Applying Functions: `apply`, `map`, and Vectorized Operations

Applying Functions in Pandas

Pandas allows you to apply custom Python logic across rows or columns.

Using map for element‑wise transformation

df['temp_f'] = df['temp_c'].map(lambda c: c * 9/5 + 32)

Using apply on a Series

df['name'] = df['name'].apply(str.title)

Using apply on a DataFrame (row‑wise)

def compute_score(row):
    return row['units'] * row['price'] * 0.9   # 10% discount

df['score'] = df.apply(compute_score, axis=1)

Vectorized operations (preferred when possible)

df['revenue'] = df['units'] * df['price']

Using transform for broadcasting results back to original shape

# Z‑score per group
zscore = df.groupby('store_id')['revenue'].transform(lambda x: (x - x.mean()) / x.std())
df['revenue_z'] = zscore