python将天气预报可视化

# -*- coding: UTF-8 -*-
"""
# @Time: 2022/1/4 11:02
# @Author: 远方的星
# @CSDN: https://blog.csdn.net/qq_44921056
"""
import chardet
import requests
from lxml import etree
from fake_useragent import UserAgent
import pandas as pd
from matplotlib import pyplot as plt

# 随机产生请求头
ua = UserAgent(verify_ssl=False, path='D:/Pycharm/fake_useragent.json')

# 随机切换请求头
def random_ua():
 headers = {
  "user-agent": ua.random
 }
 return headers

# 解析页面
def res_text(url):
 res = requests.get(url=url, headers=random_ua())
 res.encoding = chardet.detect(res.content)['encoding']
 response = res.text
 html = etree.HTML(response)
 return html

# 获得未来七天及八到十五天的页面链接
def get_url(url):
 html = res_text(url)
 url_7 = 'http://www.weather.com.cn/' + html.xpath('//*[@id="someDayNav"]/li[2]/a/@href')[0]
 url_8_15 = 'http://www.weather.com.cn/' + html.xpath('//*[@id="someDayNav"]/li[3]/a/@href')[0]
 # print(url_7)
 # print(url_8_15)
 return url_7, url_8_15

# 获取未来七天的天气情况
def get_data_7(url):
 html = res_text(url)
 list_s = html.xpath('//*[@id="7d"]/ul/li')  # 获取天气数据列表
 Date, Weather, Low, High = [], [], [], []
 for i in range(len(list_s)):
  list_date = list_s[i].xpath('./h1/text()')[0]  # 获取日期，如：4日（明天）
  # print(list_data)
  list_weather = list_s[i].xpath('./p[1]/@title')[0]  # 获取天气情况，如：小雨转雨夹雪
  # print(list_weather)
  tem_low = list_s[i].xpath('./p[2]/i/text()')  # 获取最低气温
  tem_high = list_s[i].xpath('./p[2]/span/text()')  # 获取最高气温
  if tem_high == []:  # 遇到夜晚情况，筛掉当天的最高气温
tem_high = tem_low  # 无最高气温时，使最高气温等于最低气温
  tem_low = int(tem_low[0].replace('℃', '')) # 将气温数据处理
  tem_high = int(tem_high[0].replace('℃', ''))
  # print(type(tem_high))
  Date.append(list_date), Weather.append(list_weather), Low.append(tem_low), High.append(tem_high)
 excel = pd.DataFrame()  # 定义一个二维列表
 excel['日期'] = Date
 excel['天气'] = Weather
 excel['最低气温'] = Low
 excel['最高气温'] = High
 # print(excel)
 return excel

def get_data_8_15(url):
 html = res_text(url)
 list_s = html.xpath('//*[@id="15d"]/ul/li')
 Date, Weather, Low, High = [], [], [], []
 for i in range(len(list_s)):
  # data_s[0]是日期，如：周二（11日），data_s[1]是天气情况，如：阴转晴，data_s[2]是最低温度，如：/-3℃
  data_s = list_s[i].xpath('./span/text()')
  # print(data_s)
  date = modify_str(data_s[0])  # 获取日期情况
  weather = data_s[1]
  low = int(data_s[2].replace('/', '').replace('℃', ''))
  high = int(list_s[i].xpath('./span/em/text()')[0].replace('℃', ''))
  # print(date, weather, low, high)
  Date.append(date), Weather.append(weather), Low.append(low), High.append(high)
 # print(Date, Weather, Low, High)
 excel = pd.DataFrame()  # 定义一个二维列表
 excel['日期'] = Date
 excel['天气'] = Weather
 excel['最低气温'] = Low
 excel['最高气温'] = High
 # print(excel)
 return excel

# 将8-15天日期格式改成与未来7天一致
def modify_str(date):
 date_1 = date.split('（')
 date_2 = date_1[1].replace('）', '')
 date_result = date_2 + '（' + date_1[0] + '）'
 return date_result

# 实现数据可视化
def get_image(date, weather, high, low):
 # 用来正常显示中文标签
 plt.rcParams['font.sans-serif'] = ['SimHei']
 # 用来正常显示负号
 plt.rcParams['axes.unicode_minus'] = False
 # 根据数据绘制图形
 fig = plt.figure(dpi=128, figsize=(10, 6))
 ax = fig.add_subplot(111)
 plt.plot(date, high, c='red', alpha=0.5, marker='*')
 plt.plot(date, low, c='blue', alpha=0.5, marker='o')
 # 给图表中两条折线中间的部分上色
 plt.fill_between(date, high, low, facecolor='blue', alpha=0.2)
 # 设置图表格式
 plt.title('邳州近15天天气预报', fontsize=24)
 plt.xlabel('日期', fontsize=12)
 # 绘制斜的标签，以免重叠
 fig.autofmt_xdate()
 plt.ylabel('气温', fontsize=12)
 # 参数刻度线设置
 plt.tick_params(axis='both', which='major', labelsize=10)
 # 修改刻度
 plt.xticks(date[::1])
 # 对点进行标注，在最高气温点处标注当天的天气情况
 for i in range(15):
  ax.annotate(weather[i], xy=(date[i], high[i]))
 # 显示图片
 plt.show()

def main():
 base_url = 'http://www.weather.com.cn/weather1d/101190805.shtml'
 url_7, url_8_15 = get_url(base_url)
 data_1 = get_data_7(url_7)
 data_2 = get_data_8_15(url_8_15)
 data = pd.concat([data_1, data_2], axis=0, ignore_index=True)  # ignore_index=True实现两张表拼接，不保留原索引
 get_image(data['日期'], data['天气'], data['最高气温'], data['最低气温'])

if __name__ == '__main__':
 main()