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---
- oeasy Python 0552
- 这是 oeasy 系统化 Python 教程，从基础一步步讲，扎实、完整、不跳步。愿意花时间学，就能真正学会。
- 本教程同步发布在：
- 个人网站： `https://oeasy.org`
- 蓝桥云课： `https://www.lanqiao.cn/courses/3584`
- GitHub： `https://github.com/overmind1980/oeasy-python-tutorial`
- Gitee： `https://gitee.com/overmind1980/oeasypython`
---pip install bs4
import requests
from bs4 import BeautifulSoup
import csv
import json
def getHTMLtext(url):
"""请求获得网页内容"""
try:
r = requests.get(url, timeout=30)
r.raise_for_status()
r.encoding = r.apparent_encoding
print("成功访问")
return r.text
except:
print("访问错误")
return " "
def get_content(html):
"""处理得到有用信息保存数据文件"""
final = [] # 初始化一个列表保存数据
bs = BeautifulSoup(html, "html.parser") # 创建BeautifulSoup对象
body = bs.body
data = body.find('div', {'id': '7d'}) # 找到div标签且id = 7d
# 下面爬取当天的数据
data2 = body.find_all('div', {'class': 'left-div'})
text = data2[2].find('script').string
text = text[text.index('=') + 1:-2] # 移除改var data=将其变为json数据
jd = json.loads(text)
dayone = jd['od']['od2'] # 找到当天的数据
final_day = [] # 存放当天的数据
count = 0
for i in dayone:
temp = []
if count <= 23:
temp.append(i['od21']) # 添加时间
temp.append(i['od22']) # 添加当前时刻温度
temp.append(i['od24']) # 添加当前时刻风力方向
temp.append(i['od25']) # 添加当前时刻风级
temp.append(i['od26']) # 添加当前时刻降水量
temp.append(i['od27']) # 添加当前时刻相对湿度
temp.append(i['od28']) # 添加当前时刻控制质量
# print(temp)
final_day.append(temp)
count = count + 1
# 下面爬取7天的数据
ul = data.find('ul') # 找到所有的ul标签
li = ul.find_all('li') # 找到左右的li标签
i = 0 # 控制爬取的天数
for day in li: # 遍历找到的每一个li
if i < 7 and i > 0:
temp = [] # 临时存放每天的数据
date = day.find('h1').string # 得到日期
date = date[0:date.index('日')] # 取出日期号
temp.append(date)
inf = day.find_all('p') # 找出li下面的p标签,提取第一个p标签的值，即天气
temp.append(inf[0].string)
tem_low = inf[1].find('i').string # 找到最低气温
if inf[1].find('span') is None: # 天气预报可能没有最高气温
tem_high = None
else:
tem_high = inf[1].find('span').string # 找到最高气温
temp.append(tem_low[:-1])
if tem_high[-1] == '℃':
temp.append(tem_high[:-1])
else:
temp.append(tem_high)
wind = inf[2].find_all('span') # 找到风向
for j in wind:
temp.append(j['title'])
wind_scale = inf[2].find('i').string # 找到风级
index1 = wind_scale.index('级')
temp.append(int(wind_scale[index1 - 1:index1]))
final.append(temp)
i = i + 1
return final_day, final
# print(final)
def get_content2(html):
"""处理得到有用信息保存数据文件"""
final = [] # 初始化一个列表保存数据
bs = BeautifulSoup(html, "html.parser") # 创建BeautifulSoup对象
body = bs.body
data = body.find('div', {'id': '15d'}) # 找到div标签且id = 15d
ul = data.find('ul') # 找到所有的ul标签
li = ul.find_all('li') # 找到左右的li标签
final = []
i = 0 # 控制爬取的天数
for day in li: # 遍历找到的每一个li
if i < 8:
temp = [] # 临时存放每天的数据
date = day.find('span', {'class': 'time'}).string # 得到日期
date = date[date.index('（') + 1:-2] # 取出日期号
temp.append(date)
