2008 Canada Some reservation 1 2 oz Gold Maple SKU BU Leaf #30756 - $127 2008 Canada 1/2 oz Gold Maple Leaf BU - SKU #30756 Coins & Paper Money Bullion Gold $127 2008 Canada 1/2 oz Gold Maple Leaf BU - SKU #30756 Coins & Paper Money Bullion Gold www.gumvalencia.org,Gold,Canada,Coins & Paper Money , Bullion , Gold,-,SKU,/limuloid465345.html,BU,oz,Maple,2008,#30756,1/2,$127,Leaf 2008 Canada Some reservation 1 2 oz Gold Maple SKU BU Leaf #30756 - www.gumvalencia.org,Gold,Canada,Coins & Paper Money , Bullion , Gold,-,SKU,/limuloid465345.html,BU,oz,Maple,2008,#30756,1/2,$127,Leaf

2008 Canada Some reservation 1 2 oz Gold Maple SKU BU Leaf #30756 favorite -

2008 Canada 1/2 oz Gold Maple Leaf BU - SKU #30756

$127

2008 Canada 1/2 oz Gold Maple Leaf BU - SKU #30756

|||

Item specifics

Brand:
Canada - Royal Canadian Mint
MPN:
30756
Year:
2008
Fineness:
0.9999
Denomination:
$20
Country/Region of Manufacture:
Canada
Circulated/Uncirculated:
Brilliant Uncirculated
Certification:
Uncertified


2008 Canada 1/2 oz Gold Maple Leaf BU - SKU #30756

NumPy
The fundamental package for scientific computing with Python
NumFOCUS DEI research study - call for participation

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 3D Yellow Bow Dog NAO4178 Bed Pillowcases Quilt Duvet Cover Set, 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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