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numpy/numpy is an open source project licensed under BSD 3clause "New" or "Revised" License which is an OSI approved license.
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Posts
 How to replace an integer with a letter in a dictionary

Question regarding python imports
What is going on is numpy has chosen to import the linalg subpackage and expose it through the numpy package. This is entirely at the discretion of the package, and doesn't need to be done. The logic exposing the linalg package by way of numpy can be seen here. The numpy package maintainer may consider the linalg subpackage as semantically "part" of numpy  though you would have to go ask them for yourself.

Write Better Python Functions (Using Type Dispatch)
Numpy stands for Numerical Python. Numpy is the core library for scientific computing in Python. It provides a highperformance object, and tools for working with these arrays.

Using PyTorch and NumPy? You're making a mistake
I suppose...
1) This is an issue from 2018 (https://github.com/pytorch/pytorch/issues/5059), which links to the closed numpy issue (https://github.com/numpy/numpy/issues/9248) which just says: seed your random numbers folk.
2) The documentation in pytorch covers this (https://pytorch.org/docs/stable/data.html#randomnessinmult...), but it's not really highlighted specifically in, eg. tutorials.
3) To quote the author:
> I downloaded and analysed over a hundred thousand repositories from GitHub that import PyTorch. I kept projects that use NumPy’s random number generator with multiprocess data loading. Out of these, over 95% of the repositories are plagued by this problem.
^ No actual stats, just some vague hand waving; this just seems like nonsense.
So, I suppose... there's some truth to it being a documentation issue, but I guess the title + (13) kind of say to me: OP thought they discovered something significant... turns out, they didn't.
Oh well, spin it into some page views.

5 Python Libraries You Need to Know
NumPy is the fundamental package for scientific computing in Python and provides a solid foundation on top of which to build your numerical algorithms. Pandas provides powerful data structures and tools to make working with large datasets easier. It can load data in many formats including csv, json, html and others.

How I Calculated the 1,000,000th Fibonacci Number with Python
I don't think any of those are correct. Looking at the source code of linalg: https://github.com/numpy/numpy/blob/main/numpy/linalg/linalg.py#L660

