Why Data Cleaning Matters
Real-world datasets are messy — missing values, wrong types, duplicates, and outliers are the norm. Data scientists spend up to 80% of their time cleaning data before any analysis begins.
Common Cleaning Operations
- df.isnull().sum() — count missing values per column
- df.fillna(value) — replace missing values
- df.dropna() — remove rows with missing values
- df.drop_duplicates() — remove duplicate rows
- df['col'].astype(float) — convert column types
Key Takeaway
Clean data is the foundation of trustworthy analysis. Always inspect, clean, and validate before drawing conclusions.