weather = day.find('span', {'class': 'wea'}).string # 找到天气
temp.append(weather)
tem = day.find('span', {'class': 'tem'}).text # 找到温度
temp.append(tem[tem.index('/') + 1:-1]) # 找到最低气温
temp.append(tem[:tem.index('/') - 1]) # 找到最高气温
wind = day.find('span', {'class': 'wind'}).string # 找到风向
if '转' in wind: # 如果有风向变化
temp.append(wind[:wind.index('转')])
temp.append(wind[wind.index('转') + 1:])
else: # 如果没有风向变化，前后风向一致
temp.append(wind)
temp.append(wind)
wind_scale = day.find('span', {'class': 'wind1'}).string # 找到风级
index1 = wind_scale.index('级')
temp.append(int(wind_scale[index1 - 1:index1]))
final.append(temp)
return final
def write_to_csv(file_name, data, day=14):
"""保存为csv文件"""
with open(file_name, 'a', errors='ignore', newline='') as f:
if day == 14:
header = ['日期', '天气', '最低气温', '最高气温', '风向1', '风向2', '风级']
else:
header = ['小时', '温度', '风力方向', '风级', '降水量', '相对湿度', '空气质量']
f_csv = csv.writer(f)
f_csv.writerow(header)
f_csv.writerows(data)
def main():
"""主函数"""
print("Weather test")
# 朝阳
url1 = 'https://www.weather.com.cn/weather/101010300.shtml' # 7天天气中国天气网
url2 = 'https://www.weather.com.cn/weather15d/101010300.shtml' # 8-15天天气中国天气网
html1 = getHTMLtext(url1)
data1, data1_7 = get_content(html1) # 获得1-7天和当天的数据
html2 = getHTMLtext(url2)
data8_14 = get_content2(html2) # 获得8-14天数据
data14 = data1_7 + data8_14
# print(data)
write_to_csv('./weather14.csv', data14, 14) # 保存为csv文件
write_to_csv('./weather1.csv', data1, 1)
if __name__ == '__main__':
main()
# data1_analysis.py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math
from matplotlib.patches import Patch
def preprocess_data(data):
"""预处理数据：填充缺失值并转换数据类型"""
data = data.ffill() # 前向填充缺失值
dtype_mapping = {
'小时': int,
'温度': float,
'风级': float,
'相对湿度': float,
'空气质量': float,
'降水量': float
}
for col, dtype in dtype_mapping.items():
if col in data.columns:
try:
data[col] = data[col].astype(dtype)
except ValueError:
print(f"警告：无法转换列 '{col}' 为 {dtype}，保留为原始类型")
return data
def plot_curve(data, column, title, ylabel, color, avg_color, categories=None):
"""通用曲线绘制函数"""
hour = data['小时']
values = data[column].dropna() # 过滤缺失值
if values.empty:
print(f"警告：{column} 数据为空，无法绘图")
return
avg_value = values.mean()
max_value = values.max()
max_hour = hour[values.idxmax()]
min_value = values.min()
min_hour = hour[values.idxmin()]
plt.figure(figsize=(12, 6))
plt.plot(hour, values, color=color, label=column)
plt.scatter(hour, values, color=color)
plt.axhline(avg_value, color=avg_color, linestyle='--', label=f'平均{column}')
# 标注最大值和最小值
plt.annotate(f'{max_value:.1f}', xy=(max_hour, max_value), xytext=(max_hour + 0.5, max_value + 1),
arrowprops=dict(facecolor='red', shrink=0.05))
plt.annotate(f'{min_value:.1f}', xy=(min_hour, min_value), xytext=(min_hour + 0.5, min_value - 1),
arrowprops=dict(facecolor='blue', shrink=0.05))
if categories:
for h, v in zip(hour, values):
for thresh, color in categories:
if v <= thresh:
plt.bar(h, v, color=color, width=0.7, alpha=0.6)
break
# 添加空气质量图例
legend = [Patch(facecolor=c, label=l) for c, l in zip(
[cat[1] for cat in categories],
['优(≤50)', '良(≤100)', '轻度污染(≤150)', '中度污染(≤200)', '重度污染(≤300)', '严重污染(>300)']
)]
plt.legend(handles=legend, loc='upper right')
plt.xticks(range(0, 24, 2))
plt.title(title)
plt.xlabel('时间/h')
plt.ylabel(ylabel)
plt.grid(True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
return avg_value, max_value, min_value
def tem_curve(data):
"""温度曲线绘制"""
return plot_curve(
data, '温度', '一天温度变化曲线图', '℃', 'red', 'blue'
)
def hum_curve(data):
"""相对湿度曲线绘制"""
return plot_curve(
data, '相对湿度', '一天相对湿度变化曲线图', '%', 'blue', 'red'
)
def air_curve(data):
"""空气质量曲线绘制"""
categories = [
(50, 'lightgreen'), (100, 'wheat'), (150, 'orange'),
(200, 'orangered'), (300, 'darkviolet'), (float('inf'), 'maroon')
]
return plot_curve(
data, '空气质量', '一天空气质量变化曲线图', 'AQI', 'black', 'black', categories
)
def wind_radar(data):
"""风向雷达图"""
wind = data['风力方向']
speed = data['风级']
# 风向角度映射
wind_map = {
'北风': 90, '南风': 270, '西风': 180, '东风': 0,
'东北风': 45, '西北风': 135, '西南风': 225, '东南风': 315
}
wind_angle = wind.map(wind_map).dropna()
if wind_angle.empty:
print("警告：无有效风向数据，无法绘制雷达图")
return
# 计算各方向平均风速
directions = np.linspace(0, 360, 8, endpoint=False) + 22.5 # 8个方向中心角度
avg_speed = []
for deg in directions:
mask = (wind_angle >= deg - 22.5) & (wind_angle < deg + 22.5)
avg = speed[mask].mean()
avg_speed.append(avg if not np.isnan(avg) else 0)
# 绘制雷达图
N = 8
theta = np.deg2rad(directions) # 转换为弧度
theta = np.concatenate((theta, [theta[0]])) # 闭合图形
avg_speed += [avg_speed[0]]
plt.figure(figsize=(8, 8))
ax = plt.subplot(111, polar=True)
ax.plot(theta, avg_speed, 'o-', linewidth=2, color='blue')
ax.fill(theta, avg_speed, alpha=0.2, color='blue')
# 设置方向标签
ax.set_xticks(np.deg2rad(directions))
ax.set_xticklabels(['东北', '北', '西北', '西', '西南', '南', '东南', '东'])
ax.set_ylim(0, max(avg_speed) * 1.2 if avg_speed else 5)
plt.title('风向-风速雷达图', fontsize=15)
plt.tight_layout()
plt.show()
def corr_tem_hum(data):
"""温湿度相关性分析"""
tem = data['温度'].dropna()
hum = data['相对湿度'].dropna()
if len(tem) < 2 or len(hum) < 2:
print("警告：温度或湿度数据不足，无法计算相关性")
return
corr = tem.corr(hum)
plt.figure(figsize=(10, 6))
plt.scatter(tem, hum, color='green', alpha=0.8)
plt.plot(tem, np.poly1d(np.polyfit(tem, hum, 1))(tem), 'r--', linewidth=2)
plt.title(f'温湿度相关性分析 (R={corr:.2f})')
plt.xlabel('温度(℃)')
plt.ylabel('相对湿度(%)')
plt.grid(True)
plt.show()
print(f"相关系数：{corr:.2f}")
def main():
plt.rcParams['font.sans-serif'] = ['WenQuanYi Micro He']
plt.rcParams['axes.unicode_minus'] = False
file_path = r'weather1.csv'
try:
data = pd.read_csv(file_path, encoding='utf-8', header=0)
print("数据读取成功，开始预处理...")