How to fix random number duplicate
# Built with python 3, dependencies installed with pip # library to generate images  Pillow # https://pillow.readthedocs.io/en/stable/installation.html from PIL import Image # library to work with arrays # https://numpy.org/ import numpy as np # library to convert rgb to hsl import colorsys # library to interact with the operating system import os # gets path to be used in image creation mechanism, using os dirname = os.path.dirname(__file__) # sets final image dimensions as 480x480 pixels # the original 24x24 pixel image will be expanded to these dimensions dimensions = 480, 480 # tells how many times to iterate through the following mechanism # which equals the number of box for x in range(0, 10): # skin color  randomly generate each number in an RGB color import random # base hsv color B2 = np.concatenate( [np.random.rand(2), np.random.uniform(0.3, 0.7, size=1)]) B2 = (B2[0], B2[1], B2[2]) # darken color by 10% v_darker = max(min(B2[2]  0.2, 1.0), 0.0) # lighten color by 10% v_lighter = max(min(B2[2] + 0.2, 1.0), 0.0) # forming light color rgb tuple r_lighter, g_lighter, b_lighter = colorsys.hsv_to_rgb( B2[0], B2[1], v_lighter) values = [r_lighter, g_lighter, b_lighter] B1 = tuple([int(value*255.0) for value in values]) # forming dark color rgb tuple r_darker, g_darker, b_darker = colorsys.hsv_to_rgb( B2[0], B2[1], v_darker) values = [r_darker, g_darker, b_darker] B3 = tuple([int(value*255.0) for value in values]) # formating originl B2 color to rgb tuple r, g, b = colorsys.hsv_to_rgb(B2[0], B2[1], B2[2]) values = [r, g, b] B2 = tuple([int(value*255.0) for value in values]) # box colour box = [ [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], ]
# Built with python 3, dependencies installed with pip # library to generate images  Pillow # https://pillow.readthedocs.io/en/stable/installation.html from PIL import Image # library to work with arrays # https://numpy.org/ import numpy as np # library to convert rgb to hsl import colorsys # library to interact with the operating system import os # gets path to be used in image creation mechanism, using os dirname = os.path.dirname(__file__) # sets final image dimensions as 480x480 pixels # the original 24x24 pixel image will be expanded to these dimensions dimensions = 480, 480 # tells how many times to iterate through the following mechanism # which equals the number of box for x in range(0, 10): # skin color  randomly generate each number in an RGB color import random # base hsv color B2 = np.concatenate( [np.random.rand(2), np.random.uniform(0.3, 0.7, size=1)]) B2 = (B2[0], B2[1], B2[2]) # darken color by 10% v_darker = max(min(B2[2]  0.2, 1.0), 0.0) # lighten color by 10% v_lighter = max(min(B2[2] + 0.2, 1.0), 0.0) # forming light color rgb tuple r_lighter, g_lighter, b_lighter = colorsys.hsv_to_rgb( B2[0], B2[1], v_lighter) values = [r_lighter, g_lighter, b_lighter] B1 = tuple([int(value*255.0) for value in values]) # forming dark color rgb tuple r_darker, g_darker, b_darker = colorsys.hsv_to_rgb( B2[0], B2[1], v_darker) values = [r_darker, g_darker, b_darker] B3 = tuple([int(value*255.0) for value in values]) # formating originl B2 color to rgb tuple r, g, b = colorsys.hsv_to_rgb(B2[0], B2[1], B2[2]) values = [r, g, b] B2 = tuple([int(value*255.0) for value in values]) # box colour box = [ [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], [B1, B1, B2, B2, B3, B3], ] box_2 = [ [B2, B2, B1, B1, B3, B3], [B2, B2, B1, B1, B3, B3], [B2, B2, B1, B1, B3, B3], [B2, B2, B1, B1, B3, B3], [B2, B2, B1, B1, B3, B3], [B2, B2, B1, B1, B3, B3], ] # choose which box image to use import random My_list = [*range(0, 21)] random.shuffle(My_list) f = My_list.pop() if f > 10: # if between 2010 pixels = box p = "box" else: # 100 pixels = box_2 p = "box2" # convert the pixels into an array using numpy array = np.array(pixels, dtype=np.uint8) # use PIL to create an image from the new array of pixels new_image = Image.fromarray(array) new_image = new_image.resize(dimensions, resample=0) imgname = dirname + '\\restest' + '.png' new_image.save(imgname)
 How do I quickly find the value of a point on a graph?

[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit?
Napoleon is a Sphinx extension that enables Sphinx to parse both NumPy and Google style docstrings  the style recommended by Khan Academy.

Top 10 Python Libraries
Download NumPy or visit its GitHub repository to know more.
NumPy is an opensource numerical Python library used for scientific computing and performing basic and advanced array operations. It supports multidimensional arrays and matrices along with a collection of highlevel mathematical functions. It manipulates this data using complex mathematical operations like Fourier transformation, linear algebra, random number, etc. You can also use NumPy as an efficient multidimensional container to treat generic data.

It's not much but I'm proud of myself!
Looked into the source and the magic is basically sorting and very clever index magic. A total Complexity of roughly n + n*log(n). Sounds fine to me. Keep in mind while in theory you might have O(n) with sets, in practice you have hash collisions and random memory access. Contiguous memory acces is usually faster. I have not tested it but I bet the C implementation of quicksort in numpy is faster than using python sets.

"The concept that Python is slow is myth only perpetuated by newbies who lack the skill to utilize it to its full potential."
Well NumPy does generate Python C API code from FORTRAN 77/90 code.

How is numpy implemented in C/C++ ?
Take a look on how it is done? https://github.com/numpy/numpy