def get_info(self, response): item = baidu_item() item['id']=response.meta['id'] item['name']=response.meta['name'] redirect_url_list = response.xpath('''//h3/a/@href''').extract() for url in redirect_url_list:
你在尝试迭代list，list是Python的内置类型，你可能想做的是迭代table，它是一个字典，* 确实 * 有一...
3. 利用filter进行条件筛选 如果需要同时筛选和转换数据,filter配合列表推导式是最佳选择。例如,筛选出偶数并计算其平方: numbers = [1, 2, 3, 4, 5, 6] even_squares = [x**2 for x in filter(lambda x: x % 2 == 0, numbers)] print(even_squares) # 输出: [4, 16, 36] 这段代码中,filt...
filter(is_active=True) index = 0 for task in tasks: newtasklistitem = TaskListItem() newtasklistitem.userprofile = userprofile newtasklistitem.op_task = task newtasklistitem.product = product newtasklistitem.index = index # if index == 0 or experiment.sequential_tasks == False: # new...
make_all_period() free_cells = chain(p[1:-1] for p in paths) free_cells = list(set(free_cells)) shuffle(free_cells) out_cells = [] for cell in free_cells: out_cells.append(cell) times = SpearGenerator.visit_time(cell, paths) cur = [] for per in periods: times = filter(...
使用filter获得具有abc的元素。 >>>lst = ['abc-123','def-456','ghi-789','abc-456']>>>printfilter(lambdax:'abc'inx, lst) ['abc-123','abc-456'] 您还可以使用列表推导。 >>> [xforxinlstif'abc'inx] 顺便说一句,不要将单词list用作变量名,因为它已经用于list类型。
format(doc['_id'])) return [] for collection_name in collections: found_related = es_mlt_search( data_db.name, doc, related_fields, collection_name, items_per_collection) if found_related: related.extend(found_related) # Filter results ret = [] for item_name in related: slug = Slug...
("参数cityid:"+cityid+"不存在于表city_city中,请检查")forcityincitys:self.city_entrance_urls.append(city.href)iflen(self.city_entrance_urls)>0:scrapy_item=ScrapyItem()scrapy_item.scrapy_name=self.nameifscrapy_item.is_existed_scrapy_name()isFalse:scrapy_item.save()self.scrapy_batch_item....