World Trend

import pip
pip.main(["install","twitter"])
import pandas as pd
import json
import twitter
from twitter import Twitter 
from twitter import OAuth 
from twitter import TwitterHTTPError 
from twitter import TwitterStream 

ACCESS_TOKEN = '3151548164-KGXKM6vZipCfBfIA16bUxBaNhIcg7CmfiYb6iWU'
ACCESS_SECRET = '8qKGbi5JRnrZb3yKcKijbYxVHVZ4lFonuQDsN3dWonzao'
consumer_key = 'nqn05e7YWcj57FBzJhu2vJpQU'
consumer_secret = 'a3Y1S2DXVkbRoGaO3NMgstbGm4LSAyRnKSeShseZFrFqWmTwJo'
oauth = OAuth(ACCESS_TOKEN, ACCESS_SECRET, consumer_key, consumer_secret)
twit_api = Twitter(auth=oauth)

# World Trends
world_trends = twit_api.trends.available(_woeid=1)

# NYC Trends
from pandas.io.json import json_normalize
df = json_normalize(world_trends)
df[df.name == 'New York']
nyc_trends = twit_api.trends.place(_id = 2459115)
ny_trends = json_normalize(nyc_trends,'trends')
nyc_result = ny_trends.sort('tweet_volume', ascending = False)
from matplotlib import pyplot as plt
nyc_result.plot('name','tweet_volume', kind='bar')

# Nashville Trends
df[df.name == 'Nashville']
nashville_trends = twit_api.trends.place(_id=2457170 )
nash_trends = json_normalize(nashville_trends,'trends')
nash_result = nash_trends.sort('tweet_volume', ascending = False)
from matplotlib import pyplot as plt
nash_result.plot('name','tweet_volume', kind='bar')