说明
有时候我们想查询一个地方的历史气温用来预测今年的气温，自己去互联网查询又太麻烦，闲来无事写了个查询代码
- 本代码为本人原创，转载请注明出处。
- 仅限论坛学习交流使用，请勿滥用，谢谢！
- 代码并未做全面测试，有问题请在评论区留言。
源代码如下
第一步，运行一次下面的代码，用于获取地区对应的代码，会自动保存为 “city_data.csv” 文件，用于第二步的文件调用
import os
import csv
import requests
from lxml import etree
# 目标主网址
main_url = "https://lishi.tianqi.com/"
# 设置请求头
headers = {
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36 Edg/127.0.0.0',
}
if not os.path.exists('city_data.csv'):
# 获取不同城市对应的网址
try:
response = requests.get(url=main_url, headers=headers)
html = etree.HTML(response.text)
city_name_list = html.xpath("//td/ul/li/a/text()")
city_url_list = html.xpath("//td/ul/li/a/@href")
with open('city_data.csv', mode='w', encoding='utf-8-sig', newline='') as f:
writer = csv.writer(f)
writer.writerow(['城市', '代码'])
for n, u in zip(city_name_list, city_url_list):
writer.writerow([n, u.split('/')[0]])
print(n, u.split('/')[0])
except Exception as e:
print(e)
else:
print('城市数据已存在')
第二步：输入地区名和日期，开始查询
代码运行结束之后，会在同目录下生成一个html文件，在浏览器打开即可看见折线图
import requests
import csv
import re
import os
from lxml import etree
import pyecharts.options as opts
from pyecharts.charts import Line
# 设置请求头
headers= {
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36 Edg/127.0.0.0',
}
def input_data():
"""输入地点，时间"""
city = input("请输入查询城市：")
month = input("请输入查询月份（格式：202307）：")
return city, month
def get_date_url(city, month):
"""获取当月日期地址"""
if os.path.exists('city_data.csv'):
with open('city_data.csv', mode='r', encoding='utf-8') as f:
reader = csv.reader(f)
for row in reader:
if city == row[0]:
city_id = row[1]
month_url = f"https://lishi.tianqi.com/{city_id}/{month}.html"
return month_url
def extract_numbers(string):
"""提取字符串中的数字"""
numbers = re.findall(r'-?\d+', string)[0]
return float(numbers)
def spider_weather(date_url, city, month):
try:
response = requests.get(url=date_url, headers=headers)
html = etree.HTML(response.text)
tree = html.xpath('/html/body/div[7]/div[1]/div[4]/ul/li')
date_name_list = []
high_temperatures = []
low_temperatures = []
weathers = []
for i in tree:
date = i.xpath('./div[1]/text()')[0].split(' ')[0]
high_temperature = extract_numbers(i.xpath('./div[2]/text()')[0])
low_temperature = extract_numbers(i.xpath('./div[3]/text()')[0])
weather = i.xpath('./div[4]/text()')[0]
wind = i.xpath('./div[5]/text()')[0]
print(date, high_temperature, low_temperature, weather, wind)
date_name_list.append(date)
high_temperatures.append(high_temperature)
low_temperatures.append(low_temperature)
weathers.append(weather)
line = (
Line(init_opts=opts.InitOpts(width="1200px", height="600px",page_title='月份气温折线图'))
.add_xaxis(xaxis_data=date_name_list)
.add_yaxis(
series_name="最高气温",
y_axis=high_temperatures,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.add_yaxis(
series_name="最低气温",
y_axis=low_temperatures,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min", name="最小值")]
),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="average", name="平均值"),
]
),
)
.set_global_opts(
# 设置主副标题
title_opts=opts.TitleOpts(title=f"{city}地区{month[0:4]}年{month[4:]}月气温走势折线图", subtitle=f"{city}"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True, feature=opts.ToolBoxFeatureOpts(save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(pixel_ratio=3, type_='jpg', background_color='#fff'))),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False, name='日期', min_=0, max_=len(date_name_list), axisline_opts=opts.AxisLineOpts(symbol=['none', 'arrow'])),
datazoom_opts=opts.AxisLineOpts(),
)
)
line.render(f"{city}地区{month[0:4]}年{month[4:]}月气温走势折线图.html")
except Exception as e:
print(e)
def main():
city, month = input_data()
month_url = get_date_url(city, month)
spider_weather(month_url, city, month)
if __name__ == '__main__':
main()
效果图如下
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发表于 2024-8-17 18:25
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发表于 2024-8-18 09:02
