[FIXED] Pandas missing add row method (Python)

Issue

Is it just me, or is Pandas missing a dedicated method for adding a row? Especially now that append() has been depreciated.

The only ways I know of is to create a second df (even if it's just one row) and concat the two, or use the loc[-1] method which doesn't allow you to easily pick which columns to add data to. Am I missing something?

I created my own method which can add a full row or only specified columns. I'm just surprised that they have an insert method for columns but not rows.

Solution

You are right.

Since Append method is deprecated you should concat to add row.

But you have the possibility to design a custom adding function as follows. I think this is a wise thing to do :

a simple method :

import pandas as pd

def add_row(df, row_data):
    new_row = pd.DataFrame(row_data, columns=df.columns)
    return pd.concat([df, new_row], ignore_index=True)

# Example usage
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Data for the new row
new_row_data = {'A': [7], 'B': [8]}

# Add the new row to the DataFrame
df = add_row(df, new_row_data)

   A  B
0  1  4
1  2  5
2  3  6
3  7  8

Implement a row-checking process :

We could want to check for instance if :

• row is a list or a dict
• check row columns content versus original dataframe columns if it's a dict
• row length if it's a list

Concatenation is processed if checking test is ok otherwise the function returns a message error.

import pandas as pd

def add_row(df, row_data):
    """Custom method for adding a row to a dataframe"""
    if isinstance(row_data, dict):
        # Check if row data is a dictionary
        if set(row_data.keys()) != set(df.columns):
            raise ValueError("Row data doesn't match DataFrame columns")
        new_row = pd.DataFrame(row_data, columns=df.columns)
    elif isinstance(row_data, list):
        # Check if row data is a list
        if len(row_data) != len(df.columns):
            raise ValueError("Row data doesn't match DataFrame columns")
        new_row = pd.DataFrame([row_data], columns=df.columns)
    else:
        raise ValueError("Row data format not supported")
    
    return pd.concat([df, new_row], ignore_index=True)

# Example usage
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Data for the new row
new_row_data = {'A': [7], 'B': [8]}

# Add the new row to the DataFrame
df = add_row(df, new_row_data)

   A  B
0  1  4
1  2  5
2  3  6
3  7  8

Answered By - Laurent B.