DataFrame Basics

Add Derived Column

Compute a new column elementwise by looping over two parallel value lists and appending each product. With pandas, a single vectorized expression df['price'] * df['qty'] produces all products at once, and df.assign attaches the result as a new column without mutating the original DataFrame.

By hand

With pandas

df.assign(total=df['price'] * df['qty']) multiplies the two Series elementwise (index-aligned) and returns a new DataFrame with the total column appended. The snapshot shows the input columns, then the new column as a Series.

naive.py 
price = [10, 25, 5, 20]
qty = [3, 2, 8, 1]
total = []
for i in range(len(price)):
    total.append(price[i] * qty[i])
print('RESULT:', total)
library.py 
import pandas as pd
from dalib.display import set_display
set_display()

price = [10, 25, 5, 20]
qty = [3, 2, 8, 1]
df = pd.DataFrame({'price': price, 'qty': qty})
df2 = df.assign(total=df['price'] * df['qty'])
col = df2['total']
print('price:', df['price'].tolist())
print('qty:', df['qty'].tolist())
print('index:', col.index.tolist())
print('values:', col.tolist())
print('dtype:', col.dtype)
print('RESULT:', col.tolist())

price: [10, 25, 5, 20]
qty: [3, 2, 8, 1]
index: [0, 1, 2, 3]
values: [30, 50, 40, 20]
dtype: int64
RESULT: [30, 50, 40, 20]

Implementation notes

  • df.assign returns a new DataFrame with the added column; the original df is unchanged. This makes it safe to chain: df.assign(...).assign(...).
  • df['price'] * df['qty'] is vectorized column arithmetic — the same broadcast-and-apply pattern as NumPy elementwise multiplication (see elementwise-product in python-numpy ch03), applied to Series aligned by index label.
  • To add the column in-place instead, use df['total'] = df['price'] * df['qty']. Prefer assign in pipelines; prefer direct assignment for one-off mutations.
  • Cross-reference: add-derived-field (python-data-basics) for the pure-Python dict version; elementwise-product (python-numpy ch03) for the NumPy array version.