Data Cleaning with Pandas

Missing Data Handling

Pandas provides comprehensive missing data handling.

Detection

df.isnull() returns boolean DataFrame. df.isnull().sum() counts missing per column. df.dropna(axis=1) drops columns with any missing.

Imputation

df.fillna(value) fills with scalar. df.fillna(method='ffill') forward fills. df.fillna(df.mean()) fills with column mean.

SimpleImputer from sklearn provides more options: 'mean', 'median', 'most_frequent'.

Analysis

Missing data patterns might reveal data collection issues. Analyze patterns before deciding on handling.

Key Takeaways

1. Pandas provides flexible missing data handling
2. Imputation choices depend on missing mechanism
3. Analyze patterns to inform handling strategy