前几篇文章，我们对慕课网的课程进行了爬取，本文就对数据进行统计和可视化，让这些数据更直观的展现出来。

Flask 是基于Python的非常流行的框架之一，主要用于web开发，适合开发中小型项目，易于扩展。Flask的官方网站是 http://flask.pocoo.org/ 。

Echarts （http://echarts.baidu.com/ ）是百度出品的，基于Canvas的，纯Javascript 的图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。创新的拖拽重计算、数据视图、值域漫游等特性大大增强了用户体验，赋予了用户对数据进行挖掘、整合的能力。

前两篇文章中，简单用scrapy写了一个小demo，本篇文章主要目标是完整用scrapy爬取，慕课网所有免费的课程、标题、图片、地址、学习人数、难度、方向、分类、时长、评分、评论数等。

其实，很简单，在上一次的代码中修改调整一下就好。

# -*- coding: utf-8 -*-

import scrapy
import json
from urllib import parse as urlparse
from scrapyDemo.ImoocCourseItem import ImoocCourseItem

# 慕课网爬取
class ImoocSpider(scrapy.Spider):
    # spider的名字定义了Scrapy如何定位(并初始化)spider，所以其必须是唯一的
    name = "imooc"

    # URL列表
    start_urls = ['http://www.imooc.com/course/list']
    #  域名不在列表中的URL不会被爬取。
    allowed_domains = ['www.imooc.com']

    def parse(self, response):
        # 课程类型
        types = response.css('div.course-content .course-nav-row')[2].css(
            '.bd ul li a')
        for key in range(len(types)):
            if key == 0:
                continue
            course_type = types[key].css('::text').extract_first()
            # 类型的url
            type_url = types[key].css('::attr(href)').extract_first()
            # print (item)
            yield scrapy.Request(
                url=urlparse.urljoin(response.url, type_url),
                callback=self.parse_by_type,
                meta={
                    'course_type': course_type
                })

    # 按课程类型爬取
    def parse_by_type(self, response):
        itemBase = response.meta
        item = ImoocCourseItem()
        item['course_type'] = itemBase['course_type']
        # print(item)
        learn_nodes = response.css('a.course-card')
        # 遍历该页上所有课程列表
        for learn_node in learn_nodes:
            course_url = learn_node.css("::attr(href)").extract_first()
            # 拼接课程详情页地址
            course_url = urlparse.urljoin(response.url, course_url)
            # 课程地址
            item['course_url'] = course_url
            # 课程图片
            item['image'] = learn_node.css(
                "img.course-banner::attr(src)").extract_first()
            # 分类
            cate = learn_node.css("div.course-label label::text").extract()
            item['cate'] = ','.join(cate)
            # 进入课程详情页面
            yield scrapy.Request(
                url=course_url, callback=self.parse_learn, meta=item)

        # 下一页地址
        next_page_url = response.css(
            u'div.page a:contains("下一页")::attr(href)').extract_first()
        if next_page_url:
            yield scrapy.Request(
                url=urlparse.urljoin(response.url, next_page_url),
                callback=self.parse_by_type,
                meta={
                    'course_type': item['course_type']
                })

    # 课程详情
    def parse_learn(self, response):
        item = response.meta
        # 课程标题
        item['title'] = response.xpath(
            '//h2[@class="l"]/text()').extract_first()
        # 课程简介
        item['brief'] = response.xpath(
            '//div[@class="course-brief"]/p/text()').extract_first()

        staticItem = response.css(
            'div#main div.statics div.static-item span.meta-value::text'
        ).extract()
        # 难度级别
        item['difficulty_level'] = staticItem[0]
        # 课程时长
        item['duration'] = staticItem[1]
        # 综合评分
        item['overall_rating'] = staticItem[2]
        # 评论数
        item['evaluation_number'] = response.css(
            'a.person-num span.person-num::text').extract_first().replace(
                '人评价', '')
        # 教师id
        item['teacher_id'] = response.css(
            'div.teacher-info a img::attr(data-userid)').extract_first()
        # 学习人数
        ids = response.url.split('/')[-1]
        yield scrapy.Request(
            url=urlparse.urljoin(response.url,
                                 '/course/AjaxCourseMembers?ids=' + ids),
            callback=self.parse_learn_num,
            meta=item)

    # 爬取学习人数
    def parse_learn_num(self, response):
        item = response.meta
        data = json.loads(response.body_as_unicode())
        # 学习人数
        item['learn_num'] = data['data'][0]['numbers']
        # print (item)
        yield item

# -*- coding: utf-8 -*-

# Define here the models for your scraped items
#
# See documentation in:
# http://doc.scrapy.org/en/latest/topics/items.html

import scrapy

class ImoocCourseItem(scrapy.Item):
    # define the fields for your item here like:
    title = scrapy.Field()    
    image = scrapy.Field()    
    brief = scrapy.Field()
    cate = scrapy.Field()
    course_type = scrapy.Field()
    course_url = scrapy.Field()
    learn_num = scrapy.Field()
    difficulty_level = scrapy.Field()
    duration = scrapy.Field()
    overall_rating = scrapy.Field()
    evaluation_number = scrapy.Field()
    teacher_id = scrapy.Field()
    pass