1. make a backup of database

1st, open a terminal in jupyter $ mongod $ mongo 2nd, select the db you will use $ show dbs $ use awms 3rd, select which table you will use $ show tables 4th, create a backup table and copy the data from orgin one to the one newly created $ db.createCollection('loginfo_backup') $ db.loginfo.copyTo('logdata_backup') 5th, use backup db when sort data

import pymongo
client = pymongo.MongoClient('localhost', 27017)
awms = client['awms']
loginfo = awms['loginfo_backup']

2. String of ‘\xa0’ in [‘caseid’]

1.png 1st, use this codes to find out which one has ‘\xa0’

for i in loginfo.find():
    if '\xa0' in i['caseid']:
        print(i['caseid'])

2.png

2nd, delete this string of data, you need to j = "".join(j.split()),or j = ''.join(j.split()) refer to :https://www.cnblogs.com/yqpy/p/8203783.html

for i in loginfo.find():
    if '\xa0' in i['caseid']:
        j = i['caseid']
        j = "".join(j.split())
        if '\xa0' in j:
            print('yes')
        else:
            print('no')

3.png

3rd, update the refreshed data to db

for i in loginfo.find():
    if '\xa0' in i['caseid']:
        j = i['caseid']
        j = ''.join(j.split())
        loginfo.update({'_id':i['_id']},{'$set':{'caseid':j}})

1.png

3. Make data visible by highcharts

import charts package

import charts

shift + enter, it runs well if the following come out‘Server running in the folder /Users/osx at 127.0.0.1:65060’

firstly, let’s see a sample

series = [{
    'name': 'OS X',
    'data': [11],
    'type': 'column'
},{
    'name': 'windows',
    'data': [12],
    'type': 'column'
},{
    'name': 'other',
    'data': [2],
    'type': 'column'
}]
charts.plot(series, show='inline', options=dict(title=dict(text='charts are nice!')))

2.png

NOW, let’s start to make a chart to show the volume of each city 1st, unduplicated city names

city_list = []
for i in loginfo.find():
    city_list.append(i['city'])
city_index = list(set(city_list))
print(city_index)

# unduplicated city索引, 集合set可自动去重 ![3.png](http://user-image.logdown.io/user/42937/blog/39533/post/4734411/i4cAurYwR3GMDsjSbT4U_3.png)

2nd, volumes of each unduplicated city

post_times = []
for index in city_index:
    post_times.append(city_list.count(index))
print(post_times)

# count each unduplicated city in the duplicated city_list

4.jpg

3rd, make a data generator for making a chart

def data_gen(types):
    length = 0
    if length <= len(city_index):
        for city, times in zip(city_index, post_times):
            data = {
                'name': city,
                'data': [times],
                'type': types
            }
            yield data
            length += 1

5.jpg

4th, make the chart

series = [data for data in data_gen('column')]
charts.plot(series, show='inline', options=dict(title=dict(text='case city')))

6.jpg

Tipping