Appending a List as a Row to a Pandas DataFrame

Introduction

Appending a list to a DataFrame is a common operation in data manipulation. However, it can be tricky when you want to add a new row with a specific index. This guide will help you understand how to do this efficiently using Pandas.

• Note: append method (deprecated as of v1. 4.0) has been completely removed as of pandas 2.0

Prerequisites

Before we start, make sure you have the following:

• Python 3.6 or later installed
• Pandas library installed
• Basic understanding of Python and Pandas

Step 1: Importing the Pandas Library

First, we need to import the Pandas library. If you haven’t installed it yet, you can do so using pip:

pip install pandas

Then, import the library in your Python script:

import pandas as pd

Step 2: Creating a DataFrame

Let’s create a simple DataFrame for demonstration:

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

Step 3: Appending a List as a a New Row

To append a list as a new row, we use the loc[] function. Here’s how:

list = [10, 11, 12]
df.loc[len(df)] = list

The new dataframe would look like this:

    A   B   C
0   1   4   7
1   2   5   8
2   3   6   9
3  12  13  14

Appending a List with a Specific Index

If you want to append a list at a specific index, you would use the iloc[] method. Here is how it works:

import pandas as pd
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})
  

list2 = [13,14,15]
df.iloc[0] = list2

print(df)

The output would look like this:

    A   B   C
0  13  14  15
1   2   5   8
2   3   6   9

Note: It is used for location-based indexing so it works for only the existing index and replaces the row element.

Conclusion

Appending a list as a new row in a DataFrame with a specific index is a common operation in data manipulation. This guide has shown you how to do it efficiently using Pandas. However, appending rows to a df is time-consuming and should be avoided. Sometimes it is necessary but a good practice is to build your table outside of pandas then create a df to process the data which is much faster than adding rows.

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