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Python爬取城市天气数据,并作数据可视化_使用爬虫爬取自己家乡近几年的天气状况

作者：繁依Fanyi0 | 2024-02-29 09:31:34

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使用爬虫爬取自己家乡近几年的天气状况

1.爬取广惠河深2022-2024年的天气数据 

  1. import requests     # 发送请求要用的模块 需要额外安装的
  2. import parsel
  3. import csv
  4.  
  5. f = open('广-惠-河-深天气.csv', mode='a', encoding='utf-8', newline='')
  6. csv_writer = csv.writer(f)
  7. csv_writer.writerow(['日期', '最高温度', '最低温度', '天气', '风向', '城市'])
  8. city_list = [72049, 59287,59293,59493]
  9. for city in city_list:
  10.     city_name = ''
  11.     if city == 72049:
  12.         city_name = '惠州'
  13.     elif city == 59287:
  14.         city_name = '广州'
  15.     elif city == 59293:
  16.         city_name = '河源'
  17.     elif city == 59493:
  18.         city_name = '深圳'
  19.     for year in range(2022, 2024):
  20.         for month in range(1, 13):
  21.             url = f'https://tianqi.2345.com/Pc/GetHistory?areaInfo%5BareaId%5D={city}&areaInfo%5BareaType%5D=2&date%5Byear%5D={year}&date%5Bmonth%5D={month}'
  22.             # 1. 发送请求
  23.             response = requests.get(url=url)
  24.             # 2. 获取数据
  25.             html_data = response.json()['data']
  26.             # 3. 解析数据
  27.             select = parsel.Selector(html_data)
  28.             trs = select.css('.history-table tr')   # 拿到31个tr
  29.             for tr in trs[1:]:                      # 第一个表头不要
  30.                 tds = tr.css('td::text').getall()   # 针对每个tr进行提取 取出所有的td里面的内容
  31.                 tds.append(city_name)               # 把城市追加到列表里面
  32.                 print(tds)
  33.                 # 4. 保存数据
  34.                 csv_writer.writerow(tds)

爬取的数据如下图所示 

 

 2.读取csv文件

  1. import pandas as pd
  2. data = pd.read_csv('广-惠-河-深天气.csv')
  3. data

3.去除多余字符

  1. #去除多余字符
  2. data[['最高温度','最低温度']] = data[['最高温度','最低温度']].apply(lambda x: x.str.replace('°','').replace('', '0'))
  3. data.head()

4.分割星期和日期

  1. #分割日期与星期
  2. data[['日期','星期']] = data['日期'].str.split(' ',expand=True,n=1)
  3. data

5.筛选出城市数据子集。其中包含了四个城市在不同天气下的天数统计结果。

  1. # 按城市和天气分组，并计算每组的天数
  2. grouped = data.groupby(['城市', '天气']).size().reset_index(name='天数')
  3.  
  4. # 将结果按城市分为4个DataFrame
  5. gz_weather = grouped[grouped['城市'] == '广州']
  6. hy_weather = grouped[grouped['城市'] == '河源']
  7. hz_weather = grouped[grouped['城市'] == '惠州']
  8. sz_weather = grouped[grouped['城市'] == '深圳']
  9. gz_weather
hy_weather
hz_weather
sz_weather

 

 6.将原有的天气类型按照关键字划分

  1. # 定义一个函数，将原有的天气类型按照关键字划分
  2. def classify_weather(weather):
  3.     if '多云' in weather:
  4.         return '多云'
  5.     elif '晴' in weather:
  6.         return '晴'
  7.     elif '阴' in weather:
  8.         return '阴'
  9.     elif '大雨' in weather:
  10.         return '雨'
  11.     elif '中雨' in weather:
  12.         return '雨'
  13.     elif '小雨' in weather:
  14.         return '雨'
  15.     elif '雷阵雨' in weather:
  16.         return '雨'
  17.     elif '雾' in weather:
  18.         return '雾'
  19.     else:
  20.         return '其他'
  21.  
  22. # 将原有的天气类型按照关键字划分，并存进新的 DataFrame 中
  23. new_data = data[['城市', '天气']].copy()
  24. new_data['新天气'] = new_data['天气'].apply(classify_weather)
  25. new_data

