How to add one row in an existing Pandas DataFrame?

While working with data using Pandas in Python, adding a new row to an existing DataFrame is a common task that can be performed using various methods. Pandas is a popular data manipulation library that provides multiple functionalities for data analysis. In this article, we will discuss how to add one row in an existing Pandas DataFrame using different methods.

Sample DataFrame

Before we add a new row to the Pandas DataFrame, let's first create a sample DataFrame that we will use throughout the article. We will create a DataFrame with three columns: "Name", "Gender", and "Age".

import pandas as pd

data = {
    "Name": ["Jane", "Martin", "Baskin"],
    "Gender": ["Female", "Male", "Male"],
    "Age": [20, 34, 32]
}

df = pd.DataFrame(data)
print(df)

     Name  Gender  Age
0    Jane  Female   20
1  Martin    Male   34
2  Baskin    Male   32

Method 1: Using concat() Method (Recommended)

The concat() method is the recommended approach for adding rows to a DataFrame. It concatenates a new DataFrame with the existing one ?

import pandas as pd

data = {
    "Name": ["Jane", "Martin", "Baskin"],
    "Gender": ["Female", "Male", "Male"],
    "Age": [20, 34, 32]
}

df = pd.DataFrame(data)

# Create a new row as a DataFrame
new_row = pd.DataFrame({"Name": ["Alicia"], "Gender": ["Female"], "Age": [30]})
df = pd.concat([df, new_row], ignore_index=True)
print(df)

     Name  Gender  Age
0    Jane  Female   20
1  Martin    Male   34
2  Baskin    Male   32
3  Alicia  Female   30

Method 2: Using loc[] Method

The loc[] method allows us to add a new row at a specific location in the DataFrame. We use len(df) to add the row at the end ?

import pandas as pd

data = {
    "Name": ["Jane", "Martin", "Baskin"],
    "Gender": ["Female", "Male", "Male"],
    "Age": [20, 34, 32]
}

df = pd.DataFrame(data)

# Add a new row using loc[]
df.loc[len(df)] = ["Alice", "Female", 27]
print(df)

     Name  Gender  Age
0    Jane  Female   20
1  Martin    Male   34
2  Baskin    Male   32
3   Alice  Female   27

Method 3: Using append() Method (Deprecated)

Note: The append() method is deprecated as of Pandas 1.4.0. Use concat() instead. Here's how it was used ?

import pandas as pd

data = {
    "Name": ["Jane", "Martin", "Baskin"],
    "Gender": ["Female", "Male", "Male"],
    "Age": [20, 34, 32]
}

df = pd.DataFrame(data)

# Deprecated method - use concat() instead
new_row = {"Name": "Alicia", "Gender": "Female", "Age": 30}
df = df.append(new_row, ignore_index=True)
print(df)

Adding Multiple Rows

To add multiple rows simultaneously, create a DataFrame with multiple rows and use concat() ?

import pandas as pd

data = {
    "Name": ["Jane", "Martin", "Baskin"],
    "Gender": ["Female", "Male", "Male"],
    "Age": [20, 34, 32]
}

df = pd.DataFrame(data)

# Create multiple new rows
new_rows = pd.DataFrame({
    'Name': ['Alaska', 'David'],
    'Gender': ['Female', 'Male'],
    'Age': [28, 33]
})

df = pd.concat([df, new_rows], ignore_index=True)
print(df)

     Name  Gender  Age
0    Jane  Female   20
1  Martin    Male   34
2  Baskin    Male   32
3  Alaska  Female   28
4   David    Male   33

Comparison

Conclusion

Use concat() for adding single or multiple rows to a DataFrame as it's the current recommended approach. The loc[] method is efficient for adding single rows when you know the exact position.