Efficiently Adding Rows to Pandas DataFrames

Pandas DataFrames are a cornerstone of data manipulation in Python. Adding rows efficiently is a common task, and this article details the best practices for appending a single row to your DataFrame.

Table of Contents

• Using .loc for Efficient Row Addition
• Appending with Dictionaries for Readability
• Why You Should Avoid the append() Method

Using .loc for Efficient Row Addition

The .loc accessor provides the most efficient and direct way to add a row. It’s particularly advantageous when working with larger DataFrames, minimizing performance overhead. You specify the index for the new row and provide the data as a list or NumPy array.

import pandas as pd
import numpy as np

# Sample DataFrame
data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
df = pd.DataFrame(data)

# New row data as a list
new_row_list = [4, 7]

# Add the new row using .loc
df.loc[len(df)] = new_row_list

print("Using list:n", df)


#New row data as a numpy array
new_row_array = np.array([5,8])

#Add the new row using .loc with numpy array
df.loc[len(df)] = new_row_array

print("nUsing NumPy array:n",df)

This approach directly modifies the DataFrame’s underlying structure, making it faster than alternatives. The output shows the new rows appended.

Appending with Dictionaries for Readability

When dealing with many columns, using a dictionary to represent the new row enhances code readability. The dictionary keys correspond to column names, and values are the row’s data. pd.concat efficiently combines the existing DataFrame with the new row.

import pandas as pd

# Sample DataFrame
data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
df = pd.DataFrame(data)

# New row data as a dictionary
new_row_dict = {'col1': 5, 'col2': 8}

# Add the new row using pd.concat
df = pd.concat([df, pd.DataFrame([new_row_dict])], ignore_index=True)

print(df)

ignore_index=True ensures proper index handling, preventing duplicate indices.

Why You Should Avoid the append() Method

The append() method is deprecated in modern Pandas versions. It’s less efficient and can lead to unexpected behavior. The .loc and dictionary-based methods are superior in terms of performance and maintainability. Always prefer the more efficient and supported approaches described above.