How to Create a New Dataframe With Selected Columns of Existing Dataframe in Python
To create a new dataframe with selected columns of an existing dataframe in Python, you can use the = assignment operator in combination with [] square brackets.
The following example shows how to create a new dataframe with selected columns of an existing dataframe in Python.
Using = Operator & [] Bracket
The combination of the =
operator and []
brackets allows you to pick specific columns and create a new DataFrame.
Let’s start with a DataFrame containing various product details and filter specific columns:
# Import pandas library
import pandas as pd
# Create dataframe
old_df = pd.DataFrame({
'Date': ['01-03-2023', '01-03-2023', '01-03-2023', '01-03-2023', '02-03-2023', '02-03-2023'],
'Product_Code': ['A-101', 'A-102', 'A-103', 'B-101', 'B-102', 'B-104'],
'Product_Name': ['Laptop', 'Mobile', 'Printer', 'Keyboard', 'Scanner', 'Mouse'],
'Price': [4500, 550, 250, 50, 350, 50],
'Status': [1, 1, 1, 0, 1, 1]
})
# Create new dataframe with selected columns
new_df = old_df[["Date", "Product_Code", "Price"]]
# Show new dataframe
print(new_df)
Output: 👇️
Date Product_Code Price
0 01-03-2023 A-101 4500
1 01-03-2023 A-102 550
2 01-03-2023 A-103 250
3 01-03-2023 B-101 50
4 02-03-2023 B-102 350
5 02-03-2023 B-104 50
In this code snippet:
- old_df: The original DataFrame with all columns.
- new_df: A new DataFrame that includes only the selected columns “Date”, “Product_Code”, and “Price”.
- Square Brackets ([]): Used to specify the column names you want to extract.
The output shows the new dataframe with only the selected columns.
Conclusion
Selecting specific columns from a DataFrame in Python is straightforward and essential for efficient data manipulation.
- Use the = assignment operator with [] square brackets to extract the columns you need.
- This approach allows you to streamline your dataset for analysis or other tasks.