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Vintage Personality Pet John Henry the Diary Basset Hound Dog Co


Vintage Personality Pet John Henry the Diary Basset Hound Dog Co


Item specifics

Used: An item that has been used previously. See the seller’s listing for full details and ...
Small (6-14 in)
Country/Region of Manufacture:
United States
Character Family:
Personality Pet

Vintage Personality Pet John Henry the Diary Basset Hound Dog Co

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 HUNGARY - PALESTINE TRANSIT VISA 1933, 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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