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LEE Filter - 186 Cosmetic Silver Rose - 20 inch by 24 inch gel s

$3

LEE Filter - 186 Cosmetic Silver Rose - 20 inch by 24 inch gel s

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Item specifics

Condition:
New: A brand-new, unused, unopened, undamaged item in its original packaging (where packaging is ...
Model:
186 Cosmetic Silver Rose
Type:
Continuous Lighting
Color:
Silver
MPN:
Does Not Apply
Brand:
LEE Filters
UPC:
Does not apply




LEE Filter - 186 Cosmetic Silver Rose - 20 inch by 24 inch gel s

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 [Qty.4] 120-1R-C/L #120 Standard Roller Chain Connecting Link [P, 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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