processed_data = preprocess_data(data)
# 检查关键列是否存在
required_columns = ['小时', '温度', '相对湿度', '空气质量', '风力方向', '风级']
for col in required_columns:
if col not in processed_data.columns:
raise ValueError(f"缺失列：{col}")
# 执行绘图函数（确保函数已定义）
print("\n=== 绘制温度曲线 ===")
tem_curve(processed_data)
print("\n=== 绘制湿度曲线 ===")
hum_curve(processed_data)
print("\n=== 绘制空气质量曲线 ===")
air_curve(processed_data)
print("\n=== 绘制风向雷达图 ===")
wind_radar(processed_data)
print("\n=== 温湿度相关性分析 ===")
corr_tem_hum(processed_data)
except Exception as e:
print(f"错误：{str(e)}")
import traceback
traceback.print_exc() # 打印详细错误堆栈
if __name__ == '__main__':
main()
# data14_analysis.py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math
def preprocess_data(data):
"""数据预处理函数"""
# 处理缺失值（前向填充）
data = data.ffill()
# 转换数值列类型
num_cols = ['最低气温', '最高气温', '风级']
for col in num_cols:
data[col] = pd.to_numeric(data[col], errors='coerce')
return data
def tem_curve(data):
"""温度曲线绘制"""
date = list(data['日期'])
tem_low = list(data['最低气温'])
tem_high = list(data['最高气温'])
# 处理可能的首日缺失值（原逻辑从i=0开始，需增加边界检查）
for i in range(len(tem_low)):
if math.isnan(tem_low[i]):
tem_low[i] = tem_low[i - 1] if i > 0 else 0 # 首日缺失设为0或根据实际调整
if math.isnan(tem_high[i]):
tem_high[i] = tem_high[i - 1] if i > 0 else 0
tem_high_ave = sum(tem_high) / len(tem_high)
tem_low_ave = sum(tem_low) / len(tem_low)
tem_max = max(tem_high)
tem_max_date = tem_high.index(tem_max)
tem_min = min(tem_low)
tem_min_date = tem_low.index(tem_min)
x = range(1, len(date) + 1) # 动态适应数据长度
plt.figure(figsize=(10, 6))
plt.plot(x, tem_high, 'r-', label='最高气温')
plt.scatter(x, tem_high, color='red')
plt.plot(x, tem_low, 'b-', label='最低气温')
plt.scatter(x, tem_low, color='blue')
plt.axhline(tem_high_ave, c='black', linestyle='--', label='平均高温')
plt.axhline(tem_low_ave, c='black', linestyle='-.', label='平均低温')
# 标注极值（注意x坐标范围）
plt.text(tem_max_date + 1, tem_max, f'{tem_max}℃', ha='center', va='bottom')
plt.text(tem_min_date + 1, tem_min, f'{tem_min}℃', ha='center', va='top')
plt.xticks(x, date, rotation=45) # 显示日期标签
plt.legend()
plt.title('未来14天高低温变化曲线图')
plt.xlabel('日期')
plt.ylabel('摄氏度/℃')
plt.grid(True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
def change_wind(wind_list):
"""风向转换函数"""
wind_mapping = {
"北风": 90, "南风": 270, "西风": 180, "东风": 0,
"东北风": 45, "西北风": 135, "西南风": 225, "东南风": 315
}
return [wind_mapping.get(wind, np.nan) for wind in wind_list] # 处理未知风向
def wind_radar(data):
"""风向雷达图绘制"""
wind1 = change_wind(data['风向1'])
wind2 = change_wind(data['风向2'])
wind_speed = data['风级'].tolist()
# 合并两个风向数据
all_winds = np.concatenate([wind1, wind2])
all_speeds = np.concatenate([wind_speed, wind_speed])
# 计算各方向平均风速
directions = np.linspace(0, 360, 8, endpoint=False) + 22.5 # 8个方向中心角度
avg_speeds = []
for deg in directions:
mask = (all_winds >= deg - 22.5) & (all_winds < deg + 22.5)
speeds = all_speeds[mask]
avg_speeds.append(speeds.mean() if len(speeds) > 0 else 0)
# 绘制雷达图
N = 8
theta = np.deg2rad(directions)
theta = np.concatenate((theta, [theta[0]])) # 闭合图形
avg_speeds += [avg_speeds[0]]
plt.figure(figsize=(8, 8))
ax = plt.subplot(111, polar=True)
bars = ax.bar(theta, avg_speeds, width=2 * np.pi / N, bottom=0, alpha=0.8)
# 颜色映射（风速越大颜色越深）
norm = plt.Normalize(vmin=min(avg_speeds), vmax=max(avg_speeds))
for bar, speed in zip(bars, avg_speeds):
bar.set_facecolor(plt.cm.viridis(norm(speed)))
ax.set_xticklabels(['东北', '北', '西北', '西', '西南', '南', '东南', '东'])
plt.title('未来14天风级分布雷达图', fontsize=16)
plt.show()
def weather_pie(data):
"""天气类型饼图绘制"""
weather_counts = data['天气'].value_counts(dropna=False) # 统计天气类型频次
# 处理空值（如果有的话）
weather_counts = weather_counts.rename({'NaN': '未知天气'}) if np.nan in weather_counts.index else weather_counts
explode = [0.05] * len(weather_counts) # 突出显示每个部分
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
'#9467bd', '#8c564b', '#e377c2', '#7f7f7f'] # 自定义颜色
plt.figure(figsize=(8, 6))
wedges, labels, autotexts = plt.pie(
weather_counts.values,
explode=explode,
labels=weather_counts.index,
autopct='%1.1f%%',
colors=colors,
textprops={'fontsize': 10},
pctdistance=0.8
)
# 调整字体颜色为白色（深色区域更易读）
for autotext in autotexts:
autotext.set_color('white' if autotext.get_text() > '6.0%' else 'black')
plt.title('未来14天气候类型分布', fontsize=14)
plt.axis('equal') # 保持圆形
plt.show()
def main():
plt.rcParams['font.sans-serif'] = ['WenQuanYi Micro Hei']
plt.rcParams['axes.unicode_minus'] = False
file_path = r'weather14.csv'
try:
data = pd.read_csv(file_path, encoding='utf-8', header=0)
print("数据读取成功，开始预处理...")
