NumPy Versus APL
summary
Claims that NumPy workflows map directly into APL and shows side by side code for CSV loading (with character conversion hacks), indexing, a simple linear regression, plotting and serialization. APL code is visually shorter for array math; CSV reading, string formatting, plotting styles and Python interop need workarounds; plots look similar but some styling and alpha blending are missing. End result: equivalent numeric outputs and saved files, with rough edges around tooling and I/O.
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chapters (14)
- 0:00NumPy vs APL
- 2:01Speaker background and talk overview
- 3:32Setup and dependencies
- 6:04Mathematical models
- 10:51Moore’s Law calculations
- 13:20Reading CSV data
- 21:14Linear regression
- 24:31Plotting results
- 27:102017 data comparison
- 32:07Exporting results
- 36:11Reviewing saved data
- 38:50Building CSV files
- 43:13Key differences
- 46:52Q&A
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