Programming Tutorials

  You are watching your ETL pipeline logs. The memory usage climbs steadily: 8GB, 12GB, 16GB. Then, the inevitable crash:   MemoryError   or the Linux OOM killer sigkills your process. If you are processing datasets exceeding 10 million rows with Pandas, this scenario is a daily reality. While Pandas is the industry standard for exploration, its architectural design struggles with scale. It requires datasets to fit entirely in RAM, often needing 5x to 10x the dataset size in available memory to perform complex operations. This article provides a direct, technical migration path from Pandas to Polars. We will solve the  MemoryError  not by buying more RAM, but by leveraging lazy evaluation, streaming execution, and Apache Arrow memory layouts. The Root Cause: Why Pandas Explodes Memory To fix the crash, you must understand why Pandas manages memory inefficiently compared to modern alternatives. 1. Eager Execution Pandas is eager. When you execute a command like...