processed_data = preprocess_data(data)
# 检查关键列
required_columns = ['日期', '天气', '最低气温', '最高气温', '风向1', '风向2', '风级']
for col in required_columns:
if col not in processed_data.columns:
raise ValueError(f"缺失关键列：{col}")
# 执行绘图函数
print("\n=== 绘制温度曲线 ===")
tem_curve(processed_data)
print("\n=== 绘制风向雷达图 ===")
wind_radar(processed_data)
print("\n=== 绘制天气饼图 ===")
weather_pie(processed_data) # 修正函数调用错误
except FileNotFoundError:
print(f"错误：文件 {file_path} 未找到")
except pd.errors.ParserError:
print("错误：CSV文件解析失败，可能是编码或格式问题")
except Exception as e:
print(f"发生异常：{str(e)}")
import traceback
traceback.print_exc()
if __name__ == '__main__':
main()
import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
import random
from datetime import datetime, timedelta
import logging
import os
import json
# 配置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler("weather_crawler.log"),
logging.StreamHandler()
]
)
logger = logging.getLogger()
# 配置参数
CONFIG = {
"city_code": 54511, # 北京城市代码
"year": 2024, # 爬取年份
"output_file": "北京2024年全天气数据.xlsx", # 输出Excel文件名
"retry_times": 3, # 单条数据最大重试次数
"retry_interval": 10, # 重试间隔（秒）
"delay_range": (2, 5), # 正常请求延迟范围（秒）
"breakpoint_file": "breakpoint.json" # 断点文件
}
def get_days_in_month(year, month):
"""获取指定年月的总天数"""
next_month = month + 1 if month < 12 else 1
next_year = year if month < 12 else year + 1
first_day_next_month = datetime(next_year, next_month, 1)
first_day_current_month = datetime(year, month, 1)
return (first_day_next_month - first_day_current_month).days
def load_breakpoint():
"""加载断点信息"""
if os.path.exists(CONFIG["breakpoint_file"]):
try:
with open(CONFIG["breakpoint_file"], "r", encoding="utf-8") as f:
return json.load(f)
except Exception as e:
logger.warning(f"加载断点文件失败: {e}")
return {"current_month": 1, "current_day": 1, "collected_dates": set()}
def save_breakpoint(month, day, collected_dates):
"""保存断点信息"""
try:
breakpoint_data = {
"current_month": month,
"current_day": day,
"collected_dates": list(collected_dates)
}
with open(CONFIG["breakpoint_file"], "w", encoding="utf-8") as f:
json.dump(breakpoint_data, f)
except Exception as e:
logger.warning(f"保存断点文件失败: {e}")
def get_weather_data_for_date(date_str):
"""获取指定日期的天气数据"""
year, month, day = map(int, date_str.split("-"))
url = f"http://tianqi.2345.com/Pc/GetHistory?areaInfo[areaId]={CONFIG['city_code']}&areaInfo[areaType]=2&date[year]={year}&date[month]={month}"
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
'Referer': f'http://tianqi.2345.com/wea_history/{CONFIG["city_code"]}.html'
}
for retry in range(CONFIG["retry_times"]):
try:
# 随机延迟
time.sleep(random.uniform(CONFIG["delay_range"][0], CONFIG["delay_range"][1]))
response = requests.get(url, headers=headers, timeout=20)
response.raise_for_status()
data = response.json()
if not data.get('data'):
logger.warning(f"{date_str} 数据为空，重试 {retry+1}/{CONFIG['retry_times']}")
continue
soup = BeautifulSoup(data['data'], 'html.parser')
