How to Convert Pandas DataFrame into a List

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Python’s Pandas library is a popular tool for data manipulation and analysis. It provides two key data structures – Series and DataFrame, which make data manipulation in Python a seamless task. Sometimes, it might be useful to convert these data structures into other data types, such as lists, to use with other Python libraries or functions that might not accept Pandas objects.

This article will delve into the various methods of converting a Pandas DataFrame into a list, along with some use cases and nuances of each method.

Creating a Pandas DataFrame

Let’s start by creating a simple DataFrame:

# import pandas
import pandas as pd

# create a simple dataset of people
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
        'Country': ['USA', 'Canada', 'Germany', 'Australia'],
        'Age': [24, 36, 29, 50]}

df = pd.DataFrame(data)

# print the dataframe

Converting DataFrame into a List

Pandas provides various ways to convert a DataFrame into a list, each serving a different purpose.

Method 1: Using tolist()

The simplest way to convert a DataFrame into a list is to use the tolist() function. This function returns the DataFrame values as a Python list:

list_data = df.values.tolist()

Here, df.values returns a Numpy array and then tolist() converts that array to a list.

However, it’s important to note that this will return a list of lists where each list corresponds to a row in the DataFrame.

Method 2: Using to_dict() and list()

Another way to convert a DataFrame into a list is to first convert the DataFrame into a dictionary, and then convert that dictionary into a list:

dict_data = df.to_dict('records')
list_data = list(dict_data)

In this case, the output will be a list of dictionaries, where each dictionary corresponds to a row in the DataFrame, with column names as keys and row values as values.

Method 3: Converting a Single Column to a List

Often, you might want to convert only one column from the DataFrame into a list. This can be done by using the tolist() function on the column:

age_list = df['Age'].tolist()

This will return a list containing the values of the ‘Age’ column.

Method 4: Converting DataFrame into a Flattened List

If you want a single flat list containing all values in the DataFrame, you can use the ravel() function from Numpy, followed by tolist():

flat_list = df.values.ravel().tolist()

This will return a single list containing all the values in the DataFrame, read from left-to-right, and top-to-bottom.


In this article, we explored multiple ways to convert a Pandas DataFrame into a list. These methods provide a lot of flexibility, allowing you to transform the DataFrame into various types of list structures as needed.

The method to use depends largely on the structure of the list you need. If you want a list of lists, use tolist(). If you want a list of dictionaries, use to_dict() followed by list(). If you want a list from a single column, use tolist() on the specific column. And if you want a flattened list of all values, use ravel() followed by tolist().

Understanding these different methods of converting a DataFrame into a list will allow you to work more flexibly with your data and leverage the vast array of tools and functions available in Python that operate on lists.

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