To convert Pandas DataFrame to Series using squeeze:
df.squeeze()
In this guide, you’ll see 3 scenarios of converting:
- Single DataFrame column into a Series (from a single-column DataFrame)
- Specific DataFrame column into a Series (from a multi-column DataFrame)
- 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 = {"Products": ["Computer", "Printer", "Tablet", "Chair", "Desk"]} df = pd.DataFrame(data) 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):
Products
0 Computer
1 Printer
2 Tablet
3 Chair
4 Desk
<class 'pandas.core.frame.DataFrame'>You can then use df.squeeze() to convert the DataFrame into a Series:
import pandas as pd data = {"Products": ["Computer", "Printer", "Tablet", "Chair", "Desk"]} df = pd.DataFrame(data) my_series = df.squeeze() print(my_series) print(type(my_series))
The resulted Series after the conversion:
0 Computer
1 Printer
2 Tablet
3 Chair
4 Desk
Name: Products, dtype: object
<class 'pandas.core.series.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 = {"Products": ["Computer", "Printer", "Tablet", "Chair", "Desk"], "Brand": ["A", "B", "C", "D", "E"], "Price": [750, 200, 300, 150, 400] } df = pd.DataFrame(data) print(df) print(type(df))
Run the code, and you’ll get a DataFrame with 3 columns:
Products Brand Price
0 Computer A 750
1 Printer B 200
2 Tablet C 300
3 Chair D 150
4 Desk E 400
<class 'pandas.core.frame.DataFrame'>Let’s say that your goal is to convert the “Price” column into a Series. In that case, you’ll need to add the following syntax to the code:
my_series = df["Price"].squeeze()So the complete code to perform the conversion is as follows:
import pandas as pd data = {"Products": ["Computer", "Printer", "Tablet", "Chair", "Desk"], "Brand": ["A", "B", "C", "D", "E"], "Price": [750, 200, 300, 150, 400] } df = pd.DataFrame(data) my_series = df["Price"].squeeze() print(my_series) print(type(my_series))
The “Price” column will now become a Series:
0 750
1 200
2 300
3 150
4 400
Name: Price, dtype: int64
<class 'pandas.core.series.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 the “Chair” product (where the associated index value is 3):
import pandas as pd data = {"Products": ["Computer", "Printer", "Tablet", "Chair", "Desk"], "Brand": ["A", "B", "C", "D", "E"], "Price": [750, 200, 300, 150, 400] } df = pd.DataFrame(data) my_series = df.iloc[3].squeeze() print(my_series) print(type(my_series))
You’ll then get the following Series:
Products Chair
Brand D
Price 150
Name: 3, dtype: object
<class 'pandas.core.series.Series'>And if you’d like reset the index (to contain only integers), you may use this syntax:
import pandas as pd data = {"Products": ["Computer", "Printer", "Tablet", "Chair", "Desk"], "Brand": ["A", "B", "C", "D", "E"], "Price": [750, 200, 300, 150, 400] } df = pd.DataFrame(data) 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:
0 Chair
1 D
2 150
Name: 3, dtype: object
<class 'pandas.core.series.Series'>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.