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AtariMax cartridge Omaha Mall loaded Free Shipping Cheap Bargain Gift with games. Atari machines 800 48K

AtariMax cartridge loaded with games. (48K machines) Atari 800

$25

AtariMax cartridge loaded with games. (48K machines) Atari 800

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Item specifics

Seller Notes:
“tested and working with Atari 800, 800XL, 65XE, 130XE, XEGS”
Model:
Atari 800XL
Featured Refinements:
Atari XE
Vintage:
Yes
Brand:
Atari












AtariMax cartridge loaded with games. (48K machines) Atari 800

NumPy
The fundamental package for scientific computing with Python
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Powerful N-dimensional arrays
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

Numerical computing tools
NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
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Interoperable
NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

Performant
The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

Easy to use
NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

Open source
Distributed under a liberal 1970's / 1980's HEY! THUMBS UP! VINTAGE NOVELTY PATCH (FUN) (bl, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.
Try NumPy

Use the interactive shell to try NumPy in the browser

"""
To try the examples in the browser:
1. Type code in the input cell and press
   Shift + Enter to execute
2. Or copy paste the code, and click on
   the "Run" button in the toolbar
"""

# The standard way to import NumPy:
import numpy as np

# Create a 2-D array, set every second element in
# some rows and find max per row:

x = np.arange(15, dtype=np.int64).reshape(3, 5)
x[1:, ::2] = -99
x
# array([[  0,   1,   2,   3,   4],
#        [-99,   6, -99,   8, -99],
#        [-99,  11, -99,  13, -99]])

x.max(axis=1)
# array([ 4,  8, 13])

# Generate normally distributed random numbers:
rng = np.random.default_rng()
samples = rng.normal(size=2500)
samples
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