我的成果

我的代码

代码的部分其实不难，就是挺繁琐的，东拼拼西凑凑。
贴一下我的views.py吧：

from django.shortcuts import render
# 调用modules中的类
from final_45.models import ItemInfo
# 分页工具 Paginator
from django.core.paginator import Paginator

cate_list = [
'北京二手美容/保健', '北京二手平板电脑', '北京二手文体/户外/乐器', 
'北京二手家电', '北京二手图书/音像/软件', '北京二手母婴/儿童用品', 
'北京二手设备', '北京二手数码产品','北京二手手机', '北京二手办公用品/设备',
 '北京二手服装/鞋帽/箱包', '北京二手台式机/配件', '北京其他二手物品',
 '北京二手笔记本', '北京二手家具'
]

# *----------------------发帖总量柱状图----------------------*
def every_cate_post(cate_list):
    post_counts = []
    for i in cate_list:
        data = ItemInfo._get_collection().find({'cates': i}).count()
        post_counts.append(data)
    data = {
        'name': '各个类别',
        'type': 'column',
        'data': post_counts
    }
    return data

series_ch1 = [every_cate_post(cate_list)]

# *----------------------一天内交易物品分布：饼状图----------------------*
# 按种类分布
def oneday_cates():
    pipeline = [
       {'$match':{'time':1}},
       {'$group':{'_id':{'$slice':['$cates',2,1]},'counts':{'$sum':1}}},
       {'$sort':{'counts':-1}}
    ]
    for i in ItemInfo._get_collection().aggregate(pipeline):
        data = [i['_id'][0][4:], i['counts']]
        yield data

series_ch2 = [i for i in oneday_cates()]

# 按地区分布
def oneday_areas():
    pipeline = [
       {'$match':{'time':1}},
       {'$group':{'_id':{'$slice':['$area',0,1]},'counts':{'$sum':1}}},
       {'$sort':{'counts':-1}}
    ]
    for i in ItemInfo._get_collection().aggregate(pipeline):
        data = [i['_id'][0], i['counts']]
        yield data

series_ch3 = [i for i in oneday_areas()]

# *----------------------地区发帖量前三----------------------*
def hot_in_areas(area, limit):
    pipeline = [
        {'$match': {'area': area}},
        {'$group': {'_id': {'$slice': ['$cates', 2, 1]}, 'counts': {'$sum': 1}}},
        {'$sort': {'counts': -1}},
        {'$limit': limit}
    ]
    for i in ItemInfo._get_collection().aggregate(pipeline):
        data = {
            'name': i['_id'][0],
            'type': 'column',
            'data': [i['counts']]
        }
        yield data

series_LD = [i for i in hot_in_areas('朝阳', 3)]
series_PK = [i for i in hot_in_areas('海淀', 3)]
series_PR = [i for i in hot_in_areas('丰台', 3)]

# *----------------------文字详情页----------------------*
def index(request):
    limit = 10
    iteminfo = ItemInfo.objects  # 提取数据
    paginator = Paginator(iteminfo, limit)  # 把提取的数据分页，每页10个
    page = request.GET.get('page', 1)  # 从request提取出页码
    loaded = paginator.page(page)
    content = {
        'ItemInfo': loaded,
        'counts': iteminfo.count(),
    }
    return render(request, 'index3_p1.html', content)

# *----------------------图表页----------------------*
def charts(request):
    context = {
        'chart_LD': series_LD,
        'chart_PK': series_PK,
        'chart_PR': series_PR,
        'chart_01': series_ch1,
        'chart_02': series_ch2,
        'chart_03': series_ch3,
    }
    return render(request, 'index3_p2.html', context)

# print(reuqest) == <WSGIRequest: GET'/index/'>
# print(request.GET) == <QueryDict:{}>

值得一提的是：如何让柱状图的每一个柱子不同颜色？如果你只想（能）通过简单的设置“参数”来实现的话，可以这样：阅读'colorByPoint: Boolean'。注：在修改JS可行，而在jupyter notebook里面则不行。