Unable to reload data as a csv file from IPython Notebook - api

Question

I have the following IPython Notebook, I am trying to access data base of movies from rotten tomatoes website.
But Rotten Tomatoes limits to 10,000 API requests a day
So I don't want to re-run this function every time when I restart the notebook, I am trying to save and reload this data as a CSV file. When I convert the data to a csv file I am getting this processing symbol[*] inside IPython notebook. After some time I am getting the following error
ConnectionError: HTTPConnectionPool(host='api.rottentomatoes.com', port=80): Max retries exceeded with url: /api/public/v1.0/movie_alias.json?apikey=5xr26r2qtgf9h3kcq5kt6y4v&type=imdb&id=0113845 (Caused by <class 'socket.gaierror'>: [Errno 11002] getaddrinfo failed)
Is this problem due to slow internet connection? Should I make some changes to my code? Kindly help me with this.
The code for the file is shown below:
%matplotlib inline
import json
import requests
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
api_key = '5xr26r2qtgf9h3kcq5kt6y4v'
movie_id = '770672122' # toy story 3
url = 'http://api.rottentomatoes.com/api/public/v1.0/movies/%s/reviews.json' % movie_id
#these are "get parameters"
options = {'review_type': 'top_critic', 'page_limit': 20, 'page': 1, 'apikey': api_key}
data = requests.get(url, params=options).text
data = json.loads(data) # load a json string into a collection of lists and dicts
print json.dumps(data['reviews'][0], indent=2) # dump an object into a json string
from io import StringIO
movie_txt = requests.get('https://raw.github.com/cs109/cs109_data/master/movies.dat').text
movie_file = StringIO(movie_txt) # treat a string like a file
movies = pd.read_csv(movie_file,delimiter='\t')
movies
#print the first row
movies[['id', 'title', 'imdbID', 'year']]
def base_url():
return 'http://api.rottentomatoes.com/api/public/v1.0/'
def rt_id_by_imdb(imdb):
"""
Queries the RT movie_alias API. Returns the RT id associated with an IMDB ID,
or raises a KeyError if no match was found
"""
url = base_url() + 'movie_alias.json'
imdb = "%7.7i" % imdb
params = dict(id=imdb, type='imdb', apikey=api_key)
r = requests.get(url, params=params).text
r = json.loads(r)
return r['id']
def _imdb_review(imdb):
"""
Query the RT reviews API, to return the first page of reviews
for a movie specified by its IMDB ID
Returns a list of dicts
"""
rtid = rt_id_by_imdb(imdb)
url = base_url() + 'movies/{0}/reviews.json'.format(rtid)
params = dict(review_type='top_critic',
page_limit=20,
page=1,
country='us',
apikey=api_key)
data = json.loads(requests.get(url, params=params).text)
data = data['reviews']
data = [dict(fresh=r['freshness'],
quote=r['quote'],
critic=r['critic'],
publication=r['publication'],
review_date=r['date'],
imdb=imdb, rtid=rtid
) for r in data]
return data
def fetch_reviews(movies, row):
m = movies.irow(row)
try:
result = pd.DataFrame(_imdb_review(m['imdbID']))
result['title'] = m['title']
except KeyError:
return None
return result
def build_table(movies, rows):
dfs = [fetch_reviews(movies, r) for r in range(rows)]
dfs = [d for d in dfs if d is not None]
return pd.concat(dfs, ignore_index=True)
critics = build_table(movies, 3000)
critics.to_csv('critics.csv', index=False)
critics = pd.read_csv('critics.csv')