find_one.py

weekly_demand_collection.find_one()

mongo_queries_1.py

# importing the required libraries
import pymongo
import pprint
import json
import warnings
warnings.filterwarnings('ignore')

# connect to the mongoclient
client = pymongo.MongoClient('mongodb://localhost:27017')

# get the database
database = client['sample_db']

mongo_queries_2.py

# create weekly demand collection
database.create_collection("weekly_demand")

# create meal_info collection
database.create_collection("meal_info")

mongo_queries_3.py

# get collection weekly_demand
weekly_demand_collection = database.get_collection("weekly_demand")

# open the weekly_demand json file
with open("weekly_demand.json") as f:
    file_data = json.load(f)
# insert the data into the collection
weekly_demand_collection.insert_many(file_data)

# get the count of total data points
weekly_demand_collection.find().count()
# >> 456548

# get collection meal_info 
meal_info_collection = database.get_collection("meal_info")

# open the meal_info json file
with open("meal_info.json") as f:
    file_data = json.load(f)
    
# insert the data into the collection
meal_info_collection.insert_many(file_data)

# get the count of the total data points
meal_info_collection.find().count()
# >> 51

q1.py

weekly_demand_collection.find_one( {}, { "week": 1, "checkout_price" : 1})

q10.py

meal_info_collection.find_one()

q11.py

result_7 = meal_info_collection.find({
    "cuisine" : { "$regex" : "^C" }
})

for i in result_7:
    print(i)

q12.py

result_8 = meal_info_collection.find({
    "$and" : [
        { 
            "category" : {
            "$regex" : "^S"
        }},
        {
            "cuisine" : {
                "$regex" : "ian$"
        }}
    ]
})

for i in result_8:
    print(i)

q13.py

result_9 = weekly_demand_collection.aggregate([
    ## stage 1
    {
        "$match" : 
                 {"center_id" : {"$eq" : 11 } }
    },
    ## stage 2
    {
        "$count" : "total_rows"
    }
])

for i in result_9:
    print(i)

q14.py

result_10 = weekly_demand_collection.aggregate([
    ## stage 1
    {
        "$match" : 
                 {"center_id" : {"$eq" : 11 } }
    },
    ## stage 2
    {
        "$group" : { "_id" : 0 ,
                     "average_num_orders": { "$avg" : "$num_orders"},
                     "unique_meal_id" : {"$addToSet" : "$meal_id"}} 
    }
])

for i in result_10:
    print(i)

q2.py

weekly_demand_collection.find_one( {}, {"num_orders" : 0, "meal_id" : 0})

q3.py

weekly_demand_collection.find_one( {"center_id" : 55, "meal_id" : 1885}, {"_id" : 0, "week" : 0} )

q4.py

result_1 = weekly_demand_collection.find({
    "center_id" : { "$eq" : 55},
    "homepage_featured" : { "$ne" : 0}
})

for i in result_1:
    print(i)

q5.py

result_2 = weekly_demand_collection.find({
    "center_id" : { "$in" : [ 24, 11] }
})

for i in result_2:
    print(i)

q6.py

result_3 = weekly_demand_collection.find({
    "center_id" : { "$nin" : [ 24, 11] }
})

for i in result_3:
    print(i)

q7.py

result_4 = weekly_demand_collection.find({
    "center_id" : 55,
    "checkout_price" : { "$lt" : 200, "$gt" : 100}
})

for i in result_4:
    print(i)

q8.py

result_5 = weekly_demand_collection.find({
    "$and" : [{
                 "center_id" : { "$eq" : 11}
              },
              {
                   "meal_id" : { "$ne" : 1778}
              }]
})

for i in result_5:
    print(i)

q9.py

result_6 = weekly_demand_collection.find({
    "$or" : [{
                 "center_id" : { "$eq" : 11}
              },
              {
                   "meal_id" : { "$in" : [1207, 2707]}
              }]
})

for i in result_6:
    print(i)