The parts of SciPy worth knowing, and how to navigate a library that is really twelve libraries.
Before this guide: NumPy, Deep Dive.
The notebooks
None of these notebooks is written yet. This is the plan, in reading order.
| 1 | What SciPy IsA toolbox rather than a topic, and how to navigate it | not yet written |
| 2 | InterpolationFilling gaps, and the honesty of the method you picked | not yet written |
| 3 | Optimizationminimize, and reading the result object it hands back | not yet written |
| 4 | Curve Fittingcurve_fit, and the covariance you should be reporting | not yet written |
| 5 | Root Findingbrentq, bracketing, and why it failed to converge | not yet written |
| 6 | Numerical Integrationquad, and recognizing when the answer is nonsense | not yet written |
| 7 | DecompositionsLinear algebra past what NumPy gives you | not yet written |
| 8 | Sparse MatricesWhen your matrix is mostly zeros, and the memory you save | not yet written |
| 9 | Signal ProcessingFiltering and smoothing without inventing a trend | not yet written |
| 10 | StatisticsDistributions, tests, and what scipy.stats will and will not do | not yet written |
| 11 | SpatialDistances, KD-trees, and nearest neighbors | not yet written |
| 12 | Images as Arraysndimage, and an image as data you can index | not yet written |