## Stop Crashing Jupyter Notebooks
When loading CSV files via `pd.read_csv()`, Pandas is wildly inefficient. It automatically defaults all decimals to 64-bit floats (`float64`) and assigns all strings to memory-heavy Python object pointers (`object`).
By writing a simple typemapping script before loading your dataframe, you can usually compress dataset sizes by over 60%, allowing you to fit what previously took a 64GB cloud instance onto a standard 16GB developer laptop.
### FAQ
**Q: What is a Categorical type in Pandas?**
A: If a column contains 5 million rows but only lists 50 US States, Pandas normally creates 5 million separate string objects in RAM. The `category` type creates a tiny dictionary mapping (e.g., 1 = 'Texas') and replaces the massive column with 8-bit integers, completely eliminating the string bloat.