近段时间天气暴热，所以采集北上广深去年天气数据，制作可视化图看下

本文主要是介绍近段时间天气暴热，所以采集北上广深去年天气数据，制作可视化图看下，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

前言

最近天气异常暴热，看到某些地方地表温度居然达到70°，这就离谱
所以就想采集一下天气的数据，做个可视化图，回忆一下去年的天气情况

开发环境

python 3.8 运行代码
pycharm 2021.2 辅助敲代码
requests 第三方模块

天气数据采集

1. 发送请求

url = 'https://tianqi.2345.com/Pc/GetHistory?areaInfo%5BareaId%5D=54511&areaInfo%5BareaType%5D=2&date%5Byear%5D=2022&date%5Bmonth%5D=5'
response = requests.get(url)
print(response)

返回<Response [200]>: 请求成功

2. 获取数据

print(response.json())

3. 解析数据天气信息提取出来

结构化数据解析：Python字典取值
非结构化数据解析：网页结构

json_data = response.json()
html_data = json_data['data']
select = parsel.Selector(html_data)
trs = select.css('table tr')
for tr in trs[1:]:# 网页结构# html网页 <td>asdfwaefaewfweafwaef</td> <a></a> <div></div># ::text: 我需要这个 标签里面的文本内容td = tr.css('td::text').getall()print(td)

4. 保存数据

with open('天气数据.csv', encoding='utf-8', mode='a', newline='') as f:csv_writer = csv.writer(f)csv_writer.writerow(td)

数据可视化效果

读取数据

data = pd.read_csv('天气数据.csv')
data

分割日期/星期

data[['日期','星期']] = data['日期'].str.split(' ',expand=True,n=1)
data

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('°',''))
data.head()

北上广深2021年10月份天气热力图分布

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import seaborn as sns#设置全局默认字体 为 雅黑
plt.rcParams['font.family'] = ['Microsoft YaHei'] 
# 设置全局轴标签字典大小
plt.rcParams["axes.labelsize"] = 14  
# 设置背景
sns.set_style("darkgrid",{"font.family":['Microsoft YaHei', 'SimHei']})  
# 设置画布长宽 和 dpi
plt.figure(figsize=(18,8),dpi=100)
# 自定义色卡
cmap = mcolors.LinearSegmentedColormap.from_list("n",['#95B359','#D3CF63','#E0991D','#D96161','#A257D0','#7B1216']) 
# 绘制热力图ax = sns.heatmap(data_pivot, cmap=cmap, vmax=30, annot=True, # 热力图上显示数值linewidths=0.5,) 
# 将x轴刻度放在最上面
ax.xaxis.set_ticks_position('top') 
plt.title('北京最近10个月天气分布',fontsize=16) #图片标题文本和字体大小
plt.show()

北京2021年每日最高最低温度变化

color0 = ['#FF76A2','#24ACE6']
color_js0 = """new echarts.graphic.LinearGradient(0, 1, 0, 0,[{offset: 0, color: '#FFC0CB'}, {offset: 1, color: '#ed1941'}], false)"""
color_js1 = """new echarts.graphic.LinearGradient(0, 1, 0, 0,[{offset: 0, color: '#FFFFFF'}, {offset: 1, color: '#009ad6'}], false)"""tl = Timeline()
for i in range(0,len(data_bj)):coordy_high = list(data_bj['最高温度'])[i]coordx = list(data_bj['日期'])[i]coordy_low = list(data_bj['最低温度'])[i]x_max = list(data_bj['日期'])[i]+datetime.timedelta(days=10)y_max = int(max(list(data_bj['最高温度'])[0:i+1]))+3y_min = int(min(list(data_bj['最低温度'])[0:i+1]))-3title_date = list(data_bj['日期'])[i].strftime('%Y-%m-%d')c = (Line(init_opts=opts.InitOpts(theme='dark',#设置动画animation_opts=opts.AnimationOpts(animation_delay_update=800),#(animation_delay=1000, animation_easing="elasticOut"),#设置宽度、高度width='1500px',height='900px', )).add_xaxis(list(data_bj['日期'])[0:i]).add_yaxis(series_name="",y_axis=list(data_bj['最高温度'])[0:i], is_smooth=True,is_symbol_show=False,linestyle_opts={'normal': {'width': 3,'shadowColor': 'rgba(0, 0, 0, 0.5)','shadowBlur': 5,'shadowOffsetY': 10,'shadowOffsetX': 10,'curve': 0.5,'color': JsCode(color_js0)}},itemstyle_opts={"normal": {"color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{offset: 0,color: '#ed1941'}, {offset: 1,color: '#009ad6'}], false)"""),"barBorderRadius": [45, 45, 45, 45],"shadowColor": "rgb(0, 160, 221)",}},).add_yaxis(series_name="",y_axis=list(data_bj['最低温度'])[0:i], is_smooth=True,is_symbol_show=False,
#             linestyle_opts=opts.LineStyleOpts(color=color0[1],width=3),itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_js1)),linestyle_opts={'normal': {'width': 3,'shadowColor': 'rgba(0, 0, 0, 0.5)','shadowBlur': 5,'shadowOffsetY': 10,'shadowOffsetX': 10,'curve': 0.5,'color': JsCode(color_js1)}},).set_global_opts(title_opts=opts.TitleOpts("北京2021年每日最高最低温度变化\n\n{}".format(title_date),pos_left=330,padding=[30,20]),xaxis_opts=opts.AxisOpts(type_="time",max_=x_max),#, interval=10,min_=i-5,split_number=20,axistick_opts=opts.AxisTickOpts(length=2500),axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color="grey"))yaxis_opts=opts.AxisOpts(min_=y_min,max_=y_max),#坐标轴颜色,axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color="grey"))))tl.add(c, "{}".format(list(data_bj['日期'])[i]))tl.add_schema(axis_type='time',play_interval=100,  # 表示播放的速度pos_bottom="-29px",is_loop_play=False, # 是否循环播放width="780px",pos_left='30px',is_auto_play=True,  # 是否自动播放。is_timeline_show=False)
tl.render_notebook()

