erichannell

from random import randrange, random, choice
import dataextract as tde
import os
import string
import time
import csv

def write_to_extract(rows_to_write, name):

    start = time.time()

erichannell

from TwitterSearch import *
import csv

def get_tweets(query, max = 2000):
    # takes a search term (query) and a max number of tweets to find
    # gets content from twitter and writes it to a csv bearing the name of your query
    
    i = 0
    search = query

erichannell

{"nbformat_minor": 0, "cells": [{"execution_count": null, "cell_type": "code", "source": "print \"Hello, world!\" #ohai! this is a comment", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "name = \"Eric\" # creating a variable\nprint \"Hello\" + \" \" + name # adding strings", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "your_name = raw_input(\"Who are you? \")\nprint \"Hello\", your_name # , adds spaces", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "print your_name.lower() # string method\nprint your_name.upper()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "i = 0\nprint i\ni = i + 1 # i += 1\nprint i", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "

erichannell

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are working with Comma-separated value (CSV) files, so we'll need that library."
   ]
  },
  {

erichannell

from textblob import Word
import re

def parse(text):
    pattern = '''Synset\(\'(.*)\.n'''
    return re.findall(pattern,text)[0]

def classify(word):
    try:
        word = Word(word)

erichannell

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Importing the **Extract API**"
   ]
  },
  {

erichannell

import pybikes
import csv

bike_system = "bicing"

bicing = pybikes.get(bike_system)

print bicing.meta

bicing.update()

erichannell

# -*- coding: utf-8 -*-

import tableauserverclient as TSC

# migrate from
site = ""
user = ""
pwd = ""
tableau_auth = TSC.TableauAuth(user, pwd, site_id=site)
server = TSC.Server('https://dub01.online.tableau.com')

erichannell

import pandas as pd

df_gps = pd.read_json('input.json')

# parse lat, lon, and timestamp from the dict inside the locations column
df_gps['lat'] = df_gps['locations'].map(lambda x: x['latitudeE7'])
df_gps['lon'] = df_gps['locations'].map(lambda x: x['longitudeE7'])
df_gps['timestamp_ms'] = df_gps['locations'].map(lambda x: x['timestampMs'])

# convert lat/lon to decimalized degrees and the timestamp to date-time

erichannell

import reverse_geocoder as rg
import csv

with open("output.csv", 'w') as outf:
   writer = csv.writer(outf)
   writer.writerow(['index', 'lat', 'lon', 'datetime', 'country', 'location', 'area1', 'area2''])

   with open("input.csv", 'r') as inf:
       reader = csv.reader(inf)
       reader.next()