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EBS Germany 1923 - Numeral Dedication Rosette Miche in III Wholesale Rentenmark

EBS Germany 1923 - Numeral in Rosette (III) - Rentenmark - Miche


EBS Germany 1923 - Numeral in Rosette (III) - Rentenmark - Miche


Item specifics

Seller Notes:
“Please note: the image is provided for reference purposes. The item supplied will be of equal quality but may differ in minor respects from the image shown.Bitte beachten Sie: Das Bild dient nur zu Referenzzwecken. Der gelieferte Artikel ist von gleicher Qualität, kann sich jedoch in geringfügiger Hinsicht von der Abbildung unterscheiden.”
Inflation, Korbdeckel, Inflazeit, Numeral, Infla
Year of Issue:
Mint Never Hinged/MNH

EBS Germany 1923 - Numeral in Rosette (III) - Rentenmark - Miche

The fundamental package for scientific computing with Python
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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 Vtg Lisa Frank Polar Bear Golden Retriever Dog Puppy Kitten Cat, 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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