Handling Missing Data in Pandas

Detecting Nulls

df.isnull().sum() # Count nulls per column

df[df['price'].isnull()] # Show rows with null price

Filling Nulls (COALESCE)

df['price'].fillna(0) # Fill with 0

df['price'].fillna(df['price'].mean()) # Fill with average

Dropping Nulls

df.dropna() # Drop any row with a null

df.dropna(subset=['price']) # Drop only if 'price' is null

*Day 110: Creating and Transforming Columns.*