Graham Duncan duncangh

https://github.com/reireias/dotseeker

grep -r -E -v -h '^\s*#' files | grep --color=none -E "^\s*alias " | sed -e 's/#.*//g' -e 's/^\s*//g' -e 's/\s*$//g' | sort | uniq -c | sort -nr

 90 alias ls='ls --color=auto'
 87 alias grep='grep --color=auto'
 59 alias fgrep='fgrep --color=auto'

59 alias egrep='egrep --color=auto'

Preface

This article walks you through an example of deploying a Python 3.6 application that uses Pandas and AWS S3 on AWS Lambda using Boto3 in Python in 2018. No shell, no bash, no web console, everything is automated in Python. The previous article of a Hello World example can be found here.

Again, the reason to use Python Boto3 to interact with AWS is that,

I'm more familiar with Python than Bash, which means a Python script can be more flexible and powerful than Bash for me.
I'm not a fun of the AWS web console. It might be easier to do certain things, but it is definitely not automated.

Introduction

Summary

Many modern web browsers provide a speech synthesis API for JavaScript. You can write and invoke a function to have your notebook speak when it finishes executing certain cells, whether you're running it in JupyterLab (>=0.34) or classic Jupyter Notebook.

def speak(text):
    from IPython.display import Javascript as js, clear_output
 # Escape single quotes

How to update an existing vega view with new fetched data

This took quite a bit of figuring out because the web is littered with Vega 2 examples but Vega 3 is a bit different.

https://vega.github.io/vega/docs/api/view/#view_change

var spec = {
  data: {"url": "https://fivethirtyeight.datasettes.com/fivethirtyeight-45d758d/most-common-name%2Fsurnames.json?_shape=array"},
  mark: "bar",

	#!/bin/bash

	# * Defaults

	compression=xz
	subdir="web"

	# * Functions

	function debug {

	"""
	Django ORM Optimization Tips

	Caveats:
	* Only use optimizations that obfuscate the code if you need to.
	* Not all of these tips are hard and fast rules.
	* Use your judgement to determine what improvements are appropriate for your code.
	"""

	# ---------------------------------------------------------------------------

	import boto3
	import pandas as pd
	from io import BytesIO

	bucket, filename = "bucket_name", "filename.csv"

	s3 = boto3.resource('s3')
	obj = s3.Object(bucket, filename)
	with BytesIO(obj.get()['Body'].read()) as bio:
	df = pd.read_csv(bio)

	"""
	@Will Reynolds 2018
	this is a financial data gathering and manipulation program.

	"""

	# import libraries
	# you must have the following packages installed to run the program

	import requests

	import timeit

	setup='''
	import random, bisect
	from collections import deque

	random.seed('slartibartfast')
	s = [random.random() for i in range(1000)]
	timsort = sorted

	################################################################################################
	# name: convert_daily_to_monthly.py
	# desc: takes inout as daily prices and convert into monthly data
	# date: 2018-06-15
	# Author: conquistadorjd
	################################################################################################
	import pandas as pd
	import numpy as np

	print('* Program Started *')