7.对城市的天气数据按照新天气列分组后，计算每一种天气的天数，然后将“天气”列名改为“天数”得到的数据框。

  1. # 按照城市和新天气列进行分组，并计算每一种天气的天数
  2. count_data = new_data.groupby(['城市', '新天气'])['天气'].count().reset_index()
  3. # 根据条件筛选出符合要求的行
  4. df1 = count_data.loc[count_data['城市'] == '广州']
  5. df2 = count_data.loc[count_data['城市'] == '河源']
  6. df3 = count_data.loc[count_data['城市'] == '惠州']
  7. df4 = count_data.loc[count_data['城市'] == '深圳']
  8. # 将“天气”列名改为“天数”
  9. df5 = df1.rename(columns={'天气': '天数'})
  10. df6 = df2.rename(columns={'天气': '天数'})
  11. df7 = df3.rename(columns={'天气': '天数'})
  12. df8 = df4.rename(columns={'天气': '天数'})
  13. # 输出结果
  14. df5.to_csv('df5.csv',index=False)

上面输出结果保存到csv文件中,如果要查看可以输出df5 查看数据

 

8.筛选出每个城市平均温度等于最高温度和最低温度平均值的数据

  1. # 筛选出平均温度等于最高温度和最低温度平均值的数据
  2. data1 = data[(data['平均温度'] == (data['最高温度'] + data['最低温度']) / 2)]
  3.  
  4. data_AB = data1[(data1['城市'] == '广州') | (data1['城市'] == '深圳') | (data1['城市'] == '河源') | (data1['城市'] == '惠州')]
  5.  
  6. #将日期转换为月份并赋值给新的列
  7. data_AB['月份'] = pd.to_datetime(data_AB['日期']).dt.month
  8.  
  9. #按照城市和月份分组，计算每组的平均气温
  10. grouped_AB = data_AB.groupby(['城市', '月份'])['平均温度'].mean().reset_index()
  11.  
  12. #按照城市和月份排序
  13. grouped_AB = grouped_AB.sort_values(['城市', '月份'])
  14.  
  15. #打印结果
  16. grouped_AB

9.绘制广州、河源、惠州和深圳每日平均温度的折线图

  1. import matplotlib as mpl
  2. import matplotlib.pyplot as plt
  3. import matplotlib.ticker as ticker
  4. # 筛选出广州和湛江的数据
  5. gz_data = data[data['城市'] == '广州']
  6. hy_data = data[data['城市'] == '河源']
  7. hz_data = data[data['城市'] == '惠州']
  8. sz_data = data[data['城市'] == '深圳']
  9.  
  10. # 提取日期和平均温度数据
  11. x = gz_data['日期']
  12. y1 = gz_data['平均温度']
  13. y2 = hy_data['平均温度']
  14. y3 = hz_data['平均温度']
  15. y4 = sz_data['平均温度']
  16.  
  17. # 绘制折线图
  18. plt.figure(dpi=500, figsize=(10, 5))
  19. plt.title("广河惠深每日平均温度折线图")
  20. plt.plot(x, y1, color='red', label='广州')
  21. plt.plot(x, y2, color='blue', label='河源')
  22. plt.plot(x,y3,color='green',label='惠州')
  23. plt.plot(x,y4,color='yellow',label='深圳')
  24. # 获取图的坐标信息
  25. coordinates = plt.gca()
  26. # 设置x轴每个刻度的间隔天数
  27. xLocator = mpl.ticker.MultipleLocator(30)
  28. coordinates.xaxis.set_major_locator(xLocator)
  29. # 将日期旋转30°
  30. plt.xticks(rotation=30)
  31. plt.xticks(fontsize=8)
  32. plt.ylabel("温度(℃)")
  33. plt.xlabel("日期")
  34. plt.legend()
  35. plt.savefig("广河惠深每日平均温度折线图.png")
  36. plt.show()

