matmoody

from tweepy.streaming import StreamListener
from tweepy import OAuthHandler
from tweepy import Stream
import json
import requests

client_key = "TwitterConsumerKey"
client_secret = "TwitterConsumerSecret"
token = "TwitterAppAccessToken"
token_secret = "TwitterAppAccessTokenSecret"

matmoody

import json
import requests


class LiveFace():

  # Connect To Nest Cam REST Streaming API
  def get_stream(self, cam_url):
    # Manipulate the response so we can detect if motion was detected
    DATA_PREFIX = 'data: '

matmoody

import pandas as pd
from scipy import stats

data = '''Region,Alcohol,Tobacco
North,6.47,4.03
Yorkshire,6.13,3.76
Northeast,6.19,3.77
East Midlands,4.89,3.34
West Midlands,5.63,3.47
East Anglia,4.52,2.92

matmoody

# Frequency
import collections

testlist = [1, 4, 5, 6, 7, 9, 9, 9]

c = collections.Counter(testlist)

print c

# calculate number of instances in list

matmoody

import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
%matplotlib inline

loansData = pd.read_csv('https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv')

loansData.head()

# Remove rows with null values

matmoody

from scipy import stats
import collections

# Apply collections.Counter() on # open credit lines in Loans data to get counts of observations for each # credit lines

# Load reduced version of Lending Club dataset
loansData = pd.read_csv("https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv")

# Drop null rows
loansData.dropna(inplace=True)

matmoody

import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
%matplotlib inline


import pandas as pd

loansData = pd.read_csv('https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv')

matmoody

import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
%matplotlib inline

loansData = pd.read_csv('https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv')

# Clean Interest.Rate
loansData['Interest.Rate'] = loansData['Interest.Rate'].map(lambda x: float(x.rstrip('%')))

matmoody

import collections
import matplotlib.pyplot as plt
import pandas as pd
import requests
import sqlite3 as lite
import time
from pandas.io.json import json_normalize
from dateutil.parser import parse 

%matplotlib inline

matmoody

import requests
import sqlite3 as lite
import datetime

api_key = 'API_KEY'

url = 'https://api.forecast.io/forecast/' + api_key

cities = { "Wamego": '39.201941,-96.304998',
            "Grinnell": '39.127230,-100.629031',