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Item specifics

Pre-owned: An item that has been used or worn previously. See the seller’s listing for full details ...
Outer Shell Material:
100% Nylon
Sleeve Length:
Long Sleeve
Lining Material:
100% Nylon
Fabric Type:
Blended Fabric
Insulation Material:
Size Type:
Rain Gear
Full Zip
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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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NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

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 College University Pennant Wolverines Used vintage mancave spor, 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
# array([[  0,   1,   2,   3,   4],
#        [-99,   6, -99,   8, -99],
#        [-99,  11, -99,  13, -99]])

# array([ 4,  8, 13])

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