Data Handling and Visualization › Summary
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This chapter covered essential data handling and visualization skills for quantitative finance.
Data loading and management: Pandas reads many tabular formats and organizes records returned by source-specific connectors. A DatetimeIndex enables time-based slicing, resampling, and rolling calculations.
Time series operations: Resampling converts between frequencies, rolling windows compute statistics, and explicit missing-value policies with validation reduce avoidable errors.
Data manipulation: Convert prices to returns, then filter, merge, and summarize data for analysis.
Performance optimization: Start with clear pandas and NumPy code, profile the target workload, and test Numba on supported numeric kernels when Python-level loops dominate. Specify whether compilation and warm-up are included; use repeated controlled measurements appropriate to cold-start or steady-state workloads. Speedups are not guaranteed.
Visualization: Charts reveal patterns invisible in summary statistics. Use price charts, distributions, correlations, and rolling metrics to explore data.
You will repeatedly use these data handling patterns when calculating risk, building factor models, or training machine learning models.