DataFrames: The AI Engineer's Spreadsheet
Pandas DataFrames are tabular data structures that combine the power of SQL with Python's flexibility. In AI engineering, you'll use DataFrames for data loading, cleaning, feature engineering, and exploratory analysis before feeding data into models.
Core Operations
- df.head() / df.describe() — quick inspection
- df[df['col'] > threshold] — boolean filtering
- df.groupby('col').agg({'val': 'mean'}) — aggregation
- df.fillna(0) / df.dropna() — missing value handling
Missing Data Strategy
Real-world AI data is messy. Missing values can bias models, crash pipelines, or silently degrade performance. Your strategy — drop, fill with mean/median, or impute — depends on the data and the model.
Key Takeaway
Pandas DataFrames are your primary tool for data preparation. Master filtering, grouping, and missing value handling before moving to model training.