When working with Pandas, you may need to get a list of values in a column.
You can get a list from pandas dataframe columns using the df[“Column name“].tolist()
statement.
Basic Example
values_list = df[“Column Name“].tolist()
values_list
This tutorial teaches you how to convert pandas dataframe columns to a list.
Table of Contents
Sample Dataframe
Create a sample dataframe with Four columns.
Column names: First Name, Last Name, Country, Country Code
In the sample dataframe, the First Name column contains only distinct values and the Last Name, Country, and Country Code have duplicate values.
Code
import pandas as pd
# List of Tuples
users = [ ('Shivam', 'Pandey', 'India', 1),
('Kumar', 'Ram' , 'US', 2 ),
('Felix','John' , 'Germany', 3 ),
('Michael','John' , 'India', 1 ),
]
#Create a DataFrame object
df = pd.DataFrame( users,
columns = ['First Name' , 'Last Name', 'Country', 'Country Code']
)
df
Dataframe Will Look Like
First Name | Last Name | Country | Country Code | |
---|---|---|---|---|
0 | Shivam | Pandey | India | 1 |
1 | Kumar | Ram | US | 2 |
2 | Felix | John | Germany | 3 |
3 | Michael | John | India | 4 |
Now let us see the different methods to get values as a list.
Using Series toList()
In this section, you’ll learn how to use the toList() method available in the Pandas Series.
- Get the pandas series of a specific column using
df[column_name]
- Invoke the
tolist()
method to convert the series to a Python list.
Code
The following code demonstrates how to get the Country column as a list.
Countries = df["Country"].tolist()
Countries
Output
You’ll get the country column values as a list(Including duplicate values).
['India', 'US', 'Germany', 'India']
Using Numpy toList()
In this section, you’ll learn how to use the tolist() method available in the NumPy array.
- Convert the pandas dataframe column to a NumPy array using the values attribute
- Use the
tolist()
method to convert the array to a list
Use this method when you already have the pandas dataframe column values as a NumPy array.
Code
Countries = df["Country"].values.tolist()
Countries
Output
All the values in the Country column are displayed, including the duplicate values.
['India', 'US', 'Germany', 'India']
Using Python List()
In this section, you’ll learn how to use the Python list() function to get a list of values from a column in the pandas dataframe.
- Pass the Pandas series values using the
df[Column name]
to thelist()
function - It’ll return the Python list object with the list of values.
Code
Countries = list(df["Country"])
Countries
Output
The output consists of all the values, including the duplicate values.
['India', 'US', 'Germany', 'India']
Using to_numpy()
In this section, you’ll learn how to get a list of values from a Pandas Dataframe column using the to_numpy() method.
to_numpy()
method returns the array, not a list.- Use the
list()
function to convert the array to a list.
Code
Countries = df["Country"].to_numpy()
list(Countries)
Output
The values are converted to a list, including duplicate values.
['India', 'US', 'Germany', 'India']
Get Unique values as List Using Pandas Series.Unique()
In this section, you’ll learn how to get Unique values as a list from pandas Dataframe columns.
- Use the unique() method to get the unique values from the Dataframe column. It’ll return a NumPy array with unique values.
- Pass this array to the
list()
function to get the list of unique values.
Code
unique_array = df["Country"].unique()
list(unique_array)
Output
['India', 'US', 'Germany']
These are the different methods to convert pandas dataframe columns to a list.