restaurant initial commit

Author: vijayanandrp

db_restaurants.py

@@ -0,0 +1,228 @@
+# PyMongo Tutorial : Insert, Read, Update, Delete in MongoDB
+
+# !/usr/bin/env python3.5
+# -*- coding: UTF-8 -*-
+
+import pprint
+import dateutil.parser
+import datetime
+
+try:
+    from pymongo import MongoClient
+except ImportError:
+    raise ImportError('PyMongo is not installed in your machine.')
+
+# The below code will connect on the default host and port.
+client = MongoClient()
+
+# The below code will connect on the specified  host and port.
+client = MongoClient(host='127.0.0.1', port=27017, maxPoolSize=100)
+
+# selecting database (use db)
+database = client['test']
+
+# selecting collection - kind of selecting table in NoSQL
+collection = database['restaurants']
+
+# get/query/fetch only one value from collection  without any conditions
+result = collection.find_one()
+
+# get/query/fetch all documents from collection without any conditions.
+# Below will return all values from collection
+result = collection.find()
+
+#  inserting a sample document
+sample_document_post = {
+    "address":       {
+        "street":   "2 Avenue",
+        "zipcode":  "10075",
+        "building": "1480",
+        "coord":    [-73.9557413, 40.7720266]
+    },
+    "borough":       "Manhattan",
+    "cuisine":       "Italian",
+    "grades":        [
+        {
+            "date":  dateutil.parser.parse("2014-10-01T00:00:00Z"),
+            "grade": "A",
+            "score": 11
+        },
+        {
+            "date":  dateutil.parser.parse("2014-01-16T00:00:00Z"),
+            "grade": "B",
+            "score": 17
+        }
+    ],
+    "name":          "Vella",
+    "restaurant_id": "41704620"
+}
+
+# post_id = collection.insert(sample_document_post)
+# pprint.pprint(post_id)
+
+#  Query by a Top Level Field
+''' I am trying to get the above restaurant ID  from collection  '''
+result = collection.find({"restaurant_id": "41704620"})
+
+# Query by a Top Level Field
+result = collection.find_one({"restaurant_id": "41704620"})
+
+#   Query by a Top Level Field
+''' The following operation finds documents whose borough field equals "Manhattan". '''
+result = collection.find({"borough": "Manhattan"})
+
+# Query by a Field in an Embedded Document
+'''
+To specify a condition on a field within an embedded document, use the dot notation.
+Dot notation requires quotes around the whole dotted field name.
+The following operation specifies an equality condition on the zipcode field in the address embedded document.
+Example -
+{"address": {
+        "street": "2 Avenue",
+        "zipcode": "10075",
+        "building": "1480",
+        "coord": [-73.9557413, 40.7720266 ]
+    }}
+'''
+result = collection.find({"address.zipcode": "10075"})
+
+#  Query by a Field in an Array
+'''
+The grades array contains embedded documents as its elements.
+To specify a condition on a field in these documents, use the dot notation.
+Dot notation requires quotes around the whole dotted field name.
+The following queries for documents whose grades array contains an embedded document with a field grade equal to "B".
+Example -
+{"grades": [
+        {
+            "date": dateutil.parser.parse("2014-10-01T00:00:00Z"),
+            "grade": "A",
+            "score": 11
+        },
+        {
+            "date": dateutil.parser.parse("2014-01-16T00:00:00Z"),
+            "grade": "B",
+            "score": 17
+        }]}
+'''
+result = collection.find({"grades.grade": "B"})
+
+#  Greater Than Operator ($gt)
+''' Query for documents whose grades array contains an embedded document with a field score greater than 30. '''
+result = collection.find({"grades.score": {'$gt': 30}})
+
+#  Lesser Than Operator ($lt)
+''' Query for documents whose grades array contains an embedded document with a field score lesser than 10. '''
+result = collection.find({"grades.score": {'$lt': 10}})
+
+#  Lesser Than Equals To Operator ($lte) / Greater Than Equals To Operator ($gte)
+''' I would like to get all documents in the year 2015 '''
+start_date = datetime.datetime(year=2015, month=1, day=1)
+end_date = datetime.datetime(year=2015, month=12, day=31)
