About how to get taget data by using pipeline
Source code
- Ｄefine a pipeline to do similar query with GRPUP BY in SQL
# 統(tǒng)計(jì)一天內(nèi)交易成功的商品區(qū)域分布餅圖
def get_closed_item_chart():
pipeline = [
{'$match': {'saletime': ''}},
{'$group': {'_id': {'$slice': ['$address', 1, 1]}, 'counts': {'$sum': 1}}}
]
salesum = sum([i['counts'] for i in tinfo.aggregate(pipeline)])
#print(salesum)
for i in tinfo.aggregate(pipeline):
item = {
'name': i['_id'][0] +''+ str(int(float(i['counts']/salesum) * 10000) / 100)+'%',
'y': int(float(i['counts']/salesum) * 10000) / 100,
'type': 'pie'
}
yield item
#print(item)
#get_closed_item_chart()
- The data format to draw a pie chart is a little different from line chart and column chart.
data = [i for i in get_closed_item_chart()]
print(data)
cdata = [{
"type": "pie",
'name': 'sales',
'colorByPoint': True,
'data': data
}]
charts.plot(cdata, show='inline')
- The printed data is as below
[{'y': 0.17, 'type': 'pie', 'name': '崇明0.17%'}, {'y': 1.47, 'type': 'pie', 'name': '靜安1.47%'}, {'y': 3.08, 'type': 'pie', 'name': '黃浦3.08%'}, {'y': 1.47, 'type': 'pie', 'name': '南匯1.47%'}, {'y': 2.12, 'type': 'pie', 'name': '上海周邊2.12%'}, {'y': 2.36, 'type': 'pie', 'name': '青浦2.36%'}, {'y': 4.33, 'type': 'pie', 'name': '楊浦4.33%'}, {'y': 6.95, 'type': 'pie', 'name': '松江6.95%'}, {'y': 14.13, 'type': 'pie', 'name': '閔行14.13%'}, {'y': 5.1, 'type': 'pie', 'name': '普陀5.1%'}, {'y': 0.94, 'type': 'pie', 'name': '盧灣0.94%'}, {'y': 5.19, 'type': 'pie', 'name': '閘北5.19%'}, {'y': 3.13, 'type': 'pie', 'name': '長寧3.13%'}, {'y': 2.72, 'type': 'pie', 'name': '虹口2.72%'}, {'y': 5.19, 'type': 'pie', 'name': '寶山5.19%'}, {'y': 5.31, 'type': 'pie', 'name': '5.31%'}, {'y': 0.95, 'type': 'pie', 'name': '金山0.95%'}, {'y': 7.26, 'type': 'pie', 'name': '嘉定7.26%'}, {'y': 2.46, 'type': 'pie', 'name': '奉賢2.46%'}, {'y': 19.27, 'type': 'pie', 'name': '浦東19.27%'}, {'y': 6.3, 'type': 'pie', 'name': '徐匯6.3%'}]
The pie chart
Screen Shot 2016-07-13 at 7.33.32 PM.png
