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# didn't seem to work, but saving here in case it's useful to others

import requests
from base64 import b64encode

workspace_id = 67337
project_id = 190841872
headers = {
    "content-type": "application/json",
    "Authorization": "Basic %s"

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import pandas as pd


# maintain the original data format by reading everything as strings
original = pd.read_csv("original.csv", dtype="object")

# set the random_state so it's reproducible. alternatively, can pass a `frac` to use a percentage.
sampled = original.sample(n=5000, random_state=1).sort_index()

# exclude the index so the columns match the orignal

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# 1. `gem install icalendar`
# 2. Get the "Secret adddress in iCal format" from Google Calendar: https://support.google.com/calendar/answer/37083#link
# 3. Open that URL in a browser to download the .ics file
# 3. Modify the constants below
# 4. Run `ruby rehearsals.rb`

require "icalendar"
require "csv"

START_FROM = DateTime.new(2023, 4, 25)

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// ensures that there's a clean slate for each test, and that no real HTTP requests are made

const nock = require("nock");

beforeAll(() => {
  // ensures no requests hit live APIs
  nock.disableNetConnect();
});

beforeEach(() => {

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Google Sheets has a QUERY function that can be used for writing SQL queries. It uses the Google Visualization API Query Language, which is unfortunately a pretty limited SQL dialect. A few options for writing more complex SQL queries:

• Export to CSV and load into a database somewhere
  • Pros: Use whatever database you want
  • Cons: Have to re-export every time the data is updated
• Load into BigTable
  • Pros: Automatically updates
  • Cons: Requires some Google Cloud setup
• Load into Google Colaboratory ("Colab")
• Pros: Very flexible, as you can use SQL, Pandas, or any other Python code/packages

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#!/bin/bash

set -eo pipefail

aws route53 list-hosted-zones --query 'HostedZones[].Id' | \
  jq -r '.[]' | sed 's/\/hostedzone\///' \
| \
xargs -I zone_id \
  aws route53 list-resource-record-sets \
    --hosted-zone-id zone_id \

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To use, put the script in a file on the machine you want to run SAS, then make it executable.

chmod +x run_sas.sh

After that, you can use it to run SAS scripts:

./run_sas.sh myscript.sas

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<?php

use Google\Cloud\ErrorReporting\Bootstrap;
use Google\Cloud\Logging\LoggingClient;
use Google\Cloud\Core\Report\SimpleMetadataProvider;

// application/core/MY_Exceptions.php
class MY_Exceptions extends CI_Exceptions {

    public function __construct()

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Uses jq.

cat schema.json | \
  jq -r '[paths | join(".")] | .[]' | \
  grep 'properties\.\w\+$' | \
  sed 's/properties\.//g'

Example with this file: