Python实战实现爬取天气数据并完成可视化分析详解

目录

实现需求:

爬虫代码:

实现需求:

从网上(随便一个网址,我爬的网址会在评论区告诉大家,dddd)获取某一年的历史天气信息,包括每天最高气温、最低气温、天气状况、风向等,完成以下功能:

(1)将获取的数据信息存储到csv格式的文件中,文件命名为”城市名称.csv”,其中每行数据格式为“日期,最高温,最低温,天气,风向”;

(2)在数据中增加“平均温度”一列,其中:平均温度=(最高温+最低温)/2,在同一张图中绘制两个城市一年平均气温走势折线图;

(3)统计两个城市各类天气的天数,并绘制条形图进行对比,假设适合旅游的城市指数由多云天气占比0.3,晴天占比0.4,阴天数占比0.3,试比较两个城市中哪个城市更适合旅游;

(4)统计这两个城市每个月的平均气温,绘制折线图,并通过折线图分析该城市的哪个月最适合旅游;

(5)统计出这两个城市一年中,平均气温在18~25度,风力小于5级的天数,并假设该类天气数越多,城市就越适宜居住,判断哪个城市更适合居住;

爬虫代码: import random import time from spider.data_storage import DataStorage from spider.html_downloader import HtmlDownloader from spider.html_parser import HtmlParser class SpiderMain: def __init__(self): self.html_downloader=HtmlDownloader() self.html_parser=HtmlParser() self.data_storage=DataStorage() def start(self): """ 爬虫启动方法 将获取的url使用下载器进行下载 将html进行解析 数据存取 :return: """ for i in range(1,13): # 采用循环的方式进行依次爬取 time.sleep(random.randint(0, 10)) # 随机睡眠0到40s防止ip被封 url="XXXX" if i<10: url =url+"20210"+str(i)+".html" # 拼接url else: url=url+"2021"+str(i)+".html" html=self.html_downloader.download(url) resultWeather=self.html_parser.parser(html) if i==1: t = ["日期", "最高气温", "最低气温", "天气", "风向"] resultWeather.insert(0,t) self.data_storage.storage(resultWeather) if __name__=="__main__": main=SpiderMain() main.start() import requests as requests class HtmlDownloader: def download(self,url): """ 根据给定的url下载网页 :param url: :return: 下载好的文本 """ headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:101.0) Gecko/20100101 Firefox/101.0"} result = requests.get(url,headers=headers) return result.content.decode('utf-8')

此处大家需要注意,将User-Agent换成自己浏览器访问该网址的,具体如何查看呢,其实很简单,只需大家进入网站后,右键网页,然后点击检查将出现这样的界面:

然后只需再点击网络,再随便点击一个请求,如下图:

就可以进入如下图,然后再复制,图中User-Agent的内容就好了!

继续:

from bs4 import BeautifulSoup class HtmlParser: def parser(self,html): """ 解析给定的html :param html: :return: area set """ weather = [] bs = BeautifulSoup(html, "html.parser") body = bs.body # 获取html中的body部分 div = body.find('div', {'class:', 'tian_three'}) # 获取class为tian_three的<div></div> ul = div.find('ul') # 获取div中的<ul></ul> li = ul.find_all('li') # 获取ul中的所有<li></li> for l in li: tempWeather = [] div1 = l.find_all("div") # 获取当前li中的所有div for i in div1: tempStr = i.string.replace("℃", "") # 将℃进行替换 tempStr = tempStr.replace(" ", "") # 替换空格 tempWeather.append(tempStr) weather.append(tempWeather) return weather import pandas as pd class DataStorage: def storage(self,weather): """ 数据存储 :param weather list :return: """ data = pd.DataFrame(columns=weather[0], data=weather[1:]) # 格式化数据 data.to_csv("C:\\Users\\86183\\Desktop\\成都.csv", index=False, sep=",",mode="a") # 保存到csv文件当中

注意,文件保存路径该成你们自己的哦!

ok,爬取代码就到这,接下来是图形化效果大致如下:

代码如下:

import pandas as pd import matplotlib as mpl import numpy as np import matplotlib.pyplot as plt plt.rcParams["font.sans-serif"] = ["SimHei"] # 设置字体 plt.rcParams["axes.unicode_minus"] = False # 该语句解决图像中的“-”负号的乱码问题 def broken_line_chart(x, y1, y2): # 折线图绘制函数 plt.figure(dpi=500, figsize=(10, 5)) plt.title("泸州-成都每日平均气温折线图") plt.plot(x, y1, color='cyan', label='泸州') plt.plot(x, y2, color='yellow', label='成都') # 获取图的坐标信息 coordinates = plt.gca() # 设置x轴每个刻度的间隔天数 xLocator = mpl.ticker.MultipleLocator(30) coordinates.xaxis.set_major_locator(xLocator) # 将日期旋转30° plt.xticks(rotation=30) plt.xticks(fontsize=8) plt.ylabel("温度(℃)") plt.xlabel("日期") plt.legend() plt.savefig("平均气温走势折线图.webp") # 平均气温折线图 plt.show() plt.close() data_luZhou = pd.read_csv('C:\\Users\\86183\\Desktop\\泸州.csv') data_chengdu = pd.read_csv('C:\\Users\\86183\\Desktop\\成都.csv') # 将列的名称转为列表类型方便添加 columS = data_luZhou.columns.tolist() columY = data_chengdu.columns.tolist() # 将数据转换为列表 data_luZhou=np.array(data_luZhou).tolist() data_chengdu=np.array(data_chengdu).tolist() # 在最开始的位置上添加列的名字 data_luZhou.insert(0, columS) data_chengdu.insert(0, columY) # 添加平均气温列 data_luZhou[0].append("平均气温") data_chengdu[0].append("平均气温") weather_dict_luZhou = {} weather_dict_chengdu = {} for i in range(1, len(data_luZhou)): # 去除日期中的星期 data_luZhou[i][0] = data_luZhou[i][0][0:10] data_chengdu[i][0] = data_chengdu[i][0][0:10] # 获取平均气温 average_luZhou = int((int(data_luZhou[i][1]) + int(data_luZhou[i][2])) / 2) average_chengdu = int((int(data_chengdu[i][1]) + int(data_chengdu[i][2])) / 2) # 将平均气温添加进入列表中 data_luZhou[i].append(average_luZhou) data_chengdu[i].append(average_chengdu) # 将新的数据存入新的csv中 new_data_luZhou = pd.DataFrame(columns=data_luZhou[0], data=data_luZhou[1:]) new_data_chengdu = pd.DataFrame(columns=data_chengdu[0], data=data_chengdu[1:]) new_data_luZhou.to_csv("D:/PythonProject/spider/泸州.csv", index=False, sep=",") new_data_chengdu.to_csv("D:/PythonProject/spider/成都.csv", index=False, sep=",") # 折线图的绘制 y1 = np.array(new_data_luZhou.get("平均气温")).tolist() y2 = np.array(new_data_chengdu.get("平均气温")).tolist() x = np.array(new_data_luZhou.get("日期")).tolist() broken_line_chart(x, y1, y2) # 进行每个月的平均气温求解 new_data_luZhou["日期"] = pd.to_datetime(new_data_luZhou["日期"]) new_data_chengdu["日期"] = pd.to_datetime(new_data_chengdu["日期"]) new_data_luZhou.set_index("日期", inplace=True) new_data_chengdu.set_index("日期", inplace=True) # 按月进行平均气温的求取 month_l = new_data_luZhou.resample('m').mean() month_l = np.array(month_l).tolist() month_c = new_data_chengdu.resample('m').mean() month_c = np.array(month_c).tolist() length = len(month_c) month_average_l = [] month_average_c = [] for i in range(length): month_average_l.append(month_l[i][2]) month_average_c.append(month_c[i][2]) month_list = [str(i) + "月" for i in range(1, 13)] plt.figure(dpi=500, figsize=(10, 5)) plt.title("泸州-成都每月平均折线气温图") plt.plot(month_list, month_average_l, color="cyan",label="泸州", marker='o') plt.plot(month_list, month_average_c, color="blue",label='成都', marker='v') for a, b in zip(month_list, month_average_l): plt.text(a, b + 0.5, '%.2f' % b, horizontalalignment='center', verticalalignment='bottom', fontsize=6) for a, b in zip(month_list, month_average_c): plt.text(a, b - 0.5, '%.2f' % b, horizontalalignment='center', verticalalignment='bottom', fontsize=6) plt.legend() plt.xlabel("月份") plt.ylabel("温度(℃)") plt.savefig("月平均气温折线图.webp") # 月平均气温折线图 plt.show() # # 只获取两列的数据 data_l = pd.read_csv("泸州.csv", usecols=['风向', '平均气温']) data_c = pd.read_csv("成都.csv", usecols=['风向', '平均气温']) data_l = np.array(data_l).tolist() data_c = np.array(data_c).tolist() day_c = 0 day_l = 0 for i in range(len(data_l)): if len(data_l[i][0]) == 5: if int(data_l[i][0][3]) < 5 and 18 <= int(data_l[i][1]) <= 25: day_l += 1 else: if int(data_l[i][0][2]) < 5 and 18 <= int(data_l[i][1]) <= 25: day_l += 1 if len(data_c[i][0]) == 5: if int(data_c[i][0][3]) < 5 and 10 <= int(data_c[i][1]) <= 25: day_c += 1 else: if int(data_c[i][0][2]) < 5 and 18 <= int(data_c[i][1]) <= 25: day_c += 1 plt.figure(dpi=500, figsize=(8, 4)) plt.title("泸州-成都平均气温在18-25且风力<5级的天数") list_name = ['泸州', '成都'] list_days = [day_l, day_c] plt.bar(list_name, list_days, width=0.5) plt.text(0, day_l, '%.0f' % day_l, horizontalalignment='center', verticalalignment='bottom', fontsize=7) plt.text(1, day_c, '%.0f' % day_c, horizontalalignment='center', verticalalignment='bottom', fontsize=7) plt.xlabel("城市") plt.ylabel("天数(d)") plt.savefig("适宜居住柱形图.webp") plt.show() data_l=pd.read_csv("泸州.csv") data_c=pd.read_csv("成都.csv") # 将数据转换为列表 data_l=np.array(data_l).tolist() data_c=np.array(data_c).tolist() # 获取每种天气的天数,采用字典类型进行存储 for i in range(1,365): weather_l = data_l[i][3] weather_c = data_c[i][3] if weather_l in weather_dict_luZhou: weather_dict_luZhou[weather_l] = weather_dict_luZhou.get(weather_l) + 1 else: weather_dict_luZhou[weather_l]=1 if weather_c in weather_dict_chengdu: weather_dict_chengdu[weather_c]=weather_dict_chengdu.get(weather_c)+1 else: weather_dict_chengdu[weather_c]=1 weather_list_luZhou = list(weather_dict_luZhou) weather_list_chengdu = list(weather_dict_chengdu) value_l = [] value_c = [] # 获取所有的天气种类 weather_list = sorted(set(weather_list_luZhou + weather_list_chengdu)) # 获取每种天气的天数,并将其对应的放入列表中,没有的则用0进行替代,方便条形图的绘制。 for i in weather_list: if i in weather_dict_luZhou: value_l.append(weather_dict_luZhou[i]) else: value_l.append(0) if i in weather_dict_chengdu: value_c.append(weather_dict_chengdu[i]) else: value_c.append(0) # 绘制条形图进行对比 plt.figure(dpi=500, figsize=(10, 5)) plt.title("泸州-成都各种天气情况对比") x1 = list(range(len(weather_list))) x = [i + 0.4 for i in x1] plt.bar(x1, value_l, width=0.4, color='red', label='泸州') plt.bar(x, value_c, width=0.4, color='orange', label='成都') for a, b in zip(x1, value_l): plt.text(a, b + 0.4, '%.0f' % b, ha='center', va='bottom', fontsize=7) for a, b in zip(x, value_c): plt.text(a, b + 0.4, '%.0f' % b, ha='center', va='bottom', fontsize=7) plt.xticks(x1, weather_list) plt.ylabel("天数") plt.xlabel("天气") plt.xticks(rotation=270) plt.legend() plt.savefig("泸州成都天气情况对比.webp") plt.show() plt.close()

好的这次就到这儿吧,我们下次见哦!!!

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