+result = collection.find({'grades.date': {'$gte': start_date, '$lte': end_date}}).count()
+
+# Combine Conditions - Logical AND
+'''
+You can specify a logical conjunction (AND) for a list of query conditions by
+separating the conditions with a comma in the conditions document
+'''
+result = collection.find({"cuisine": "Italian", "address.zipcode": "10075"})
+
+# Combine Conditions - Logical OR
+'''
+You can specify a logical disjunction (OR) for a list of query conditions by using the $or query operator.
+'''
+result = collection.find({'$or': [{"cuisine": "Italian", "address.zipcode": "10075"}]})
+
+# Sort Query Results
+'''
+To specify an order for the result set, append the sort() method to the query.
+
+For example, the following operation returns all documents in the restaurants collection,
+sorted first by the borough field in ascending order, and then, within each borough,
+by the "address.zipcode" field in ascending order:
+'''
+result = collection.find().sort("borough").sort("address.zipcode")
+
+# Update Top-Level Fields
+'''
+The following operation updates the first document with name equal to "Juni",
+using the $set operator to update the cuisine field and the $currentDate operator to
+update the lastModified field with the current date.
+
+upsert (boolean): True - if no matching documents found, then create a new one.
+multi (boolean): True - if update all the matching records.
+'''
+result = collection.update({
+    "name": "Juni"}, {
+    '$set':         {"cuisine": "American (New) Vijay Anand"},
+    '$currentDate': {"lastModified": True}
+}, upsert=False, multi=False)
+
+
+# Remove/Delete All Documents That Match a Condition
+result = collection.remove({"cuisine": "American (New) Vijay Anand"})
+
+# Remove/Delete one Document - Use the justOne Option
+result = collection.remove({"borough": "Queens"}, {'$justOne': True})
+
+# Total count of all value from collection
+count = collection.find().count()
+pprint.pprint('Total documents  - {}'.format(count))
+
+# Total count value from collection
+count = collection.find({"restaurant_id": "41704620"}).count()
+pprint.pprint('Total documents found with {} - {}'.format({"restaurant_id": "41704620"}, count))
+
+# Group Documents by a Field and Calculate Count
+'''
+Use the $group stage to group by a specified key. In the $group stage,
+specify the group by key in the _id field. $group accesses fields by the
+field path, which is the field name prefixed by a dollar sign $.
+The $group stage can use accumulators to perform calculations for each group.
+The following example groups the documents in the restaurants collection by the
+borough field and uses the $sum accumulator to count the documents for each group.
+'''
+result = collection.aggregate([{'$group': {"_id": "$borough", "count": {'$sum': 1}}}])
+
+
+# Filter and Group Documents
+''' The _id field contains the distinct zipcode value, i.e., the group by key value. '''
+result = collection.aggregate([
+    {'$match': {"borough": "Brooklyn"}},
+    {'$group': {"_id": "$address.zipcode", "count": {'$sum': 1}}}])
+
+
+# $in operator for getting matching documents
+borough = ['Missing', 'Manhattan']
+result = collection.find({"borough": {'$in': borough}})
+
+
+# get overall database, collection information
+details = dict((db, [collection for collection in client[db].collection_names()])
+               for db in client.database_names())
+pprint.pprint(details)
+
+
+import pandas as pd
+
+
+def posts_2_df(iterator, chunk_size=1000):
+    """
+        Turn an iterator into multiple small pandas.DataFrame
+        This is a balance between memory and efficiency
+    """
+    records = []
+    frames = []
+    for index, record in enumerate(iterator):
+        records.append(record)
+    if index % chunk_size == chunk_size - 1:
+        frames.append(pd.DataFrame(records))
+        records = []
+    if records:
+        frames.append(pd.DataFrame(records))
+    return pd.concat(frames)
+
+result = collection.find({'grades.date': {'$gte': start_date, '$lte': end_date}})
+data_frame = posts_2_df(iterator=result, chunk_size=10000)
+print(data_frame.head())