How to Convert Pandas DataFrame to List?

Name

Rajiv Chandra

Updated on 8/15/2023

As a data scientist, you may come across situations where you need to convert a Pandas DataFrame to a list. Fortunately, this is quite easy to accomplish using the tolist() function. In this article, we'll explain how to convert a DataFrame to a list using this method. We'll also cover how to convert a column to a list, how to create a nested list, and provide some quick examples.

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Converting a DataFrame to a List

The tolist() function is a built-in method in Pandas that converts a DataFrame to a list. You simply call this function on your DataFrame, and it will return a list.

import pandas as pd
 
# create a sample DataFrame
df = pd.DataFrame({'numbers': [1, 2, 3], 'letters': ['a', 'b', 'c']})
 
# convert DataFrame to list
list_from_df = df.values.tolist()
print(list_from_df)

This code will output:

[[1, 'a'], [2, 'b'], [3, 'c']]

As you can see, the tolist() function returns a list of lists, where each inner list corresponds to a row in the DataFrame. The values are ordered by column, and each value is of the type that Pandas infers from the values in the column.

Converting a Column to a List

There are also situations where you'll just want to convert a specific column from a DataFrame to a list. Here's how to do that:

import pandas as pd
 
# create a sample DataFrame
df = pd.DataFrame({'numbers': [1, 2, 3], 'letters': ['a', 'b', 'c']})
 
# convert 'numbers' column to list
list_from_column = df['numbers'].tolist()
print(list_from_column)

This code will output:

[1, 2, 3]

As you can see, the tolist() function is used on a specific column, and it returns a list with just the values from that column.

Creating a Nested List

In some situations, you may need to create a nested list from a DataFrame. For example, you may want a list of tuples for each row instead of a list of lists. Here's how to do that:

import pandas as pd
 
# create a sample DataFrame
df = pd.DataFrame({'numbers': [1, 2, 3], 'letters': ['a', 'b', 'c']})
 
# create a nested list
nested_list = [tuple(x) for x in df.to_records(index=False)]
print(nested_list)

This code will output:

[(1, 'a'), (2, 'b'), (3, 'c')]

As you can see, we used a list comprehension to create a list of tuples from the DataFrame. This can be useful if you need to pass your data to a function that expects a certain data structure.

Quick Examples

Here are a few quick examples of converting a DataFrame to a list:

import pandas as pd
 
# create a sample DataFrame
df = pd.DataFrame({'numbers': [1, 2, 3], 'letters': ['a', 'b', 'c']})
 
# convert DataFrame to list (method 1)
list_from_df1 = df.values.tolist()
 
# convert DataFrame to list (method 2)
list_from_df2 = [list(x) for x in df.values]
 
# convert 'numbers' column to list
list_from_column = df['numbers'].tolist()
 
# create a nested list
nested_list = [tuple(x) for x in df.to_records(index=False)]

In the first example, we used the tolist() function to convert the DataFrame to a list of lists.

In the second example, we used a list comprehension to achieve the same result.

In the third example, we used the tolist() function on a specific column to convert it to a list.

And in the fourth example, we used a list comprehension to create a nested list of tuples.

Conclusion

Converting a Pandas DataFrame to a list is a common task in data science. Thankfully, it's also quite simple using the tolist() function. Whether you need to convert the entire DataFrame to a list, a specific column to a list, or create a nested list, this function has got you covered. With the examples provided in this article, you'll be able to quickly and easily accomplish this task in your own projects.