Apply and Values

Apply a Function to a Column

Transform each value in a column by applying a formula element-by-element — here, rounding each price to the nearest ten. With pandas, Series.apply calls the function once per element and collects the results.

By hand

With pandas

df['price'].apply(lambda x: round(x / 10) * 10) returns a new Series with the transformed values. The index and dtype are preserved.

naive.py 
prices = [14, 27, 33, 56]
result = []
for p in prices:
    scaled = round(p / 10) * 10
    result.append(scaled)
print('RESULT:', result)
library.py 
import pandas as pd
from dalib.display import set_display
set_display()

prices = [14, 27, 33, 56]
df = pd.DataFrame({'price': prices})
result = df['price'].apply(lambda x: round(x / 10) * 10)
print('index:', result.index.tolist())
print('values:', result.tolist())
print('dtype:', result.dtype)
print('RESULT:', result.tolist())

index: [0, 1, 2, 3]
values: [10, 30, 30, 60]
dtype: int64
RESULT: [10, 30, 30, 60]

Implementation notes

  • Series.apply is an elementwise API — it calls the Python function once per element, not as a vectorized kernel. For simple arithmetic, prefer a vectorized expression: df['price'] * 2 is faster than df['price'].apply(lambda x: x * 2) because it avoids per-element Python calls.
  • Use .apply when the transformation cannot be expressed as built-in vectorized operations — e.g. multi-step logic or calls to external functions.
  • Cross-reference: map-transform (python-data-basics) for the pure-Python version; map-values (this chapter) for dict-based remapping via Series.map.