Christian Stade-Schuldt Tafkas
| # Copyright 2019 Google LLC. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # Author: Anton Mikhailov | |
| turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913 |
| { | |
| "emojis": [ | |
| {"emoji": "👩👩👧👧", "name": "family: woman, woman, girl, girl", "shortname": ":woman_woman_girl_girl:", "unicode": "1F469 200D 1F469 200D 1F467 200D 1F467", "html": "👩‍👩‍👧‍👧", "category": "People & Body (family)", "order": ""}, | |
| {"emoji": "👩👩👧👦", "name": "family: woman, woman, girl, boy", "shortname": ":woman_woman_girl_boy:", "unicode": "1F469 200D 1F469 200D 1F467 200D 1F466", "html": "👩‍👩‍👧‍👦", "category": "People & Body (family)", "order": ""}, | |
| {"emoji": "👩👩👦👦", "name": "family: woman, woman, boy, boy", "shortname": ":woman_woman_boy_boy:", "unicode": "1F469 200D 1F469 200D 1F466 200D 1F466", "html": "👩‍👩‍👦‍👦", "category": "People & Body (family)", "order": ""}, | |
| {"emoji": "👨👩👧👧", "name": "family: man, woman, girl, girl", "shortname": ":man_woman_girl_girl:", "unicode": "1F468 200D 1F469 200D 1F467 200D 1F467", "html": "👨‍👩&z |
-
pg_dump is a nifty utility designed to output a series of SQL statements that describes the schema and data of your database. You can control what goes into your backup by using additional flags.
Backup:pg_dump -h localhost -p 5432 -U postgres -d mydb > backup.sqlRestore:
psql -h localhost -p 5432 -U postgres -d mydb < backup.sql-h is for host.
-p is for port.
-U is for username.
-d is for database.
| #!/usr/bin/python3 | |
| # | |
| # Simple Bloom filter implementation in Python 3 | |
| # Copyright 2017 Hector Martin "marcan" <[email protected]> | |
| # Licensed under the terms of the MIT license | |
| # | |
| # Written to be used with the Have I been pwned? password list: | |
| # https://haveibeenpwned.com/passwords | |
| # | |
| # Download the pre-computed filter here (968MB, k=11, false positive p=0.0005): |
by Bjørn Friese
Beautiful is better than ugly. Explicit is better than implicit.
I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.
| #!/usr/bin/env bash | |
| #Code adapted from https://gist.github.com/yangj1e/3641843c758201ebbc6c (Modified to Python3.5) | |
| cd ~ | |
| #wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda2-2.4.0-Linux-x86_64.sh | |
| wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda3-2.4.1-Linux-x86_64.sh | |
| bash Anaconda3-2.4.1-Linux-x86_64.sh -b | |
| echo 'PATH="/home/ubuntu/anaconda3/bin:$PATH"' >> .bashrc | |
| . .bashrc |
The state of Iowa has released an 800MB+ dataset of more than 3 million rows showing weekly liquor sales, broken down by liquor category, vendor, and product name, e.g. STRAIGHT BOURBON WHISKIES, Jim Beam Brands, Maker's Mark
This dataset contains the spirits purchase information of Iowa Class “E” liquor licensees by product and date of purchase from January 1, 2014 to current. The dataset can be used to analyze total spirits sales in Iowa of individual products at the store level.
You can view the dataset via Socrata
| var o = {hello:'world', greetings: ['one', 'two', 'three']}; | |
| undefined | |
| // regular, difficult to read for large objects | |
| JSON.stringify(o); | |
| "{"hello":"world","greetings":["one","two","three"]}" | |
| // presto! | |
| JSON.stringify(o, '', ' '); | |
| "{ |