Adding Rows to Pandas DataFrame: A Step-by-Step Guide

Name

Rajiv Chandra

Published on 5/10/2023

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In data analysis and machine learning projects, you often need to add new observations to a Pandas DataFrame. Whether you are dealing with survey data, sensor readings, or user behavior logs, you must be able to append new rows to an existing DataFrame efficiently. Pandas, a popular data manipulation library in Python, offers several methods to add rows to a DataFrame. In this tutorial, we will explore three of them: .loc, .append, and .concat. Let's dive in.

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Adding a Row using .loc Method

The .loc method in Pandas allows you to select a subset of a DataFrame based on its labels. You can also use it to add a new row to the DataFrame. Here's how:

import pandas as pd
 
# creating a sample dataframe
df = pd.DataFrame({
   'Name': ['John', 'Sara', 'Tim'],
   'Age': [25, 34, 41],
   'City': ['New York', 'London', 'Paris']
})
 
# adding a new row using .loc
df.loc[3] = ['Anna', 29, 'Berlin']
 
print(df)

In the code above, we first create a DataFrame df with three rows and three columns. Then, we use the .loc method and specify a row index of 3. Finally, we provide a list of values for the new row.

Output:

   Name  Age       City
0  John   25   New York
1  Sara   34     London
2   Tim   41      Paris
3  Anna   29     Berlin

Note that the row index of the new row is 3, which means it will be added after the last row of the DataFrame. You can also use .loc to insert a row at a specific location by specifying the row label. For example, to insert a new row after the second row of the DataFrame, you can do:

# inserting a new row after the second row
df.loc[2.5] = ['Peter', 37, 'Los Angeles']
 
print(df)

Output:

    Name   Age         City
0   John    25     New York
1   Sara    34       London
2    Tim    41        Paris
2.5 Peter   37  Los Angeles
3   Anna    29       Berlin

Here, we insert the new row after the label 2 by using a label of 2.5. Pandas automatically reorders the row labels to keep them in ascending order.

Adding a Row using .append Method

The .append method in Pandas allows you to combine two DataFrames vertically. You can use it to add a new row to a DataFrame by passing a DataFrame with a single row to the .append method. Here's an example:

# creating a new row as a dataframe
new_row = pd.DataFrame({
   'Name': ['Julia'],
   'Age': [31],
   'City': ['Tokyo']
})
 
# using .append to add the new row
df = df.append(new_row, ignore_index=True)
 
print(df)

Output:

    Name   Age        City
0   John    25    New York
1   Sara    34      London
2    Tim    41       Paris
3   Anna    29      Berlin
4  Julia    31       Tokyo

In the code above, we first create a new DataFrame new_row with a single row of data. Then, we use the .append method and pass new_row as the argument. The ignore_index=True parameter tells Pandas to reindex the DataFrame from 0 to 4.

Adding Multiple Rows using .concat Method

The .concat method in Pandas allows you to concatenate two DataFrames along a specified axis. You can use it to add multiple rows to a DataFrame by passing a list of DataFrames to the .concat method. Here's how you can add two new rows to the DataFrame df using .concat.

# creating two new rows as dataframes
new_rows = [pd.DataFrame({
   'Name': ['Maria', 'Adam'],
   'Age': [27, 45],
   'City': ['Sydney', 'Toronto']}), 
]
 
# using .concat to add the new rows
df = pd.concat([df] + new_rows, ignore_index=True)
 
print(df)

Output:

    Name   Age        City
0   John    25    New York
1   Sara    34      London
2    Tim    41       Paris
3   Anna    29      Berlin
4  Maria    27      Sydney
5   Adam    45     Toronto

In the code above, we first create two new DataFrames new_rows. Then, we pass them as a list to the .concat method along with df. The ignore_index=True parameter reorders the index from 0 to 5.

Conclusion

In this tutorial, we explored three different methods to add new rows to a Pandas DataFrame in Python: .loc, .append, and .concat. Each method has its advantages and disadvantages, depending on the situation. The .loc method is useful when you want to add a single row at a specific location. The .append method is a simple way to add a single row to the end of a DataFrame. The .concat method is ideal when you want to add multiple rows at once. Knowing these methods will help you manipulate DataFrames efficiently and effectively in your data analysis and machine learning projects. Happy coding!