In this tutorial, you’ll see how to convert Pandas Series to a DataFrame. You’ll also observe how to convert multiple Series into a DataFrame.
To begin, here is the syntax that you may use to convert your Series to a DataFrame:
df = my_series.to_frame()
Alternatively, you can use this approach to convert your Series:
df = pd.DataFrame(my_series)
In the next section, you’ll see how to apply the above syntax using a simple example.
Steps to Convert Pandas Series to DataFrame
Step 1: Create a Series
To start with a simple example, let’s create Pandas Series from a List of 5 items:
import pandas as pd item = ['Computer', 'Printer', 'Tablet', 'Desk', 'Chair'] my_series = pd.Series(item) print(my_series) print(type(my_series))
Run the code in Python, and you’ll get the following Series:
0 Computer
1 Printer
2 Tablet
3 Desk
4 Chair
dtype: object
<class 'pandas.core.series.Series'>
Note that the syntax of print(type(my_series)) was added at the bottom of the code in order to demonstrate that we created a Series (as highlighted in yellow above).
Step 2: Convert the Pandas Series to a DataFrame
Next, convert the Series to a DataFrame by adding df = my_series.to_frame() to the code:
import pandas as pd item = ['Computer', 'Printer', 'Tablet', 'Desk', 'Chair'] my_series = pd.Series(item) df = my_series.to_frame() print(df) print(type(df))
Run the code, and you’ll now get a DataFrame:
0
0 Computer
1 Printer
2 Tablet
3 Desk
4 Chair
<class 'pandas.core.frame.DataFrame'>
In the above case, the column name is ‘0.’ Alternatively, you may rename the column by adding df = df.rename(columns = {0:’item’}) to the code:
import pandas as pd
item = ['Computer', 'Printer', 'Tablet', 'Desk', 'Chair']
my_series = pd.Series(item)
df = my_series.to_frame()
df = df.rename(columns = {0:'item'})
print(df)
print(type(df))
You’ll now see the new column name at the top:
item
0 Computer
1 Printer
2 Tablet
3 Desk
4 Chair
<class 'pandas.core.frame.DataFrame'>
Convert Multiple Series to Pandas DataFrame
Now you’ll observe how to convert multiple Series (for the following data) into a DataFrame.
| item | brand | price |
| Computer | A | 700 |
| Printer | B | 150 |
| Tablet | C | 300 |
| Desk | D | 450 |
| Chair | E | 200 |
Let’s now create the 3 Series based on the above data:
import pandas as pd item = ['Computer', 'Printer', 'Tablet', 'Desk', 'Chair'] series_item = pd.Series(item) print (series_item) brand = ['A', 'B', 'C', 'D', 'E'] series_brand = pd.Series(brand) print(series_brand) price = [700, 150, 300, 450, 200] series_price = pd.Series(price) print(series_price)
Run the code, and you’ll get the following 3 Series:
0 Computer
1 Printer
2 Tablet
3 Desk
4 Chair
dtype: object
0 A
1 B
2 C
3 D
4 E
dtype: object
0 700
1 150
2 300
3 450
4 200
dtype: int64
In order to convert the 3 Series into a DataFrame, you’ll need to:
- Convert the 3 Series into 3 DataFrames
- Concatenate the 3 DataFrames into a single DataFrame
Here is the complete code:
import pandas as pd
item = ['Computer', 'Printer', 'Tablet', 'Desk', 'Chair']
series_item = pd.Series(item)
brand = ['A', 'B', 'C', 'D', 'E']
series_brand = pd.Series(brand)
price = [700, 150, 300, 450, 200]
series_price = pd.Series(price)
df_item= pd.DataFrame(series_item)
df_item = df_item.rename(columns = {0:'item'})
df_brand = pd.DataFrame(series_brand)
df_brand = df_brand.rename(columns = {0:'brand'})
df_price = pd.DataFrame(series_price)
df_price = df_price.rename(columns = {0:'price'})
df_all = pd.concat([df_item, df_brand, df_price ], axis=1)
print(df_all)
print(type(df_all))
Once you run the code, you’ll get this single DataFrame:
item brand price
0 Computer A 700
1 Printer B 150
2 Tablet C 300
3 Desk D 450
4 Chair E 200
<class 'pandas.core.frame.DataFrame'>
Additional Resources
You may visit the Pandas Documentation to learn more about to_frame().
You may also check the following guides for the steps to: