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

PythonServer Side ProgrammingProgramming

The Dataframe.loc is used to access a group of rows and columns by label or a boolean array. We will append a list to a DataFrame using loc. Let us first create a DataFrame. The data is in the form of lists of team rankings for our example −

# data in the form of list of team rankings
Team = [['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, columns=['Country', 'Rank', 'Points'])

Following is the row to be appended −

myList = ["Sri Lanka", 7, 30]

Append the above row in the form of list using loc() −

dataFrame.loc[len(dataFrame)] = myList

Example

Following is the code −

import pandas as pd

# data in the form of list of team rankings
Team = [['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, columns=['Country', 'Rank', 'Points'])

print"DataFrame...\n",dataFrame

# row to be appended
myList = ["Sri Lanka", 7, 30]

# append the above row in the form of list using loc()
dataFrame.loc[len(dataFrame)] = myList

# display the update dataframe
print"\nUpdated DataFrame after appending a row using loc...\n",dataFrame

Output

This will produce the following output

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

Updated DataFrame after appending a row using loc...
        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
raja
Published on 21-Sep-2021 06:58:04
Advertisements