In [4]:
from yummly import Client
import json
import requests
import pandas as pd
import numpy as np
import re
In [5]:
# API call for the first 500 BB recipes labeled as such only!
header= {'X-Yummly-App-ID':'79663a75', 'X-Yummly-App-Key':'02b233108f476f3110e0f65437c4d6dd'}
url='http://api.yummly.com/v1/api/recipes?'
parameters={
'allowedCourse[]':'course^course-Soups',
'excludedCourse[]': ['course^course-Main Dishes','course^course-Appetizers', 'course^course-Salads', 'course^course-Lunch',
'course^course-Side Dishes','course^course-Desserts','course^course-Breads',
'course^course-Breakfast and Brunch', 'course^course-Beverages', 'course^course-Condiments and Sauces',
'course^course-Cocktails', 'course^course-Snacks'],
'maxResult': 500,
'start': 0
}
response=requests.get(url, headers = header, params = parameters)
In [6]:
response.status_code
Out[6]:
In [7]:
SP=response.json()
print type(SP)
print SP.keys()
In [8]:
#only interrested in the information under matches.
print len(SP['matches'])
print type(SP['matches'])
print SP['matches'][0].keys()
In [9]:
#checkout one recipe
SP_matches=SP['matches']
SP_matches[499]
Out[9]:
In [10]:
#forming lists to create dataframes of the features we want.
main_list = []
ingredients_list = []
attributes_list = []
for food in SP_matches:
_d1 = {}
_d1['id'] = food['id']
_d1['rating'] = food['rating']
_d1['recipeName'] = food['recipeName']
_d1['sourceDisplayName'] = food['sourceDisplayName']
main_list.append(_d1)
_d2 = {}
_d2['id'] = food['id']
_d2['course'] = 'Breakfast and Brunch'
_d2['ingredient_list'] = food['ingredients']
for i in food['ingredients']:
i = i.lower() # additional code to convert to lowercase
i = re.sub(r'\d+%\s', '', i) # additional code to remove 1%, 2%, etc
i = re.sub(r'\xae', '', i) # remove '\xae' characters
i = re.sub(r'shredded\s', '', i)
i = re.sub(r'chopped\s', '', i)
i = re.sub(r'diced\s', '', i)
i = re.sub(r'crumbled\s', '', i)
i = re.sub(r'fresh\s', '', i)
i = re.sub(r'grated\s', '', i)
i = re.sub(r'fat free\s', '', i)
i = re.sub(r'boneless\s', '', i)
i = re.sub(r'boneless skinless\s', '', i)
i = re.sub(r'minced\s', '', i)
i = re.sub(r'sliced\s', '', i)
i = re.sub(r'(?!ground beef)ground ', '', i)
i = re.sub(r'^dried\s', '', i)
i = re.sub(r'^cooked\s', '', i)
_d2[i] = 1
ingredients_list.append(_d2)
_d3 = {}
_d3['id'] = food['id']
for k, v in food['attributes'].items():
for i in v:
_d3[i] = 1
attributes_list.append(_d3)
flavors_dict = {}
for food in SP_matches:
flavors_dict[food.get('id')] = food.get('flavors')
In [11]:
# read in csv for cuisine and create list of possible values
cuisine_df = pd.read_csv('/Users/bruktawitabebe/Desktop/Yummly/cuisine_headers.csv', names=['cuisine'])
cuisine_list= cuisine_df.cuisine
In [16]:
#create dictionary of cuisine and course for each recipe
cuisine_dict={}
for food in SP_matches:
cuisine_dict[food.get('id')]= food['attributes'].get('cuisine')
_cuisines= {}
for k, v in cuisine_dict.iteritems():
cuisine_val = {}
for course in cuisine_list:
try:
if course in v :
cuisine_val[course] = 1
else:
cuisine_val[course] = 0
except TypeError:
cuisine_val[course] = 0
_cuisines[k] = cuisine_val
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#get list of recipe ids
recipe_ids=[]
for recipe in SP_matches:
recipe_ids.append(recipe['id'])
In [19]:
# second api call to get other features for each recipe
key_id= '_app_id=79663a75&_app_key=02b233108f476f3110e0f65437c4d6dd'
url='http://api.yummly.com/v1/api/recipe/'
In [20]:
# retrieve other features for all recipes
def get_recipe(_id):
response = requests.get(url + _id + '?' + key_id)
return response.json()
recipes=[]
for _id in recipe_ids :
recipes.append(get_recipe(_id))
In [21]:
response.status_code
Out[21]:
In [22]:
print len(recipes)
print recipes[1].keys()
In [23]:
#for each recipe create a new dictionary of selected attributes and append into a list
recipe_details=[]
for recipe in recipes:
_dict={}
#import pdb; pdb.set_trace()
_dict['id']=recipe['id']
_dict['ingredientCount'] = len(recipe['ingredientLines'])
_dict['numberOfServings'] = recipe['numberOfServings']
_dict['prepTimeInSeconds'] = recipe.get('prepTimeInSeconds')
_dict['cookTimeInSeconds'] = recipe.get('cookTimeInSeconds')
_dict['totalTimeInSeconds'] = recipe.get('totalTimeInSeconds')
recipe_details.append(_dict)
In [25]:
#create dataframes, arrange column index and save into csv
df_main = pd.DataFrame(main_list)
df_main.to_csv('SP_main.csv', encoding ='utf-8')
df_ingredients = pd.DataFrame(ingredients_list)
df_ingredients = df_ingredients.fillna(0)
cols = list(df_ingredients)
cols.insert(0, cols.pop(cols.index('id')))
cols.insert(1, cols.pop(cols.index('course')))
df_ingredients= df_ingredients.ix[:,cols]
df_ingredients.to_csv('SP_ingredients.csv', encoding ='utf-8')
df_attributes = pd.DataFrame(attributes_list)
df_attributes = df_attributes.fillna(0)
cols = list(df_attributes)
cols.insert(0, cols.pop(cols.index('id')))
df_attributes = df_attributes.ix[:,cols]
df_attributes.to_csv('SP_attributes.csv')
df_flavors = pd.DataFrame(flavors_dict).transpose()
df_flavors.reset_index(level=0, inplace=True)
df_flavors.to_csv('SP_flavors.csv')
df_cuisines = pd.DataFrame(_cuisines).transpose()
df_cuisines.reset_index(level=0, inplace=True)
df_cuisines.to_csv('SP_cuisines.csv')
df_details=pd.DataFrame(recipe_details)
cols = list(df_details)
cols.insert(0, cols.pop(cols.index('id')))
df_details=df_details.ix[:,cols]
df_details.to_csv('SP_details.csv')