A short "what's new" talk at PyData NYC about recent changes in NumPy and SciPy

1. NumPy & SciPy
What’s now?
Ralf Gommers
PyData NYC 2019

2. NumPy 1.17 - the most significant release in years
◎New random number generators
(a complete overhaul of numpy.random)
◎New Fourier transform implementations
(a complete overhaul of numpy.fft)
◎The __array_function__ protocol became
enabled by default.
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3. Vastly improved random number generators
Multiple new generators, faster & higher
statistical quality. Plus:
• Parallel/distributed capabilities (3 methods)
• Extensible infrastructure - plug in your own
bit-generator
• Can be used from Numba, and (in 1.18)
from Cython or C
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4. Vastly improved random number generators
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5. Vastly improved random number generators
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6. The NumPy array protocols - goals
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Separate NumPy API from NumPy “execution engine”
Allow other libraries (Dask, CuPy, PyTorch, …) to reuse
the NumPy API
Bigger picture: avoid or reduce ecosystem
fragmentation (we don’t want to see a reimplementation of SciPy for
PyTorch, SciPy for Tensorflow, etc.)

7. The NumPy array protocols - goals
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8. The NumPy array protocols - goals
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9. Array protocols — using the NumPy API with GPUs
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10. Array protocols — using the NumPy API with distributed
arrays via Dask
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11. Fourier transforms - faster & more accurate
All FFT functions in NumPy and SciPy reimplemented.
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12. scipy.fft — first new SciPy submodule in a decade
• API changes for consistency with NumPy
• Based on PyPocketfft (modern C++, close
to performance of FFTW)
• Bluestein algorithm: accurate & fast also
for prime-length arrays
• Context managers for backend selection
and multi-threading support
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13. scipy.fft — first new SciPy submodule in a decade
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14. More robust new global optimizers
optimize.shgo, optimize.dual_annealing
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15. Thanks!
Any questions?
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