Jayron Soares jayronsoares

This small subclass of the Pandas sqlalchemy-based SQL support for reading/storing tables uses the Postgres-specific "COPY FROM" method to insert large amounts of data to the database. It is much faster that using INSERT. To acheive this, the table is created in the normal way using sqlalchemy but no data is inserted. Instead the data is saved to a temporary CSV file (using Pandas' mature CSV support) then read back to Postgres using Psychopg2 support for COPY FROM STDIN.

	# Detecting correlation

	# Defines three functions using base R to illustrate techniques for identifying correlations
	# between continuous random variables, then tests against different types of data

	# Pearsons r, distance correlation, Maximal Information Coefficient (approximated)

	# A simple bootstrap function to estimate confidence intervals
	bootstrap <- function(x,y,func,reps,alpha){
	estimates <- c()

	#!/usr/bin/env python
	# -- coding: utf-8 --

	import json
	import sys
	import pymysql.cursors

	from pymysqlreplication import BinLogStreamReader
	from pymysqlreplication.row_event import (
	DeleteRowsEvent,

	import tweepy
	from tweepy import OAuthHandler
	import io

	def twitter_user(username,num):
	fw=io.open("twitter_user.txt",'a',encoding='utf8')
	ckey='Your Consumer Key'
	csecrett='Your Consumer Secret'

	atoken='Your Access Token'

	#!/usr/bin/env python
	# encoding: utf-8

	import sys
	from argparse import ArgumentParser
	from xml.dom import minidom
	try:
	from urllib.request import urlopen
	from urllib.parse import urlencode
	except ImportError: