Python爬取城市天气数据,并作数据可视化

1.爬取广惠河深2022-2024年的天气数据

import requests # 发送请求要用的模块 需要额外安装的import parselimport csvf = open('广-惠-河-深天气.csv', mode='a', encoding='utf-8', newline='')csv_writer = csv.writer(f)csv_writer.writerow(['日期', '最高温度', '最低温度', '天气', '风向', '城市'])city_list = [72049, 59287,59293,59493]for city in city_list:city_name = ''if city == 72049:city_name = '惠州'elif city == 59287:city_name = '广州'elif city == 59293:city_name = '河源'elif city == 59493:city_name = '深圳'for year in range(2022, 2024):for month in range(1, 13):url = f'https://tianqi.2345.com/Pc/GetHistory?areaInfo%5BareaId%5D={city}&areaInfo%5BareaType%5D=2&date%5Byear%5D={year}&date%5Bmonth%5D={month}'# 1. 发送请求response = requests.get(url=url)# 2. 获取数据html_data = response.json()['data']# 3. 解析数据select = parsel.Selector(html_data)trs = select.css('.history-table tr') # 拿到31个trfor tr in trs[1:]:# 第一个表头不要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tds.append(city_name) # 把城市追加到列表里面print(tds)# 4. 保存数据csv_writer.writerow(tds)

爬取的数据如下图所示

2.读取csv文件

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

3.去除多余字符

#去除多余字符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'))data.head()

4.分割星期和日期

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

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

hy_weather

hz_weather

sz_weather

6.将原有的天气类型按照关键字划分

# 定义一个函数，将原有的天气类型按照关键字划分def classify_weather(weather):if '多云' in weather:return '多云'elif '晴' in weather:return '晴'elif '阴' in weather:return '阴'elif '大雨' in weather:return '雨'elif '中雨' in weather:return '雨'elif '小雨' in weather:return '雨'elif '雷阵雨' in weather:return '雨'elif '雾' in weather:return '雾'else:return '其他'# 将原有的天气类型按照关键字划分，并存进新的 DataFrame 中new_data = data[['城市', '天气']].copy()new_data['新天气'] = new_data['天气'].apply(classify_weather)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。

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

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

9.绘制广州、河源、惠州和深圳每日平均温度的折线图

import matplotlib as mplimport matplotlib.pyplot as pltimport matplotlib.ticker as ticker# 筛选出广州和湛江的数据gz_data = data[data['城市'] == '广州']hy_data = data[data['城市'] == '河源']hz_data = data[data['城市'] == '惠州']sz_data = data[data['城市'] == '深圳']# 提取日期和平均温度数据x = gz_data['日期']y1 = gz_data['平均温度']y2 = hy_data['平均温度']y3 = hz_data['平均温度']y4 = sz_data['平均温度']# 绘制折线图plt.figure(dpi=500, figsize=(10, 5))plt.title("广河惠深每日平均温度折线图")plt.plot(x, y1, color='red', label='广州')plt.plot(x, y2, color='blue', label='河源')plt.plot(x,y3,color='green',label='惠州')plt.plot(x,y4,color='yellow',label='深圳')# 获取图的坐标信息coordinates = plt.gca()# 设置x轴每个刻度的间隔天数xLocator = mpl.ticker.MultipleLocator(30)coordinates.xaxis.set_major_locator(xLocator)# 将日期旋转30°plt.xticks(rotation=30)plt.xticks(fontsize=8)plt.ylabel("温度(℃)")plt.xlabel("日期")plt.legend()plt.savefig("广河惠深每日平均温度折线图.png")plt.show()

10.绘制城市为广州、惠州、深圳、河源的月平均气温数据

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')]#绘制折线图fig, ax = plt.subplots()for city in ['广州', '惠州', '深圳', '河源']: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)#设置图例和标题ax.legend()ax.set_title('广州、惠州、深圳、河源每月气温折线图')#显示图形plt.show()

11.绘制四个城市数据对比

import matplotlib.pyplot as plt#创建一个画布fig, ax = plt.subplots(figsize=(10, 5))#绘制广州各类天气条形图ax.bar(df5['新天气'], df5['天数'], width=0.2, label='广州')#绘制惠州各类天气条形图ax.bar(df7['新天气'], df7['天数'], width=0.2, label='惠州', alpha=0.7)#绘制河源各类天气条形图ax.bar(df6['新天气'], df6['天数'], width=0.2, label='河源', alpha=0.7)#绘制深圳各类天气条形图ax.bar(df8['新天气'], df8['天数'], width=0.2, label='深圳', alpha=0.7)#设置图例ax.legend()#设置 x 轴标签和标题ax.set_xlabel('天气类型')ax.set_ylabel('天数')ax.set_title('广州、惠州、河源、深圳各类天气天数对比')#显示图表plt.show()

12.各个城市天气占比

import pandas as pdfrom pyecharts.charts import Piefrom pyecharts import options as opts# 读取csv文件并转换为列表格式df = pd.read_csv('df5.csv')data_list = df[['新天气', '天数']].values.tolist()# 生成饼图pie = Pie()pie.add("", data_list)pie.set_global_opts(title_opts=opts.TitleOpts(title="广州各类天气天数占比"))pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))pie.render_notebook()

import pandas as pdfrom pyecharts.charts import Piefrom pyecharts import options as opts# 读取csv文件并转换为列表格式df = pd.read_csv('df6.csv')data_list = df[['新天气', '天数']].values.tolist()# 生成饼图pie = Pie()pie.add("", data_list)pie.set_global_opts(title_opts=opts.TitleOpts(title="河源各类天气天数占比"))pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))pie.render_notebook()

import pandas as pdfrom pyecharts.charts import Piefrom pyecharts import options as opts# 读取csv文件并转换为列表格式df = pd.read_csv('df7.csv')data_list = df[['新天气', '天数']].values.tolist()# 生成饼图pie = Pie()pie.add("", data_list)pie.set_global_opts(title_opts=opts.TitleOpts(title="惠州各类天气天数占比"))pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))pie.render_notebook()

import pandas as pdfrom pyecharts.charts import Piefrom pyecharts import options as opts# 读取csv文件并转换为列表格式df = pd.read_csv('df8.csv')data_list = df[['新天气', '天数']].values.tolist()# 生成饼图pie = Pie()pie.add("", data_list)pie.set_global_opts(title_opts=opts.TitleOpts(title="深圳各类天气天数占比"))pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}"))pie.render_notebook()

13.统计四个城市每日气温折线图

import matplotlib.pyplot as pltimport matplotlib as mplimport pandas as pd# 筛选出广州和湛江的数据gz_data = data[data['城市'] == '广州']hy_data = data[data['城市'] == '河源']hz_data = data[data['城市'] == '惠州']sz_data = data[data['城市'] == '深圳']# 提取日期和平均温度数据x = gz_data['日期']y1 = gz_data['平均温度']y2 = hy_data['平均温度']y3 = hz_data['平均温度']y4 = sz_data['平均温度']# 绘制面积图plt.figure(dpi=500, figsize=(10, 5))plt.title("广州惠州河源深圳折线图")plt.fill_between(x, y1, color='red', alpha=0.5, label='广州')plt.fill_between(x, y2, color='blue', alpha=0.5, label='河源')plt.fill_between(x, y3, color='yellow', alpha=0.5, label='惠州')plt.fill_between(x, y4, color='green', alpha=0.5, label='深圳')# 获取图的坐标信息coordinates = plt.gca()# 设置x轴每个刻度的间隔天数xLocator = mpl.ticker.MultipleLocator(30)coordinates.xaxis.set_major_locator(xLocator)# 将日期旋转30°plt.xticks(rotation=30)plt.xticks(fontsize=8)plt.ylabel("温度(℃)")plt.xlabel("日期")plt.legend()plt.savefig("广-惠-河-深折线图")plt.show()

14.读取四个城市各类天气天数对比图

import matplotlib.pyplot as pltfrom pyecharts.charts import Bar# 读取广州数据并生成柱状图df1 = pd.read_csv('df5.csv')data_list1 = df1[['新天气', '天数']].values.tolist()# 读取河源数据并生成柱状图df2 = pd.read_csv('df6.csv')data_list2 = df2[['新天气', '天数']].values.tolist()# 读取惠州数据并生成柱状图df3 = pd.read_csv('df7.csv')data_list3 = df3[['新天气', '天数']].values.tolist()# 读取深圳数据并生成柱状图df4 = pd.read_csv('df8.csv')data_list4 = df4[['新天气', '天数']].values.tolist()# 合并 x 轴数据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]))# 生成柱状图bar = Bar()bar.add_xaxis(x_data)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])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])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])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])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"), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)), legend_opts=opts.LegendOpts(pos_left="center"))bar.render_notebook()

15.读取四个城市当中一个城市的天气天数对比图

import matplotlib.pyplot as pltfrom pyecharts.charts import Bar# 读取惠州数据并生成柱状图df3 = pd.read_csv('df7.csv')data_list3 = df3[['新天气', '天数']].values.tolist()# 生成柱状图bar = Bar()bar.add_xaxis(x_data)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])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"), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)), legend_opts=opts.LegendOpts(pos_left="center"))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。

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