10.绘制城市为广州、惠州、深圳、河源的月平均气温数据

  1. data_GZ_HZ_SZ_HY = grouped_AB[(grouped_AB['城市'] == '广州') | (grouped_AB['城市'] == '惠州') | (grouped_AB['城市'] == '深圳') | (grouped_AB['城市'] == '河源')]
  2.  
  3. #绘制折线图
  4. fig, ax = plt.subplots()
  5. for city in ['广州', '惠州', '深圳', '河源']:
  6.     ax.plot(data_GZ_HZ_SZ_HY[data_GZ_HZ_SZ_HY['城市'] == city]['月份'], data_GZ_HZ_SZ_HY[data_GZ_HZ_SZ_HY['城市'] == city]['平均温度'], label=city)
  7.  
  8. #设置图例和标题
  9. ax.legend()
  10. ax.set_title('广州、惠州、深圳、河源每月气温折线图')
  11.  
  12. #显示图形
  13. plt.show()

11.绘制四个城市数据对比

  1.  
  2. import matplotlib.pyplot as plt
  3.  
  4. #创建一个画布
  5. fig, ax = plt.subplots(figsize=(10, 5))
  6.  
  7. #绘制广州各类天气条形图
  8. ax.bar(df5['新天气'], df5['天数'], width=0.2, label='广州')
  9.  
  10. #绘制惠州各类天气条形图
  11. ax.bar(df7['新天气'], df7['天数'], width=0.2, label='惠州', alpha=0.7)
  12.  
  13. #绘制河源各类天气条形图
  14. ax.bar(df6['新天气'], df6['天数'], width=0.2, label='河源', alpha=0.7)
  15.  
  16. #绘制深圳各类天气条形图
  17. ax.bar(df8['新天气'], df8['天数'], width=0.2, label='深圳', alpha=0.7)
  18.  
  19. #设置图例
  20. ax.legend()
  21.  
  22. #设置 x 轴标签和标题
  23. ax.set_xlabel('天气类型')
  24. ax.set_ylabel('天数')
  25. ax.set_title('广州、惠州、河源、深圳各类天气天数对比')
  26.  
  27. #显示图表
  28. plt.show()

 

12.各个城市天气占比

  1. import pandas as pd
  2. from pyecharts.charts import Pie
  3. from pyecharts import options as opts
  4.  
  5. # 读取csv文件并转换为列表格式
  6. df = pd.read_csv('df5.csv')
  7. data_list = df[['新天气', '天数']].values.tolist()
  8.  
  9. # 生成饼图
  10. pie = Pie()
  11. pie.add("", data_list)
  12. pie.set_global_opts(title_opts=opts.TitleOpts(title="广州各类天气天数占比"))
  13. pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))
  14. pie.render_notebook()

  1. import pandas as pd
  2. from pyecharts.charts import Pie
  3. from pyecharts import options as opts
  4.  
  5. # 读取csv文件并转换为列表格式
  6. df = pd.read_csv('df6.csv')
  7. data_list = df[['新天气', '天数']].values.tolist()
  8.  
  9. # 生成饼图
  10. pie = Pie()
  11. pie.add("", data_list)
  12. pie.set_global_opts(title_opts=opts.TitleOpts(title="河源各类天气天数占比"))
  13. pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))
  14. pie.render_notebook()

  1. import pandas as pd
  2. from pyecharts.charts import Pie
  3. from pyecharts import options as opts
  4.  
  5. # 读取csv文件并转换为列表格式
  6. df = pd.read_csv('df7.csv')
  7. data_list = df[['新天气', '天数']].values.tolist()
  8.  
  9. # 生成饼图
  10. pie = Pie()
  11. pie.add("", data_list)
  12. pie.set_global_opts(title_opts=opts.TitleOpts(title="惠州各类天气天数占比"))
  13. pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))
  14. pie.render_notebook()

