Python实战计划学习笔记（16）第三周作业

任务

1. 各城区发帖量Top3大类目和发帖量
2. 各大类目中，成色对应的平均价格情况

程序1

import pymongo
import charts
from datetime import timedelta,date

client = pymongo.MongoClient('localhost',27017)
test = client['test']
item_info = test['sample']

# In[62]:

for i in item_info.find({},{'_id':0,'pub_date':1}).limit(100):
    print(i)


# In[63]:

#for i in item_info.find():
#    frags = i['pub_date'].split('-')
#    if len(frags)==1:
#        date = frags[0]
#    else:
#        date = '{}.{}.{}'.format(frags[0],frags[1],frags[2])
#    item_info.update_one({'_id':i['_id']},{'$set':{'pub_date':date}})


# In[64]:

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


# In[65]:

#for i in get_all_dates('2015.11.12','2016.01.15'):
#    print(i)


# In[66]:

def get_data_within(date1,data2,areas):
    for area in areas:
        area_day_posts = []
        for date in get_all_dates(date1,data2):
            a = list(item_info.find({'pub_date':date,'area':area}))
            #print('#'*20,date,area,len(a),'#'*20)
            each_day_posts = len(a)
            area_day_posts.append(each_day_posts)
        data = {
            'name':area,
            'data':area_day_posts,
            'type':'line'
        }
        yield data


# In[67]:

for i in get_data_within('2015.11.12','2016.01.15',['朝阳','海淀','西城']):
    print(i)


# In[69]:

options = {
    'chart':{'zoomType':'xy'},
    'title': {'text':'发帖量统计'},
    'subtitle': {'text':'发帖量统计'},
    'xAxis' : {'categories':[i for i in get_all_dates('2015.11.12','2016.01.15')]},
    'yAxis' : {'title': { 'text' : '数量'}}
}

series = [ i for i in get_data_within('2015.11.12','2016.01.15',['朝阳','海淀','西城'])]


# In[70]:

charts.plot(series, show='inline', options = options )

程序1运行结果

1.jpg

程序2

import pymongo
import charts
from datetime import timedelta,date


# In[317]:

client = pymongo.MongoClient('localhost',27017)
test = client['test']
item_info = test['sample']


# In[318]:

for i in item_info.find({},{'_id':0,'look':1,'price':1,'cates':1}).limit(100):
    print(i)


# In[319]:

def get_look_price_data(the_cates):
    the_price = 0
    look100 = []
    look99 = []
    look95 = []
    look90 = []
    look80 = []
    look70 = []
    the_price_data = []
    for i in item_info.find({},{'_id':0,'look':1,'price':1,'cates':1}):
        if i['cates'][2]== the_cates:
            if i['price'] == '面议':
                the_price = ''
            elif i['price'][-2:]== ' 元':
                the_price = int(i['price'][:-2])
            elif i['price'][-1]=='元':
                the_price = int(i['price'][:-1])
            else:
                the_price = int(i['price'])
        #print(the_price,i['look'])
        if the_price !='' and i['look'] != '-':
            #print(the_price,i['look'])
            if i['look']=='全新':
                look100.append(the_price)
            elif i['look']=='99成新':
                look99.append(the_price)
            elif i['look']=='95成新':
                look95.append(the_price)
            elif i['look']=='9成新':
                look90.append(the_price)
            elif i['look']=='8成新':
                look80.append(the_price)
            elif i['look']=='7成新及以下':
                look70.append(the_price)
    the_price_data = [sum(look100)/len(look100),sum(look99)/len(look99),sum(look95)/len(look95),sum(look90)/len(look90),sum(look80)/len(look80),sum(look70)/len(look70)]
    print(the_price_data)
    data = {
            'name':the_cates,
            'data':the_price_data,
            'type':'line'
        }
    yield data
    


# In[320]:取二手家电类目的成色价格关系

#get_look_price_data('北京二手家电')


# In[321]:

series = [i for i in get_look_price_data('北京二手台式机/配件')]
print(series)


# In[322]:

options = {
    'chart':{'zoomType':'xy'},
    'title': {'text':'成色与价格的关系'},
    'subtitle': {'text':'统计'},
    'xAxis' : {'categories':['全新','99成新','95成新','9成新','8成新','7成新及以下']},
    'yAxis' : {'title': { 'text' : '平均价格'}}
}
charts.plot(series, show='inline',options = options)

程序2运行结果

2.jpg