使用highcharts显示mongodb中的数据
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1、mongodb数据表相关
# 显示数据库
show dbs
# 数据库
use ceshi
# 显示表
show tables
# 创建集合
db.createCollection(\'infoB\')
# 复制数据
db.item_infoA.copyTo(\'infoB\')
# 使用命令导入json 格式的数据
mongoimport -d database_name -c collection_name inpath/file_name.json
# 使用命令导出json 格式的数据
mongoexport -d database_name -c collection_name -o outputpath/file_name.json
2、常用的update与find函数以及日期相关
from string import punctuation for i in item_info.find().limit(50): print(i[\'province\']) for i in item_info.find(): if i[\'province\']: province= [i for i in i[\'province\'] if i not in punctuation] else: province= [\'不明\'] # 下面update函数使用了两个参数，第一个标识要更新哪些数据，第二个标识怎样修改 # \'_id\':i[\'_id\']，key:value一一对应，通过这种方式表示要更新每一项 sales.update({\'_id\':i[\'_id\']},{\'$set\':{\'province\':province}}) # find函数，两个参数，分别包含在{}中，第一个标识要找的条件，是一些键值对，第二个标识需要显示的字段，0不显示，1标识显示 # slice分片 for i in item_info.find({\'pub_date\':{\'$in\':[\'2016.01.12\',\'2016.01.14\']}},{\'area\':{\'$slice\':1},\'_id\':0,\'price\':0,\'title\':0}).limit(300): print(i)
from datetime import date from datetime import timedelta #日期 a = date(2017,1,12) print(a) # 2017-01-12 d = timedelta(days=1) print(d) # 1 day, 0:00:00 def get_all_dates(date1,date2): the_date = date(int(date1.split(\'.\')[0]),int(date1.split(\'.\')[1]),int(date1.split(\'.\')[2])) end_date = date(int(date2.split(\'.\')[0]),int(date2.split(\'.\')[1]),int(date2.split(\'.\')[2])) days = timedelta(days=1) while the_date <= end_date: yield (the_date.strftime(\'%Y.%m.%d\')) the_date = the_date + days for i in get_all_dates(\'2017.01.02\',\'2017.01.12\'): print(i)
3、相关数据格式
西红柿 蔬菜 山东 2.8 新 1500 kg 2017-1-11 卷心菜 蔬菜 河北 1.5 鲜 1000 kg 2017-1-9 玉米 粮食 辽宁 0.8 新 1580 kg 2016-11-25 大豆 粮食 山东 1.1 新 1000 kg 2017-1-8 卷心菜 蔬菜 河北 1.5 鲜 2705 kg 2017-1-9 玉米 粮食 辽宁 0.8 新 1669 kg 2016-11-25 大米 粮食 浙江 0.7 新 2115 kg 2016-11-28 大米 粮食 江苏 0.8 新 2151 kg 2016-11-15 西瓜 水果 山东 0.5 鲜 1518 kg 2016-10-1 山楂 水果 山东 2.5 鲜 1116 kg 2016-9-1 茄子 蔬菜 江苏 1.1 鲜 1500 kg 2016-9-15 小麦 粮食 河北 1.2 新 1695 kg 2016-9-1 葡萄 水果 山东 2.1 鲜 1719 kg 2016-9-17
4 、按照产品分类计算销售额
import charts
def data_gen(cates): pipeline = [ {\'$match\':{\'$and\':[ {\'category\':{\'$in\':cates}}, {\'province\':{\'$nin\':[\'江苏\']}} ]}}, {\'$group\':{\'_id\':\'$category\',\'sum_sales\':{\'$sum\':{ \'$multiply\':[\'$price\',\'$quantity\'] }}}}, {\'$sort\':{\'sum_sales\':1}} ] for i in salesnew.aggregate(pipeline): data = { \'name\': i[\'_id\'], \'data\': [i[\'sum_sales\']], \'type\': \'column\' } yield data for i in data_gen([\'水果\',\'蔬菜\',\'粮食\']): print(i) series = [i for i in data_gen([\'水果\',\'蔬菜\',\'粮食\'])] options = { \'chart\' : {\'zoomType\':\'xy\'}, \'title\' : {\'text\': \'销售金额\'}, \'subtitle\': {\'text\': \'图表\'}, \'yAxis\' : {\'title\': {\'text\': \'金额\'}} } charts.plot(series,options=options,show=\'inline\')
