You can convert Pandas DataFrame to a 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, columns = ['Products'])
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, columns = ['Products'])
my_series = df.squeeze()
print(my_series)
print (type(my_series))
The DataFrame will now get converted into a Series:
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, columns = ['Products', 'Brand', 'Price'])
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, columns = ['Products', 'Brand', 'Price'])
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’ (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, columns = ['Products', 'Brand', 'Price'])
my_series = df.iloc[3].squeeze()
print(my_series)
print (type(my_series))
You’ll 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, columns = ['Products', 'Brand', 'Price'])
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.