Python中怎么爬取天气数据

这期内容当中小编将会给大家带来有关Python中怎么爬取天气数据，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。

使用pygal绘图，使用该模块前需先安装pip install pygal，然后导入import pygal

bar = pygal.Line() # 创建折线图bar.add('最低气温', lows) #添加两线的数据序列bar.add('最高气温', highs) #注意lows和highs是int型的列表bar.x_labels = daytimesbar.x_labels_major = daytimes[::30]bar.x_label_rotation = 45bar.title = cityname+'未来七天气温走向图' #设置图形标题bar.x_title = '日期' #x轴标题bar.y_title = '气温(摄氏度)' # y轴标题bar.legend_at_bottom = Truebar.show_x_guides = Falsebar.show_y_guides = Truebar.render_to_file('temperate1.svg') # 将图像保存为SVG文件，可通过浏览器

完整代码

import csvimport sysimport urllib.requestfrom bs4 import BeautifulSoup # 解析页面模块import pygalimport cityinfocityname = input("请输入你想要查询天气的城市:")if cityname in cityinfo.city:citycode = cityinfo.city[cityname]else:sys.exit()url = '非常抱歉，网页无法访问' + citycode + '.shtml'header = ("User-Agent","Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36") # 设置头部信息http_handler = urllib.request.HTTPHandler()opener = urllib.request.build_opener(http_handler) # 修改头部信息opener.addheaders = [header]request = urllib.request.Request(url) # 制作请求response = opener.open(request) # 得到应答包html = response.read() # 读取应答包html = html.decode('utf-8') # 设置编码，否则会乱码# 根据得到的页面信息进行初步筛选过滤final = [] # 初始化一个列表保存数据bs = BeautifulSoup(html, "html.parser") # 创建BeautifulSoup对象body = bs.bodydata = body.find('div', {'id': '7d'})print(type(data))ul = data.find('ul')li = ul.find_all('li')# 爬取自己需要的数据i = 0 # 控制爬取的天数lows = [] # 保存低温highs = [] # 保存高温daytimes = [] # 保存日期weathers = [] # 保存天气for day in li: # 便利找到的每一个liif i < 7:temp = [] # 临时存放每天的数据date = day.find('h2').string # 得到日期#print(date)temp.append(date)daytimes.append(date)inf = day.find_all('p') # 遍历li下面的p标签 有多个p需要使用find_all 而不是find#print(inf[0].string) # 提取第一个p标签的值，即天气temp.append(inf[0].string)weathers.append(inf[0].string)temlow = inf[1].find('i').string # 最低气温if inf[1].find('span') is None: # 天气预报可能没有最高气温temhigh = Nonetemperate = temlowelse:temhigh = inf[1].find('span').string # 最高气温temhigh = temhigh.replace('℃', '')temperate = temhigh + '/' + temlow# temp.append(temhigh)# temp.append(temlow)lowStr = ""lowStr = lowStr.join(temlow.string)lows.append(int(lowStr[:-1])) # 以上三行将低温NavigableString转成int类型并存入低温列表if temhigh is None:highs.append(int(lowStr[:-1]))highStr = ""highStr = highStr.join(temhigh)highs.append(int(highStr)) # 以上三行将高温NavigableString转成int类型并存入高温列表temp.append(temperate)final.append(temp)i = i + 1# 将最终的获取的天气写入csv文件with open('weather.csv', 'a', errors='ignore', newline='') as f:f_csv = csv.writer(f)f_csv.writerows([cityname])f_csv.writerows(final)# 绘图bar = pygal.Line() # 创建折线图bar.add('最低气温', lows)bar.add('最高气温', highs)bar.x_labels = daytimesbar.x_labels_major = daytimes[::30]# bar.show_minor_x_labels = False # 不显示X轴最小刻度bar.x_label_rotation = 45bar.title = cityname+'未来七天气温走向图'bar.x_title = '日期'bar.y_title = '气温(摄氏度)'bar.legend_at_bottom = Truebar.show_x_guides = Falsebar.show_y_guides = Truebar.render_to_file('temperate.svg')

Python爬取天气数据实例扩展：

import requestsfrom bs4 import BeautifulSoupfrom pyecharts import Bar ALL_DATA = []def send_parse_urls(start_urls):headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36"}for start_url in start_urls:response = requests.get(start_url,headers=headers)# 编码问题的解决response = response.text.encode("raw_unicode_escape").decode("utf-8")soup = BeautifulSoup(response,"html5lib") #lxml解析器：性能比较好，html5lib：适合页面结构比较混乱的div_tatall = soup.find("div",class_="conMidtab") #find() 找符合要求的第一个元素tables = div_tatall.find_all("table") #find_all() 找到符合要求的所有元素的列表for table in tables:trs = table.find_all("tr")info_trs = trs[2:]for index,info_tr in enumerate(info_trs): # 枚举函数，可以获得索引# print(index,info_tr)# print("="*30)city_td = info_tr.find_all("td")[0]temp_td = info_tr.find_all("td")[6]# if的判断的index的特殊情况应该在一般情况的后面，把之前的数据覆盖if index==0:city_td = info_tr.find_all("td")[1]temp_td = info_tr.find_all("td")[7]city=list(city_td.stripped_strings)[0]temp=list(temp_td.stripped_strings)[0]ALL_DATA.append({"city":city,"temp":temp})return ALL_DATA def get_start_urls():start_urls = ["http://www.weather.com.cn/textFC/hb.shtml","http://www.weather.com.cn/textFC/db.shtml","http://www.weather.com.cn/textFC/hd.shtml","http://www.weather.com.cn/textFC/hz.shtml","http://www.weather.com.cn/textFC/hn.shtml","http://www.weather.com.cn/textFC/xb.shtml","http://www.weather.com.cn/textFC/xn.shtml","http://www.weather.com.cn/textFC/gat.shtml",]return start_urls def main():"""主程序逻辑展示全国实时温度最低的十个城市气温排行榜的柱状图"""# 1 获取所有起始urlstart_urls = get_start_urls()# 2 发送请求获取响应、解析页面data = send_parse_urls(start_urls)# print(data)# 4 数据可视化#1排序data.sort(key=lambda data:int(data["temp"]))#2切片，选择出温度最低的十个城市和温度值show_data = data[:10]#3分出城市和温度city = list(map(lambda data:data["city"],show_data))temp = list(map(lambda data:int(data["temp"]),show_data))#4创建柱状图、生成目标图chart = Bar("中国最低气温排行榜") #需要安装pyechart模块chart.add("",city,temp)chart.render("tempture.html") if __name__ == '__main__':main()

上述就是小编为大家分享的Python中怎么爬取天气数据了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注行业资讯频道。