  1. import pandas as pd
  2. from pyecharts.charts import Pie
  3. from pyecharts import options as opts
  4.  
  5. # 读取csv文件并转换为列表格式
  6. df = pd.read_csv('df8.csv')
  7. data_list = df[['新天气', '天数']].values.tolist()
  8.  
  9. # 生成饼图
  10. pie = Pie()
  11. pie.add("", data_list)
  12. pie.set_global_opts(title_opts=opts.TitleOpts(title="深圳各类天气天数占比"))
  13. pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))
  14. pie.render_notebook()

13.统计四个城市每日气温折线图

  1. import matplotlib.pyplot as plt
  2. import matplotlib as mpl
  3. import pandas as pd
  4. # 筛选出广州和湛江的数据
  5. gz_data = data[data['城市'] == '广州']
  6. hy_data = data[data['城市'] == '河源']
  7. hz_data = data[data['城市'] == '惠州']
  8. sz_data = data[data['城市'] == '深圳']
  9.  
  10. # 提取日期和平均温度数据
  11. x = gz_data['日期']
  12. y1 = gz_data['平均温度']
  13. y2 = hy_data['平均温度']
  14. y3 = hz_data['平均温度']
  15. y4 = sz_data['平均温度']
  16.  
  17. # 绘制面积图
  18. plt.figure(dpi=500, figsize=(10, 5))
  19. plt.title("广州惠州河源深圳折线图")
  20. plt.fill_between(x, y1, color='red', alpha=0.5, label='广州')
  21. plt.fill_between(x, y2, color='blue', alpha=0.5, label='河源')
  22. plt.fill_between(x, y3, color='yellow', alpha=0.5, label='惠州')
  23. plt.fill_between(x, y4, color='green', alpha=0.5, label='深圳')
  24. # 获取图的坐标信息
  25. coordinates = plt.gca()
  26. # 设置x轴每个刻度的间隔天数
  27. xLocator = mpl.ticker.MultipleLocator(30)
  28. coordinates.xaxis.set_major_locator(xLocator)
  29. # 将日期旋转30°
  30. plt.xticks(rotation=30)
  31. plt.xticks(fontsize=8)
  32. plt.ylabel("温度(℃)")
  33. plt.xlabel("日期")
  34. plt.legend()
  35. plt.savefig("广-惠-河-深折线图")
  36. plt.show()

14.读取四个城市各类天气天数对比图

  1. import matplotlib.pyplot as plt
  2. from pyecharts.charts import Bar
  3. # 读取广州数据并生成柱状图
  4. df1 = pd.read_csv('df5.csv')
  5. data_list1 = df1[['新天气', '天数']].values.tolist()
  6.  
  7. # 读取河源数据并生成柱状图
  8. df2 = pd.read_csv('df6.csv')
  9. data_list2 = df2[['新天气', '天数']].values.tolist()
  10.  
  11. # 读取惠州数据并生成柱状图
  12. df3 = pd.read_csv('df7.csv')
  13. data_list3 = df3[['新天气', '天数']].values.tolist()
  14.  
  15. # 读取深圳数据并生成柱状图
  16. df4 = pd.read_csv('df8.csv')
  17. data_list4 = df4[['新天气', '天数']].values.tolist()
  18. # 合并 x 轴数据
  19. x_data = list(set([i[0] for i in data_list1] + [i[0] for i in data_list2] + [i[0] for i in data_list3] + [i[0] for i in data_list4]))
  20.  
  21. # 生成柱状图
  22. bar = Bar()
  23. bar.add_xaxis(x_data)
  24. bar.add_yaxis("广州", [df1.loc[df1['新天气']==x, '天数'].tolist()[0] if x in df1['新天气'].tolist() else 0 for x in x_data])
  25. bar.add_yaxis("河源", [df2.loc[df2['新天气']==x, '天数'].tolist()[0] if x in df2['新天气'].tolist() else 0 for x in x_data])
  26. bar.add_yaxis("惠州", [df3.loc[df3['新天气']==x, '天数'].tolist()[0] if x in df3['新天气'].tolist() else 0 for x in x_data])
  27. bar.add_yaxis("深圳", [df4.loc[df4['新天气']==x, '天数'].tolist()[0] if x in df4['新天气'].tolist() else 0 for x in x_data])
  28. bar.set_global_opts(title_opts=opts.TitleOpts(title="广州惠州河源深圳各类天气天数对比图"),
  29.                      xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)),
  30.                      legend_opts=opts.LegendOpts(pos_left="center"))
  31. bar.render_notebook()

