View xpath-cheatsheet.js
// XPath CheatSheet
// To test XPath in your Chrome Debugger: $x('/html/body')
// 0. XPath Examples.
// More:
'//hr[@class="edge" and position()=1]' // every first hr of 'edge' class
import requests
import re
import glob
import os
import sys
import pprint
import pandas
import lxml
import lxml.html
View Untitled.csv
We can't make this file beautiful and searchable because it's too large.
"_id" title authors.0 authors.1 publisher
59b1349a30164b0f93a7ca2c On Gryphon's Wings T. C. Portier Balboa Press
59b1349a30164b0f93a7ca2d Murder in the Vatican, The CIA and the Bolshevik Pontiff Lucien Gregoire Author House
59b1349a30164b0f93a7ca2e The Taylor Women Shirley Harrington-Moore iUniverse
59b1349a30164b0f93a7ca2f Roll On Sugaree Loyd Little Author House
59b1349a30164b0f93a7ca30 The Pelican's Briefs Mark Joneschiet iUniverse
59b1349a30164b0f93a7ca31 Colophon, A Novel of Renaissance Florence Jo Ford iUniverse
59b1349a30164b0f93a7ca32 December Gold Ron Mitchell WestBow Press
59b1349a30164b0f93a7ca33 Mom's Gold Star Robert Bailey iUniverse
View foodler_md5_ipython.ipynb
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
View learning.txt
Learning How To Learn
Module 1 - What is Learning
Focused/Diffuse Modes Thinking
- Obviously ‘focused’ is when you’re concentrating. Direct approach to solving familiar problems.
- Focused: thoughts move through nicely-paved road of familiar notions (neural pattern looks very tight and directed).
- encompasses rational, sequential, analytical approaches to thinking
- Diffuse: More of a search function neural pattern. Thoughts move widely. More of a broad/big-picture perspective trying to connect ideas from different places.
- We’re always either in focused or diffuse mode of thinking.
View similar-tweets.json
"rows": [
"cell": [ "08/04/17 08:00:45", "MEXICAN - ATLANTE UTN", "Atlante", "#BuenD\u00eda #Azulgranas #YaEsViernes y tu coraz\u00f3n los sabe.\n\u00a1HOY juega el Potro!\n#TodosSomosAtlante", "893441576344723456", "Yes", "91.59", "tweet", "similarity-threshold" ] },
"cell": [ "08/03/17 18:04:42", "MEXICAN - ATLANTE UTN", "Atlante", "Acude a #LaPreviaAzulgrana en el #Andr\u00e9sQuintanaRoo antes del partido Atlante vs Celaya.\n#TodosSomosAtlante", "893231176705507332", "Yes", "93.62", "tweet", "similarity-threshold" ] },
"cell": [ "08/03/17 17:36:22", "MEXICAN - ATLANTE UTN", "Atlante", "A retomar el paso ganador: \n\u201cPotro\u201d Guti\u00e9rrez\n\n++ Atlante va ante Celaya ante su afici\u00f3n por su segundo triunfo...", "893224045927436288", "Yes", "93.82", "tweet", "similarity-threshold" ] },
"cell": [ "08/03/17 12:19:23", "MEXICAN - ATLANTE UTN", "Atlante", "#LizandroEcheverr\u
View hack.js
function click_all_the_things() {
buttons = $('.sgbutton').toArray()
function clickbutton() {
if(buttons.length > 0){
button = buttons.pop();
console.log("clicking button", button);;
from bson.objectid import ObjectId
from import *
import pprint
client = pymongo.MongoClient('mongodb://')
db = client['sportmanias']['tweets']
parent = Tw(db.find_one({'id_str':'890067420207042561'}))
a = Tw(db.find_one({'id_str':'889706097598054400'}))
import numpy as np
from sklearn.cluster import MeanShift, estimate_bandwidth
import os
import stat
files = ['archives/2017-07/{}'.format(f) for f in list(os.walk('archives/2017-07/'))[0][2]]
ar = []
arr = []
curl '' \
-H 'pragma: no-cache' \
-H 'accept-encoding: gzip, deflate, br' \
-H 'accept-language: en-US,en;q=0.8' \
-H 'upgrade-insecure-requests: 1' \
-H 'user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36' \
-H 'x-chrome-uma-enabled: 1' \
-H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8' \
-H 'cache-control: no-cache' \
-H 'authority:' \