Pandas DataFrame to a List in Python

Pandas dataframes are great for manipulating data. But, at times it might happen that you’d rather have the data as a list (or more precisely, a list of lists). In this tutorial, we’ll look at how to convert a pandas dataframe to a python list.

How to covert a pandas dataframe to a list?

There are multiple ways to get a python list from a pandas dataframe depending upon what sort of list you want to create. To quickly get a list from a dataframe with each item representing a row in the dataframe, you can use the tolist() function like df.values.tolist()

However, there are other ways as well. You can create a list with each item representing a dataframe column. Or, you can create something very specific based on your requirements. Let’s look at some of the different use cases with examples.

Examples

First, let’s create a dataframe of a sample stock portfolio that we’ll be using throughout this tutorial.

import pandas as pd

data = {
    'Name': ['Microsoft Corporation', 'Google, LLC', 'Tesla, Inc.',\
             'Apple Inc.', 'Netflix, Inc.'],
    'Symbol': ['MSFT', 'GOOG', 'TSLA', 'AAPL', 'NFLX'],
    'Industry': ['Tech', 'Tech', 'Automotive', 'Tech', 'Entertainment'],
    'Shares': [100, 50, 150, 200, 80]
}

df = pd.DataFrame(data)
print(df)

Output:

                    Name Symbol       Industry  Shares
0  Microsoft Corporation   MSFT           Tech     100
1            Google, LLC   GOOG           Tech      50
2            Tesla, Inc.   TSLA     Automotive     150
3             Apple Inc.   AAPL           Tech     200
4          Netflix, Inc.   NFLX  Entertainment      80

The following are some of the ways to get a list from a pandas dataframe explained with examples.

1. List with DataFrame rows as items

As mentioned above, you can quickly get a list from a dataframe using the tolist() function.

ls = df.values.tolist()
print(ls)

Output

[['Microsoft Corporation', 'MSFT', 'Tech', 100], ['Google, LLC', 'GOOG', 'Tech', 50], ['Tesla, Inc.', 'TSLA', 'Automotive', 150], ['Apple Inc.', 'AAPL', 'Tech', 200], ['Netflix, Inc.', 'NFLX', 'Entertainment', 80]]

In the above example, df.values returns the numpy representation of the dataframe df which is then converted to a list using the tolist() function. You can see that we get a list of lists with each item in the list representing a row in the dataframe.

2. List with DataFrame columns as items

You can also use tolist() function on individual columns of a dataframe to get a list with column values.

# list with each item representing a column
ls = []
for col in df.columns:
    # convert pandas series to list
    col_ls = df[col].tolist()
    # append column list to ls
    ls.append(col_ls)
# print the created list
print(ls)

Output

[['Microsoft Corporation', 'Google, LLC', 'Tesla, Inc.', 'Apple Inc.', 'Netflix, Inc.'], ['MSFT', 'GOOG', 'TSLA', 'AAPL', 'NFLX'], ['Tech', 'Tech', 'Automotive', 'Tech', 'Entertainment'], [100, 50, 150, 200, 80]]

In the above example, we iterate through each column of the dataframe which is converted to a list and then appended to ls. You can see that here we get a list of lists with each item in the list representing a column in the dataframe.

3. List from a DataFrame by iterating through the rows

You can also create a list by iterating through the rows of the dataframe.

ls = []
# iterate over the rows
for i, row in df.iterrows():
    # create a list representing the dataframe row
    row_ls = [row['Name'], row['Symbol'], row['Industry'], row['Shares']]
    # append row list to ls
    ls.append(row_ls)
    
print(ls)

Output

[['Microsoft Corporation', 'MSFT', 'Tech', 100], ['Google, LLC', 'GOOG', 'Tech', 50], ['Tesla, Inc.', 'TSLA', 'Automotive', 150], ['Apple Inc.', 'AAPL', 'Tech', 200], ['Netflix, Inc.', 'NFLX', 'Entertainment', 80]]

In the above example, we use the pandas dataframe iterrows() function to iterate over the rows of df and create a list with row values which gets appended to ls. You can see that we get a list of lists with each item in the list representing a row in the dataframe like we saw in the example with the tolist() function.

This method also allows you the flexibility to create specific lists based on your requirements. For instance, from the above dataframe if you want to create a list of lists with only the stock symbol and its respective share count you can easily do it by keeping only those fields.

ls = []
# iterate over the rows
for i, row in df.iterrows():
    # create a list representing the dataframe row
    row_ls = [row['Symbol'], row['Shares']]
    # append row list to ls
    ls.append(row_ls)
    
print(ls)

Output

[['MSFT', 100], ['GOOG', 50], ['TSLA', 150], ['AAPL', 200], ['NFLX', 80]]

Here, we get a list of lists with each item having the stock symbol and the respective shares count in the portfolio.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5