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114 lines
4.0 KiB
114 lines
4.0 KiB
import requests
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from lxml import etree
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import csv
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from collections import Counter
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import pandas as pd
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import matplotlib.pyplot as plt
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def getweather(url):
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weather_info = []
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high_temps = [] # 存储最高气温
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low_temps = [] # 存储最低气温
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weather_conditions = []
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headers = {
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'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:109.0) Gecko/20100101 Firefox/114.0'
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}
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response = requests.get(url, headers=headers)
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html_resp = etree.HTML(response.text)
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resp_list = html_resp.xpath("//ul[@class='thrui']/li")
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for li in resp_list:
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day_weather_info = {}
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day_weather_info['date_time'] = li.xpath('./div[1]/text()')[0].split(' ')[0]
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high = li.xpath('./div[2]/text()')[0]
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high_temp = float(high[:high.find('C')])
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day_weather_info['high'] = high_temp
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high_temps.append(high_temp)
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low = li.xpath('./div[3]/text()')[0]
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low_temp = float(low[:low.find('C')])
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day_weather_info['low'] = low_temp
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low_temps.append(low_temp)
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day_weather = li.xpath('./div[4]/text()')[0]
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day_weather_info['weather'] = day_weather
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weather_conditions.append(day_weather)
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weather_info.append(day_weather_info)
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return weather_info, high_temps, low_temps, weather_conditions
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weathers = []
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high_temps = []
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low_temps = []
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weather_conditions = []
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for month in range(1, 13):
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weather_time = '2022' + ('0' + str(month) if month < 10 else str(month))
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url = f'https://lishi.tianqi.com/guilin/{weather_time}.html'
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weather, highs, lows, conditions = getweather(url)
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weathers.append(weather)
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high_temps.extend(highs)
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low_temps.extend(lows)
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weather_conditions.extend(conditions)
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with open('weather.csv', 'w', newline='') as csvfile:
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writer = csv.writer(csvfile)
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writer.writerow(['日期', '最高气温', '最低气温', '天气'])
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writer.writerows(
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[list(day_weather_dict.values()) for month_weather in weathers for day_weather_dict in month_weather])
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weather_counts = Counter(weather_conditions)
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writer.writerow([])
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writer.writerow(['天气状况', '出现次数'])
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writer.writerows(weather_counts.items())
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def plot_weather_counts(weather_counts):
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df = pd.DataFrame(list(weather_counts.items()), columns=['天气状况', '出现次数'])
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plt.rcParams['font.sans-serif'] = ['SimHei']
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bar_width = 0.5 # 设置柱子的宽度
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bar_spacing = 0.3 # 设置柱子之间的间隔
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x_positions = range(len(df['天气状况'])) # 设置x轴位置
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plt.bar(x_positions, df['出现次数'], width=bar_width)
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plt.xlabel('天气状况')
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plt.ylabel('出现次数')
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plt.title('天气状况统计')
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plt.xticks(x_positions, df['天气状况'], rotation=45, ha='right') # 设置x轴标签
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def plot_average_temperatures(high_temps, low_temps):
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avg_high_temps = [sum(high_temps[i:i + 30]) / len(high_temps[i:i + 30]) for i in range(0, len(high_temps), 30)]
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avg_low_temps = [sum(low_temps[i:i + 30]) / len(low_temps[i:i + 30]) for i in range(0, len(low_temps), 30)]
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plt.rcParams['font.sans-serif'] = ['SimHei']
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months = range(1, 13)
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plt.plot(months[:len(avg_high_temps)], avg_high_temps, marker='o', label='平均最高气温')
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plt.plot(months[:len(avg_low_temps)], avg_low_temps, marker='o', label='平均最低气温')
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plt.xlabel('月份')
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plt.ylabel('平均气温')
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plt.title('每月平均气温')
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plt.xticks(months)
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plt.legend()
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# 创建第一个图表，并绘制天气情况柱状图
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fig1, ax1 = plt.subplots()
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plot_weather_counts(weather_counts)
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ax1.set_ylabel('出现次数')
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ax1.set_title('天气状况统计')
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# 创建第二个图表，并绘制平均气温折线图
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fig2, ax2 = plt.subplots()
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plot_average_temperatures(high_temps, low_temps)
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ax2.set_xlabel('月份')
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ax2.set_ylabel('平均气温')
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ax2.set_title('每月平均气温')
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plt.show()
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