AGGREGATE world: Difference between revisions
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==Country Profile== | ==Country Profile== | ||
For these questions you should use | For these questions you should use aggregate([]) on the collection <code>world</code> | ||
<div class='extra_space' style='width:1em; height:6em;'></div> | <div class='extra_space' style='width:1em; height:6em;'></div> | ||
<div class=q data-lang="py3"> | <div class=q data-lang="py3"> | ||
Revision as of 12:46, 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)
Country Profile
For these questions you should use aggregate([]) on the collection world
Give the name and the per capita GDP for those countries with a population of at least 200 million.
per capita GDP is the GDP divided by the population GDP/population
The aggregation framework is a data processing pipeline. There are many operators that you can use.
$match uses a query to limit or 'filter' what documents are to be used in the next stage of the pipeline.
$project is used to "shape" documents by adding or removing fields. It also allows you to compare fields with the syntax $<fieldname>
pp.pprint(list(
db.world.aggregate([
{"$match":{
"population":{"$gte":250000000}
}},
{"$project":{
"_id":0,
"name":1,
"per capita GDP": {"$divide": ["$gdp",1000000]}
}}
])
))
pp.pprint(list(
db.world.aggregate([
{"$match":{
"population":{"$gte":200000000}
}},
{"$project":{
"_id":0,
"name":1,
"per capita GDP": {"$divide": ["$gdp","$population"]}
}}
])
))