结果：
值得注意的一点，在管道中不好进行数据类型的转换，所以最好存入mongodb中的数据是正确的数据类型。
关于数据类型的转换参考文章 how to convert string to numerical values in mongodb 地址：http://stackoverflow.com/questions/29487351/how-to-convert-string-to-numerical-values-in-mongodb
#代码： db.my_collection.find({moop : {$exists : true}}).forEach( function(obj) { obj.moop = new NumberInt( obj.moop ); db.my_collection.save(obj); } );
5、计算每个月的销售数量
def data_gen(cates): pipeline = [ { \'$project\' : { \'quantity\': 1,\'province\': 1,\'saledate\': 1,\'category\':1,\'ymstring\' : { \'$concat\': [ {\'$arrayElemAt\': [ {\'$split\': [\'$saledate\', \'-\']}, 0 ]},\'-\', {\'$arrayElemAt\': [ {\'$split\': [\'$saledate\', \'-\']}, 1 ]}] }}}, {\'$match\':{\'$and\':[ {\'category\':{\'$in\':cates}}, {\'province\':{\'$nin\':[\'江苏\']}} ]}}, {\'$group\':{\'_id\':\'$ymstring\' ,\'sum_quantity\':{\'$sum\':\'$quantity\'}}}, {\'$sort\':{\'sum_quantity\':1}} ] for i in salesnew.aggregate(pipeline): yield i for i in data_gen([\'水果\',\'蔬菜\',\'粮食\']): print(i)
# 结果 {\'_id\': \'2016-10\', \'sum_quantity\': 1518} {\'_id\': \'2016-8\', \'sum_quantity\': 4350} {\'_id\': \'2016-12\', \'sum_quantity\': 8223} {\'_id\': \'2016-11\', \'sum_quantity\': 11283} {\'_id\': \'2016-9\', \'sum_quantity\': 12037} {\'_id\': \'2017-1\', \'sum_quantity\': 12394}
各个函数的相关参考 https://docs.mongodb.com/manual/reference/operator/aggregation/
语句： \'$concat\': [ {\'$arrayElemAt\': [ {\'$split\': [\'$saledate\', \'-\']}, 0 ]}, \'-\', {\'$arrayElemAt\': [ {\'$split\': [\'$saledate\', \'-\']}, 1 ]} ] 解释如下： # 分组 \'$split\': [\'$saledate\', \'-\'] # 数组中的元素，语法：$arrayElemAt: [ <array>, <idx> ] # 因为$split也是函数，所以用{}来包含 \'$arrayElemAt\': [ {\'$split\': [\'$saledate\', \'-\']}, 0 ] \'$arrayElemAt\': [ {\'$split\': [\'$saledate\', \'-\']}, 1 ] # 最后，用$concat函数连接，语法{ $concat: [ <expression1>, <expression2>, ... ] } # 同样，由于$arrayElemAt函数，所以用{}来包含{\'$arrayElemAt\': [ \'arrayname\', 0 ]}，否则，不需要{}
#以下两个函数作用相同，区别在于，第一个\'$slice在$group中，第二个在$project中
$slice可以指定从第几个元素开始分片
{ $slice: [ <array>, <position>, <n> ] }
{ $slice: [ <array>, <n> ] }
def data_gen(cates): pipeline = [ { \'$project\' : { \'quantity\': 1,\'province\': 1,\'saledate\': 1,\'category\':1,\'ymarray\' : { \'$split\': [\'$saledate\', \'-\'] }}}, {\'$match\':{\'$and\':[ {\'category\':{\'$in\':cates}}, {\'province\':{\'$nin\':[\'江苏\']}} ]}}, {\'$group\':{\'_id\':{ \'$slice\': [\'$ymarray\',2] },\'sum_quantity\':{\'$sum\':\'$quantity\'}}}, {\'$sort\':{\'sum_quantity\':1}} ] for i in salesnew.aggregate(pipeline): print(\'ymarray\') yield i for i in data_gen([\'水果\',\'蔬菜\',\'粮食\']): print(i) { \'$slice\':[ {\'$split\': [\'$saledate\', \'-\']},2 ]}
