5 Best Ways to Append DataFrame Rows to a List in Python

by

💡 Problem Formulation: Many data manipulation tasks in Python involve handling data stored in a DataFrame using libraries like pandas. Sometimes, it’s necessary to extract a row of data from a DataFrame and append it to a list for further processing or analysis. For instance, you might wish to collect specific rows based on a condition to create a new list of records. Let’s explore several effective methods for appending DataFrame rows to lists in Python.

Method 1: Using to_list() with iloc[]

This method involves selecting a row from the DataFrame with the iloc[] method and then converting it to a list using to_list(). It’s a simple and direct approach to extract a DataFrame row by its index position and transform it to a list format.

Here’s an example:

import pandas as pd

# Creating a simple DataFrame
df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': ['a', 'b', 'c']
})

# Selecting the second row and appending it to a list
row_list = df.iloc[1].to_list()
print(row_list)

Output:

[2, 'b']

This code snippet creates a pandas DataFrame with two columns and then selects the second row (index 1) converting it to a list. The list row_list contains the data from the second row of the DataFrame.

Method 2: Using values Attribute with List Slicing

Another approach is to access the underlying numpy array of the DataFrame with the values attribute and then use standard list slicing to get the desired row, which is already in the list format.

Here’s an example:

import pandas as pd

# Creating the DataFrame
df = pd.DataFrame({
    'col1': [10, 20, 30],
    'col2': ['x', 'y', 'z']
})

# Appending the first row to a list
row_list = df.values[0].tolist()
print(row_list)

Output:

[10, 'x']

The code defines a DataFrame and uses df.values followed by list slicing [0] to select the first row. It then converts the row to a list with tolist() and prints the output.

Method 3: Using apply() Method

The apply() method in pandas can be utilized to apply a function along an axis of the DataFrame. In this case, one can extract a particular row and immediately apply the list function to convert it into a list.

Here’s an example:

import pandas as pd

# Defining the DataFrame
df = pd.DataFrame({
    'col1': [100, 200, 300],
    'col2': ['alpha', 'beta', 'gamma']
})

# Appending the third row to a list
row_list = df.apply(lambda row: row.tolist(), axis=1)[2]
print(row_list)

Output:

[300, 'gamma']

This code creates a DataFrame and uses apply() with a lambda function that converts each row into a list. The specific row is then indexed to retrieve the third row as a list.

Method 4: Using List Comprehension with iterrows()

Using the iterrows() function is another way to iterate over DataFrame rows, where each row is represented as a (index, series) pair. With list comprehension, you can specifically target and append any row you want into a list.

Here’s an example:

import pandas as pd

# Setting up the DataFrame
df = pd.DataFrame({
    'col1': [11, 22, 33],
    'col2': ['one', 'two', 'three']
})

# Using  list comprehension  to append the third row to a list
row_list = [row.tolist() for index, row in df.iterrows() if index == 2]
print(row_list)

Output:

[[33, 'three']]

This snippet employs list comprehension and the iterrows() method to iterate over the DataFrame rows. The condition within the comprehension selects the third row and appends it as a list to row_list.

Bonus One-Liner Method 5: Using at[] with List Comprehension

For the quickest one-liner, you can combine the at[] accessor with list comprehension. This method is concise and can be used to extract a specific element from each column in a specific row to form a list.

Here’s an example:

import pandas as pd

# Creating the DataFrame
df = pd.DataFrame({
    'col1': [111, 222, 333],
    'col2': ['red', 'green', 'blue']
})

# One-liner to append the first row to a list
row_list = [df.at[0, col] for col in df.columns]
print(row_list)

Output:

[111, 'red']

The code uses a list comprehension that iterates through the DataFrame’s columns, using the at[] accessor to fetch the first row’s elements to compile the list row_list.

Summary/Discussion

  • Method 1: Using to_list() with iloc[]. Strengths: Straightforward and easy to understand. Weaknesses: Requires explicit indexing, which might not be dynamic.
  • Method 2: Using values Attribute with List Slicing. Strengths: Utilizes the inherent numpy array for potentially faster access. Weaknesses: Loses the pandas context and column names.
  • Method 3: Using apply() Method. Strengths: Flexible and can be used for complex row operations. Weaknesses: May be slower due to row-wise operation.
  • Method 4: Using List Comprehension with iterrows(). Strengths: Offers fine control and readability. Weaknesses: Can be less efficient for large DataFrames as iterrows() is not the fastest iteration method.
  • Bonus One-Liner Method 5: Using at[] with List Comprehension. Strengths: Very concise code for a specific row. Weaknesses: This approach can be less readable for those unfamiliar with list comprehensions and loses the ability to dynamically handle multiple rows.