5 Ways to Create List from DataFrame Row – Be on the Right Side of Change

💡 Problem Formulation: A common task is extracting a list from a row in a DataFrame. Given a DataFrame, how do we extract a row as a list? Assume you have a DataFrame df and you want to retrieve the nth row as a list, preserving the order of the columns.

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Method 1: Using iloc and values attributes

The simplest method is to use the iloc indexer to select the row, and then the values attribute to get a representation of that row as a NumPy array. You can then convert this array to a list.

A minimal example:

import pandas as pd

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

# Convert the first row to a list
row_as_list = df.iloc[0].values.tolist()

print(row_as_list)  # Output: [1, 3, 5]

Here, df.iloc[0] selects the first row of the DataFrame, values converts it into a NumPy array, and finally, tolist() is called on the array to get a list.

👉 Python Create List From DataFrame Column in Pandas

Method 2: Using a List Comprehension

Another approach is to employ a list comprehension that iterates over the columns and extracts the value at a particular row index.

Code Example:

# Continuation of Method 1's sample DataFrame
# Convert the first row to a list using list comprehension
row_as_list = [df[col].iloc[0] for col in df.columns]

print(row_as_list)  
# Output: [1, 3, 5]

Here, we loop through each column in df.columns and select the first row value using df[col].iloc[0]. This creates a list with the elements of the first row.

Method 3: Using the to_numpy() method

If you prefer to avoid using the values attribute, you can use the to_numpy() method, which also converts the selected row to a NumPy array that can then be converted to a list as demonstrated previously.

Here’s an example:

# Continuation of Method 1's sample DataFrame
# Convert the first row to a list using to_numpy and tolist methods
row_as_list = df.iloc[0].to_numpy().tolist()

print(row_as_list)  # Output: [1, 3, 5]

The to_numpy() method directly converts the DataFrame row to a NumPy array. The tolist() method is then called to convert that array into a list.

Method 4: Using the at[] accessor

For very large DataFrames, you might want efficiency; you can use the at[] accessor with a list comprehension. This is similar to method 2 but uses at[] for better performance during access of a single element.

Here’s an example:

# Continuation of Method 1's sample DataFrame
# Convert the first row to a list using at accessor and list comprehension
row_as_list = [df.at[0, col] for col in df.columns]

print(row_as_list)  # Output: [1, 3, 5]

The at[] accessor efficiently retrieves a single value from a specified row/column pair. We iterate over the columns and use df.at[0, col] to construct the list with the first row elements.