Difference between revisions of "AGGREGATE examples"
From NoSQLZoo
| Line 43: | Line 43: | ||
"_id":0, | "_id":0, | ||
"name":1, | "name":1, | ||
| − | |||
| − | |||
"per capita GDP": {"$divide": ["$gdp","$population"]} | "per capita GDP": {"$divide": ["$gdp","$population"]} | ||
}} | }} | ||
| Line 51: | Line 49: | ||
</pre> | </pre> | ||
<div class=ans> | <div class=ans> | ||
| − | pp.pprint(list(db.world.aggregate([{"$project":{"_id":0,"name | + | pp.pprint(list(db.world.aggregate([{"$project":{"_id":0,"name":1,"per capita GDP": {"$divide": ["$gdp","$population"]}}}]) |
))["$population","$area"]}}}]))) | ))["$population","$area"]}}}]))) | ||
</div> | </div> | ||
</div> | </div> | ||
Revision as of 13:15, 16 July 2015
#ENCODING
import io
import sys
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-16')
#MONGO
from pymongo import MongoClient
client = MongoClient()
client.progzoo.authenticate('scott','tiger')
db = client['progzoo']
#PRETTY
import pprint
pp = pprint.PrettyPrinter(indent=4)
Introducing the aggregation framework
These examples introduce the aggregation framework and its operators. Again we will be using the collection world
$match Allows us to perform queries in a similar way to find()
Show all the details for France
pp.pprint(list(
db.world.aggregate([
{"$match":{"name":"France"}}
])
))
pp.pprint(list(db.world.aggregate([{"$match":{"name":"France"}}])))
$project Allows us to select what fields to display.
It can also has the ability to insert new fields and allows you to compare fields against each other without using $where
Show the name and per capita GDP of all countries. (gdp/population)
pp.pprint(list(
db.world.aggregate([
{"$project":{
"_id":0,
"name":1,
"per capita GDP": {"$divide": ["$gdp","$population"]}
}}
])
))
pp.pprint(list(db.world.aggregate([{"$project":{"_id":0,"name":1,"per capita GDP": {"$divide": ["$gdp","$population"]}}}]) ))["$population","$area"]}}}])))