 15.读取四个城市当中一个城市的天气天数对比图

  1. import matplotlib.pyplot as plt
  2. from pyecharts.charts import Bar
  3. # 读取惠州数据并生成柱状图
  4. df3 = pd.read_csv('df7.csv')
  5. data_list3 = df3[['新天气', '天数']].values.tolist()
  6. # 生成柱状图
  7. bar = Bar()
  8. bar.add_xaxis(x_data)
  9. bar.add_yaxis("惠州", [df3.loc[df3['新天气']==x, '天数'].tolist()[0] if x in df3['新天气'].tolist() else 0 for x in x_data])
  10. bar.set_global_opts(title_opts=opts.TitleOpts(title="广州惠州河源深圳各类天气天数对比图"),
  11.                      xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)),
  12.                      legend_opts=opts.LegendOpts(pos_left="center"))
  13. bar.render_notebook()

当今社会，数据越来越重要，因此，仪表盘成为了一种非常流行的数据可视化方式。通过使用仪表盘，用户可以更轻松地理解数据，从而做出更好的决策。在本文中，我们将介绍如何使用前端网页来实现仪表盘文字。

首先，我们需要一个基本的 HTML 文件。在这个文件中，我们将创建一个仪表盘的基本框架，并使用 CSS 来设置样式。在这个基本框架中，我们将包含一个 div 元素，用于显示仪表盘的文本。我们将使用 JavaScript 来动态地更新这个文本。

  1. <!DOCTYPE html>
  2. <html>
  3. <head>
  4.   <meta charset="UTF-8">
  5.   <title>Awesome-pyecharts</title>
  6.   <script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
  7.   <script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/jquery.min.js"></script>
  8.   <script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/jquery-ui.min.js"></script>
  9.   <script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/ResizeSensor.js"></script>
  10.  
  11.   <link rel="stylesheet"  href="https://assets.pyecharts.org/assets/v5/jquery-ui.css">
  12.  
  13. </head>
  14. <body >
  15. <style>.box {  } </style>
  16. <button onclick="downloadCfg()">Save Config</button>
  17. <div class="box">
  18.   <div id="c48b9e2d9b7440e1b2f458d54f3b383c" class="chart-container" style="width:900px; height:500px; "></div>
  19.   <script>
  20.     var chart_c48b9e2d9b7440e1b2f458d54f3b383c = echarts.init(
  21.             document.getElementById('c48b9e2d9b7440e1b2f458d54f3b383c'), 'white', {renderer: 'canvas'});
  22.     var option_c48b9e2d9b7440e1b2f458d54f3b383c = {
  23.       "animation": true,
  24.       "animationThreshold": 2000,
  25.       "animationDuration": 1000,
  26.       "animationEasing": "cubicOut",
  27.       "animationDelay": 0,
  28.       "animationDurationUpdate": 300,
  29.       "animationEasingUpdate": "cubicOut",
  30.       "animationDelayUpdate": 0,
  31.       "aria": {
  32.         "enabled": false
  33.       },
  34.       "color": [
  35.         "#5470c6",
  36.         "#91cc75",
  37.         "#fac858",
  38.         "#ee6666",
  39.         "#73c0de",
  40.         "#3ba272",
  41.         "#fc8452",
  42.         "#9a60b4",
  43.         "#ea7ccc"
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