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$87

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Item specifics

Condition:
New
Brand:
TURBOSMART
MPN:
TURBOSMART-TS-0203-1041-2
Manufacturer Part Number:
TURBOSMART-TS-0203-1041-2
Return Postage Charges:
Buyer will pay for any return postage
Item Picture:
NOT the Actual Item (For Reference Only)
UPC:
Does not apply

HQ Turbosmart BOV Kompact V2 Dual Port FOR VAG 2.0T fits Audi A4

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 2" INCH SPRING CLAMPS - 10PC CLAMP SET - 3/4" INCH OPEN JAW WIDT, 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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