How to Convert Pandas DataFrame to a Series

You can convert Pandas DataFrame to Series using squeeze:

df.squeeze()

In this guide, you’ll see 3 scenarios of converting:

1. Single DataFrame column into a Series (from a single-column DataFrame)
2. Specific DataFrame column into a Series (from a multi-column DataFrame)
3. Single row in the DataFrame into a Series

(1) Convert a Single DataFrame Column into a Series

To start with a simple example, let’s create a DataFrame with a single column:

import pandas as pd

data = {'First_Name': ['Jeff','Tina','Ben','Maria','Rob']}
df = pd.DataFrame(data, columns = ['First_Name'])

print(df)
print (type(df))

Run the code in Python, and you’ll get the following DataFrame (note that print (type(df)) was added at the bottom of the code to demonstrate that we got a DataFrame):

You can then use df.squeeze() to convert the DataFrame into Series:

import pandas as pd

data = {'First_Name': ['Jeff','Tina','Ben','Maria','Rob']}
df = pd.DataFrame(data, columns = ['First_Name'])

my_series = df.squeeze()

print(my_series)
print (type(my_series))

The DataFrame will now get converted into a Series:

(2) Convert a Specific DataFrame Column into a Series

What if you have a DataFrame with multiple columns, and you’d like to convert a specific column into a Series?

For example, suppose that you have the following multi-column DataFrame:

import pandas as pd

data = {'First_Name': ['Jeff','Tina','Ben','Maria','Rob'],
        'Last_Name':['Miller','Smith','Lee','Green','Carter'],
        'Age':[33,42,29,28,57]
        }
df = pd.DataFrame(data, columns = ['First_Name','Last_Name','Age'])

print(df)
print (type(df))

Run the code, and you’ll get a DataFrame with 3 columns:

Let’s say that your goal is to convert the ‘Last_Name‘ column into a Series. In that case, you’ll need to add the following syntax to the code:

my_series = df['Last_Name'].squeeze()

So the complete code to perform the conversion is as follows:

import pandas as pd

data = {'First_Name': ['Jeff','Tina','Ben','Maria','Rob'],
        'Last_Name':['Miller','Smith','Lee','Green','Carter'],
        'Age':[33,42,29,28,57]
        }
df = pd.DataFrame(data, columns = ['First_Name','Last_Name','Age'])

my_series = df['Last_Name'].squeeze()

print(my_series)
print (type(my_series))

The ‘Last_Name’ column will now become a Series:

(3) Convert a Single Row in the DataFrame into a Series

In the final scenario, you’ll see how to convert a single row in the DataFrame into a Series.

For instance, you can use the syntax below to convert the row that represents ‘Maria Green’ (where the associated index value is 3):

import pandas as pd

data = {'First_Name': ['Jeff','Tina','Ben','Maria','Rob'],
        'Last_Name':['Miller','Smith','Lee','Green','Carter'],
        'Age':[33,42,29,28,57]
        }
df = pd.DataFrame(data, columns = ['First_Name','Last_Name','Age'])

my_series = df.iloc[3].squeeze()

print(my_series)
print (type(my_series))

You’ll get the following Series:

And if you’d like reset the index (to contain only integers), you may use this syntax:

import pandas as pd

data = {'First_Name': ['Jeff','Tina','Ben','Maria','Rob'],
        'Last_Name':['Miller','Smith','Lee','Green','Carter'],
        'Age':[33,42,29,28,57]
        }
df = pd.DataFrame(data, columns = ['First_Name','Last_Name','Age'])

my_series = df.iloc[3].reset_index(drop=True).squeeze()

print(my_series)
print (type(my_series))

Here is the Series with the new index that contains only integers:

Additional Resources

You may want to check the following guide to learn how to convert Pandas Series into a DataFrame.

The Pandas Documentation also contains additional information about squeeze.