def one_day_deal_area():
pipeline = [{
'$match': {
'$and': [{
'pub_date': {
'$gte': '2016.06.25',
'$lte': '2016.06.26'
}
}, {
'saled': '1'
}]
}
}, {
'$group': {
'_id': {
'$slice': ['$area', 1, 1]
},
'counts': {
'$sum': 1
}
}
}, {
'$sort': {
'counts': 1
}
}, {
'$limit': 7
}]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {'name': i['_id'][0], 'y': i['counts']}
yield data
def total_post():
# 条件
pipeline = [
{
'$group': {
'_id': {
'$slice': ['$cates', 0, 1]
},
'counts': {
'$sum': 1
}
}
},
{
'$sort': {
'counts': -1
}
}, # 倒序
{
'$limit': 6
}
]
for i in ItemInfo._get_collection().aggregate(pipeline):
print(i)
data = {
'name': i['_id'][0], # 类名
'y': i['counts'] # 数量
}
yield data
def topx(date1, date2, area, limit):
pipeline = [{
'$match': {
'$and': [{
'publish_time': {
'$gte': date1,
'$lte': date2
}
}, {
'area': area
}]
}
}, {
'$group': {
'_id': "$type",
'counts': {
'$sum': 1
}
}
}, {
'$limit': limit
}, {
'$sort': {
'counts': -1
}
}]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {'name': i['_id'], 'data': [i['counts']], 'type': 'column'}
yield data
def one_day_deal_cate():
pipeline = [{
'$match': {
'$and': [{
'pub_date': {
'$gte': '2015.12.25',
'$lte': '2016.01.11'
}
}, {
'time': 1
}]
}
}, {
'$group': {
'_id': {
'$slice': ['$cates', 2, 1]
},
'counts': {
'$sum': 1
}
}
}, {
'$sort': {
'counts': 1
}
}]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {'name': i['_id'][0], 'y': i['counts']}
yield data
def total_post():
pipeline = [
{'$group':{'_id':{'$slice':['$cates',2,1]},'counts':{'$sum':1}}},
]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {
'name':i['_id'][0],
'y':i['counts']
}
yield data
def total_post():
pipeline = [
{'$group': {'_id': {'$slice': ['$cates', 2, 1]}, 'counts': {'$sum': 1}}},
]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {
'name': i['_id'][0],
'y': i['counts']
}
yield data
def total_post():
pipeline = [
{'$group':{'_id': "$type",'counts':{'$sum':1}}},
{'$sort': {'counts': -1}},
{'$limit': 15}
]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = [
i['_id'], i['counts']
]
yield data
def one_day_deal_area():
pipeline = [
{'$match':{'$and':[{'pub_date':{'$gte':'2015.12.25','$lte':'2016.01.11'}},{'time':1}]}},
{'$group':{'_id':{'$slice':['$area',1]},'counts':{'$sum':1}}},
{'$sort':{'counts':1}}
]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {
'name':i['_id'][0],
'y':i['counts']
}
yield data
def topx(date1, date2, area, limit):
pipeline = [
{'$match': {'$and':[{'publish_time':{'$gte': date1,'$lte': date2}},{'area': area}]}},
{'$group':{'_id': "$type",'counts':{'$sum':1}}},
{'$limit':limit},
{'$sort':{'counts':-1}}
]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {
'name': i['_id'],
'data': [i['counts']],
'type': 'column'
}
yield data
def total_post():
pipeline = [{
'$group': {
'_id': "$type",
'counts': {
'$sum': 1
}
}
}, {
'$sort': {
'counts': -1
}
}, {
'$limit': 15
}]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = [i['_id'], i['counts']]
yield data
def topx(date1, date2, area, limit):
pipeline = [
{
'$match': {
'$and': [{
'pub_date': {
'$gte': date1,
'$lte': date2
}
}, {
'area': {
'$all': area
}
}]
}
}, # 发帖日期区间*地区
{
'$group': {
'_id': {
'$slice': ['$cates', 0, 1]
},
'counts': {
'$sum': 1
}
}
}, # 以商品的类名进行分组 并计总量
{
'$limit': limit
}, # 前几名
{
'$sort': {
'counts': -1
}
} # 倒序排列
]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {
'name': i['_id'][0], # 类目名
'data': [i['counts']], # 发帖量
'type': 'column' # 柱状
}
yield data # 迭代生成数据
def topx(date1, date2, area, limit):
options = {
'chart': {'zoomType': 'xy'},
'title': {'text': '发贴数量最大的类目'},
'subtitle': {'text': '数据图表'},
'yAxis': {'title': {'text': '数量'}}
}
pipeline = [
{'$match': {'$and': [{'pub_date': {'$gte': date1, '$lte': date2}}, {'area': {'$all': area}}]}},
{'$group': {'_id': {'$slice': ['$cates', 2, 1]}, 'counts': {'$sum': 1}}},
{'$limit': limit},
{'$sort': {'counts': -1}}
]
for i in ItemInfo._get_collection().aggregate(pipeline):
data = {
'name': i['_id'][0],
'data': [i['counts']],
'type': 'column'
}
yield data