table = soup.find('table')
if not table:
logger.error(f"{date_str} 未找到表格数据，重试 {retry+1}/{CONFIG['retry_times']}")
continue
# 查找对应日期的行
rows = table.find_all('tr')
target_row = None
for row in rows[1:]: # 跳过表头
date_cell = row.find('td')
if date_cell and date_str in date_cell.text:
target_row = row
break
if not target_row:
logger.warning(f"{date_str} 未找到具体数据行，重试 {retry+1}/{CONFIG['retry_times']}")
continue
cells = target_row.find_all('td')
if len(cells) < 5:
logger.error(f"{date_str} 数据格式异常，重试 {retry+1}/{CONFIG['retry_times']}")
continue
# 提取数据
high_temp = cells[1].text.strip()
low_temp = cells[2].text.strip()
weather = cells[3].text.strip()
wind = cells[4].text.strip()
return {
'日期': date_str,
'最高气温': high_temp,
'最低气温': low_temp,
'天气状况': weather,
'风力风向': wind
}
except requests.Timeout:
logger.error(f"{date_str} 请求超时，重试 {retry+1}/{CONFIG['retry_times']}")
except requests.ConnectionError:
logger.error(f"{date_str} 连接错误，重试 {retry+1}/{CONFIG['retry_times']}")
except requests.HTTPError as e:
logger.error(f"{date_str} HTTP错误: {e.response.status_code}，重试 {retry+1}/{CONFIG['retry_times']}")
except Exception as e:
logger.error(f"{date_str} 处理异常: {str(e)}，重试 {retry+1}/{CONFIG['retry_times']}")
if retry < CONFIG["retry_times"] - 1:
time.sleep(CONFIG["retry_interval"]) # 重试间隔
logger.error(f"{date_str} 达到最大重试次数，放弃")
return None
def crawl_weather_data():
"""爬取天气数据主函数"""
breakpoint_data = load_breakpoint()
current_month = breakpoint_data["current_month"]
current_day = breakpoint_data["current_day"]
collected_dates = set(breakpoint_data["collected_dates"])
logger.info(f"从断点开始: 月份={current_month}, 日期={current_day}, 已收集{len(collected_dates)}条数据")
all_data = []
# 遍历剩余月份
for month in range(current_month, 13):
days = get_days_in_month(CONFIG["year"], month)
# 从断点日期开始
start_day = current_day if month == current_month else 1
for day in range(start_day, days + 1):
date_str = f"{CONFIG['year']}-{month:02d}-{day:02d}"
# 跳过已收集的日期
if date_str in collected_dates:
logger.info(f"跳过已收集的日期: {date_str}")
continue
logger.info(f"正在爬取: {date_str}")
weather_data = get_weather_data_for_date(date_str)
if weather_data:
all_data.append(weather_data)
collected_dates.add(date_str)
save_breakpoint(month, day, collected_dates) # 保存断点
# 每爬取10天保存一次，防止意外中断
if len(all_data) % 10 == 0:
logger.info(f"已爬取{len(all_data)}条数据，临时保存...")
if all_data:
pd.DataFrame(all_data).to_excel("临时数据.xlsx", index=False)
# 月份切换时重置日期起点
current_day = 1
# 保存最终数据
if all_data:
df = pd.DataFrame(all_data)
df.to_excel(CONFIG["output_file"], index=False)
logger.info(f"成功保存{len(all_data)}条数据到{CONFIG['output_file']}")
print(f"数据已保存至{CONFIG['output_file']}，共{len(all_data)}条记录")
# 清理断点文件
if os.path.exists(CONFIG["breakpoint_file"]):
os.remove(CONFIG["breakpoint_file"])
else:
logger.error("未获取到任何天气数据")
print("爬取失败，未获取到数据")
if __name__ == "__main__":
print(f"开始爬取{CONFIG['year']}年北京天气数据...")
crawl_weather_data()
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