columns by label or a boolean array. We can use it to append a list as a new row to an existing DataFrame by specifying the next available index position.

Creating the Initial DataFrame

Let us first create a DataFrame with team ranking data ?

import pandas as pd

# Data in the form of list of team rankings
team_data = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], 
             ['New Zealand', 4, 65], ['South Africa', 5, 50], ['Bangladesh', 6, 40]]

# Creating a DataFrame and adding columns
dataFrame = pd.DataFrame(team_data, columns=['Country', 'Rank', 'Points'])
print("Original DataFrame:")
print(dataFrame)

Original DataFrame:
       Country  Rank  Points
0        India     1     100
1    Australia     2      85
2      England     3      75
3  New Zealand     4      65
4 South Africa     5      50
5   Bangladesh     6      40

Appending a List Using loc

To append a new row, we use len(dataFrame) as the index position, which gives us the next available row index ?

import pandas as pd

# Data in the form of list of team rankings
team_data = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], 
             ['New Zealand', 4, 65], ['South Africa', 5, 50], ['Bangladesh', 6, 40]]

# Creating a DataFrame
dataFrame = pd.DataFrame(team_data, columns=['Country', 'Rank', 'Points'])

# Row to be appended as a list
new_team = ["Sri Lanka", 7, 30]

# Append the row using loc
dataFrame.loc[len(dataFrame)] = new_team

print("Updated DataFrame after appending:")
print(dataFrame)

Updated DataFrame after appending:
       Country  Rank  Points
0        India     1     100
1    Australia     2      85
2      England     3      75
3  New Zealand     4      65
4 South Africa     5      50
5   Bangladesh     6      40
6    Sri Lanka     7      30

How It Works

The loc accessor works by:

• len(dataFrame) returns the current number of rows (6 in our case)
• This becomes the index for the new row (index 6)
• The list values are assigned to the corresponding columns in order

Multiple Rows Appending

You can append multiple rows by incrementing the index ?

import pandas as pd

# Original DataFrame
team_data = [['India', 1, 100], ['Australia', 2, 85]]
dataFrame = pd.DataFrame(team_data, columns=['Country', 'Rank', 'Points'])

# Append multiple rows
dataFrame.loc[len(dataFrame)] = ["England", 3, 75]
dataFrame.loc[len(dataFrame)] = ["New Zealand", 4, 65]

print("DataFrame with multiple appended rows:")
print(dataFrame)

DataFrame with multiple appended rows:
       Country  Rank  Points
0        India     1     100
1    Australia     2      85
2      England     3      75
3  New Zealand     4      65

Conclusion

Using DataFrame.loc[len(dataFrame)] = list is an efficient way to append a single row to a DataFrame. The len() function automatically provides the next available index position for the new row.