def data_gen(cates): pipeline = [ { \'$project\' : { \'quantity\': 1,\'province\': 1,\'saledate\': 1,\'category\':1,\'ymarray\' : { \'$slice\':[ {\'$split\': [\'$saledate\', \'-\']},2 ]} }}, {\'$match\':{\'$and\':[ {\'category\':{\'$in\':cates}}, {\'province\':{\'$nin\':[\'江苏\']}} ]}}, {\'$group\':{\'_id\':\'$ymarray\',\'sum_quantity\':{\'$sum\':\'$quantity\'}}}, {\'$sort\':{\'sum_quantity\':1}} ] for i in salesnew.aggregate(pipeline): yield i for i in data_gen([\'水果\',\'蔬菜\',\'粮食\']): print(i)
# 结果 {\'_id\': [\'2016\', \'10\'], \'sum_quantity\': 1518} {\'_id\': [\'2016\', \'8\'], \'sum_quantity\': 4350} {\'_id\': [\'2016\', \'12\'], \'sum_quantity\': 8223} {\'_id\': [\'2016\', \'11\'], \'sum_quantity\': 11283} {\'_id\': [\'2016\', \'9\'], \'sum_quantity\': 12037} {\'_id\': [\'2017\', \'1\'], \'sum_quantity\': 12394}
6、计算每个月的销售额
def data_gen(cates): pipeline = [ { \'$project\' : { \'quantity\': 1,\'province\': 1,\'saledate\': 1,\'category\':1 , \'price\':1}}, {\'$match\':{\'$and\':[ {\'category\':{\'$in\':cates}}, {\'province\':{\'$nin\':[\'江苏\']}} ]}}, # 先统计每天的销售额，注意$multiply函数的用法 {\'$group\':{\'_id\':\'$saledate\',\'sum_quantity\':{\'$sum\':{ \'$multiply\':[\'$price\',\'$quantity\'] }}}}, # 在上面的基础上继续分组，构造月份作为分组依据，注意上面的$saledate变为$_id，sum_quantity变为$sum_quantity，前面有$符号 {\'$group\':{\'_id\':{\'$concat\': [ {\'$arrayElemAt\': [ {\'$split\': [\'$_id\', \'-\']}, 0 ]},\'-\', {\'$arrayElemAt\': [ {\'$split\': [\'$_id\', \'-\']}, 1 ]}]},\'sumend\':{\'$sum\':\'$sum_quantity\'}}}, {\'$sort\':{\'sumend\':1}} ] for i in salesnew.aggregate(pipeline): data = { \'name\': i[\'_id\'], \'data\': [i[\'sumend\']], \'type\': \'column\' } yield data for i in data_gen([\'水果\',\'蔬菜\',\'粮食\']): print(i) series = [i for i in data_gen([\'水果\',\'蔬菜\',\'粮食\'])] options = { \'chart\' : {\'zoomType\':\'xy\'}, \'title\' : {\'text\': \'销售数量\'}, \'subtitle\': {\'text\': \'图表\'}, \'yAxis\' : {\'title\': {\'text\': \'数量\'}} } charts.plot(series,options=options,show=\'inline\')
def data_gen(cates): pipeline = [ { \'$project\' : { \'quantity\': 1,\'province\': 1,\'saledate\': 1,\'category\':1 , \'price\':1 }}, {\'$match\':{\'$and\':[ {\'category\':{\'$in\':cates}}, {\'province\':{\'$nin\':[\'江苏\']}} ]}}, {\'$group\':{\'_id\':\'$saledate\',\'sum_quantity\':{\'$sum\':{ \'$multiply\':[\'$price\',\'$quantity\'] }}}}, # 不同之处在于这里构建了一个新字段，注意各个字段是基于上一步的sum_quantity，_id，即上面的$saledate，使用$contat时，用$_id {\'$project\' : { \'sum_quantity\': 1,\'_id\': 1, \'ym\': {\'$concat\': [ {\'$arrayElemAt\': [ {\'$split\': [\'$_id\', \'-\']}, 0 ]},\'-\', {\'$arrayElemAt\': [ {\'$split\': [\'$_id\', \'-\']}, 1 ]}] } }}, {\'$group\':{\'_id\':以上是关于使用highcharts显示mongodb中的数据的主要内容，如果未能解决你的问题，请参考以下文章