How do I add a new column to an existing Pandas DataFrame? The new column should have the same number of rows as existing columns.
We can do this using the
DataFrame.assign method. When called on a given DataFrame, this method creates a copy of that DataFrame with one or more additional columns added.
The new columns can contain manually specified values, or values can be computed for each row based on other columns. Look at the following example code:
import pandas # Create a dataframe with products and their cost prices products = pandas.DataFrame([["apple", 2.0], ["orange", 3.0], ["pear", 4.0]], columns=["product", "cost_price"]) print(products) # Add a stock count column with manual data products = products.assign(stock_count=[50, 40, 30]) print(products) # Add a sale price column that calculates a 50% markup products = products.assign(sale_price=lambda row: row.cost_price * 1.5) print(products)
This code will print the
products DataFrame with two, three and then four columns. Note the use of a lambda expression in the creation of the
sale_price column – the values for this column are created by running the function on each row.