How to add one row in existing Pandas DataFrame?

Efficiently Adding a Single Row to a Pandas DataFrame

When working with data in Python, it's common to need to add new information to an existing dataset. The Pandas library provides several methods to insert a single row into a DataFrame. In this guide, we'll explore two efficient approaches: using the loc[] indexer and the concat() function.

Method 1: Using loc[] to Add a Row

The loc[] indexer allows you to add a new row by specifying its index and values. This method is straightforward and modifies the DataFrame in place, making it memory-efficient.

import pandas as pd

# Existing DataFrame
data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
df = pd.DataFrame(data)

# Add a new row using loc[]
df.loc[len(df)] = ["Charlie", 35]
print(df)

Output:

      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   35

In this example, we use len(df) to determine the next available index and assign the new row's values accordingly.

Method 2: Using concat() to Add a Row

The concat() function is versatile and can be used to append a new row. This method is particularly useful when dealing with multiple rows or when working with external data sources.

import pandas as pd

# Existing DataFrame
data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
df = pd.DataFrame(data)

# New row as a DataFrame
new_row = pd.DataFrame({"Name": ["Eve"], "Age": [28]})

# Concatenate the new row to the existing DataFrame
df = pd.concat([df, new_row], ignore_index=True)
print(df)

Output:

      Name  Age
0    Alice   25
1      Bob   30
2      Eve   28

Here, we create a new DataFrame for the row to be added and concatenate it with the original DataFrame. The ignore_index=True parameter ensures that the index is reset.

Additional Examples

Let's explore a few more scenarios where adding a row might be necessary:

• Adding a Row with Default Values: Useful when you need to add a placeholder row with default values for further updates or processing.

df.loc[len(df)] = ["Unknown", 0]

• Adding Multiple Rows: When you have multiple rows to add, it's more efficient to create a new DataFrame and concatenate it with the existing one.

new_rows = pd.DataFrame({"Name": ["Charlie", "Diana"], "Age": [35, 28]})
df = pd.concat([df, new_rows], ignore_index=True)

• Adding a Row with a Dictionary: If you have a dictionary of values, you can convert it to a DataFrame and append it.

new_row = {"Name": "Eve", "Age": 28}
df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)

Conclusion

Adding a single row to a Pandas DataFrame can be accomplished efficiently using either the loc[] indexer or the concat() function. The choice of method depends on your specific use case and the structure of your data. By understanding these techniques, you can effectively manage and manipulate your datasets in Pandas.