Nick ODell nickodell

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Installing Python 3.6.8 on Raspbian

As of March 2018, Raspbian does not yet include the latest Python release, Python 3.6.8 This means we will have to build it ourselves, and here is how to do it.

Install the required build-tools (some might already be installed on your system).

Here's a guide about when and how to use prepared statements.

Why bother with prepared statements?

The first reason to use a prepared statement is to protect against SQL injections.

You can do this by removing all special characters, but there are times when that's not practical. (Example: you want to allow users who have apostrophes in their names to register for your website.) You can do it by escaping all of the strings that you pass into the SQL query, but it's easy to forget to sanitize a single field.

So how do you protect against SQL injection? Here is the simple way to never fall prey to SQL injection: never put a user-provided string into an SQL query, except as a parameter to a prepared statement.

	FROM archlinux:latest
	RUN pacman -Syu --noconfirm git base-devel
	RUN useradd nodell \
	&& mkdir /builddir \
	&& chown nodell:nodell /builddir
	USER nodell
	WORKDIR /builddir
	RUN git clone https://aur.archlinux.org/python-scipy-mkl-tbb.git
	USER root
	RUN pacman -Su --noconfirm cython gcc-fortran meson-python pybind11 python-build python-installer python-pythran python-pytest python-hypothesis

	# 1.14.1 was benchmarked outside of SciPy's build directory
	# It was done with the following command
	for i in $(seq 1 7); do python3 bug-22655b.py; done
	# This runs the benchmark fewer times than normal, but this is fine because 1.14.1 is very clearly faster.

	// ==UserScript==
	// @name Remove Search Pulsing
	// @version 1
	// @grant none
	// @match https://stackoverflow.com/*
	// ==/UserScript==


	(new MutationObserver(check)).observe(document, {childList: true, subtree: true});

	import numpy as np
	import cv2
	import scipy.ndimage
	import matplotlib.pyplot as plt

	img = cv2.imread('HjJto.jpg')
	img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

	blue = np.array((81, 93, 147)).reshape(1, 1, 3)
	rmse = np.sqrt(((img_rgb - blue)**2).mean(axis=2))

	user www-data;
	worker_processes auto;
	pid /run/nginx/nginx.pid; # only line changed from default
	include /etc/nginx/modules-enabled/*.conf;

	events {
	worker_connections 768;
	# multi_accept on;
	}

	concatenated = data[['day', 'month', 'year', 'time']].astype(str).agg(' '.join, axis=1)
	data['date'] = pd.to_datetime(concatenated)