北上广深10月份每日最高气温变化

# 背景色
background_color_js = ("new echarts.graphic.LinearGradient(0, 0, 0, 1, ""[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)# 线条样式
linestyle_dic = { 'normal': {'width': 4,  'shadowColor': '#696969', 'shadowBlur': 10,  'shadowOffsetY': 10,  'shadowOffsetX': 10,  }}timeline = Timeline(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),width='980px',height='600px'))bj, gz, sh, sz= [], [], [], []
all_max = []
x_data = data_10[data_10['城市'] == '北京']['日'].tolist()
for d_time in range(len(x_data)):bj.append(data_10[(data_10['日'] == x_data[d_time]) & (data_10['城市']=='北京')]["最高温度"].values.tolist()[0])gz.append(data_10[(data_10['日'] == x_data[d_time]) & (data_10['城市']=='广州')]["最高温度"].values.tolist()[0])sh.append(data_10[(data_10['日'] == x_data[d_time]) & (data_10['城市']=='上海')]["最高温度"].values.tolist()[0])sz.append(data_10[(data_10['日'] == x_data[d_time]) & (data_10['城市']=='深圳')]["最高温度"].values.tolist()[0])line = (Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),width='980px',height='600px')).add_xaxis(x_data,).add_yaxis('北京',bj,symbol_size=5,is_smooth=True,is_hover_animation=True,label_opts=opts.LabelOpts(is_show=False),).add_yaxis('广州',gz,symbol_size=5,is_smooth=True,is_hover_animation=True,label_opts=opts.LabelOpts(is_show=False),).add_yaxis('上海',sh,symbol_size=5,is_smooth=True,is_hover_animation=True,label_opts=opts.LabelOpts(is_show=False),).add_yaxis('深圳',sz,symbol_size=5,is_smooth=True,is_hover_animation=True,label_opts=opts.LabelOpts(is_show=False),).set_series_opts(linestyle_opts=linestyle_dic).set_global_opts(title_opts=opts.TitleOpts(title='北上广深10月份最高气温变化趋势',pos_left='center',pos_top='2%',title_textstyle_opts=opts.TextStyleOpts(color='#DC143C', font_size=20)),tooltip_opts=opts.TooltipOpts(trigger="axis",axis_pointer_type="cross",background_color="rgba(245, 245, 245, 0.8)",border_width=1,border_color="#ccc",textstyle_opts=opts.TextStyleOpts(color="#000"),),xaxis_opts=opts.AxisOpts(
#                 axislabel_opts=opts.LabelOpts(font_size=14, color='red'),
#                 axisline_opts=opts.AxisLineOpts(is_show=True,
#                 linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))is_show = False),yaxis_opts=opts.AxisOpts(name='最高气温',            is_scale=True,
#                 min_= int(min([gz[d_time],sh[d_time],sz[d_time],bj[d_time]])) - 10,max_= int(max([gz[d_time],sh[d_time],sz[d_time],bj[d_time]])) + 10,name_textstyle_opts=opts.TextStyleOpts(font_size=16,font_weight='bold',color='#5470c6'),axislabel_opts=opts.LabelOpts(font_size=13,color='#5470c6'),splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(type_='dashed')),axisline_opts=opts.AxisLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(width=2, color='#5470c6'))),legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',legend_icon='roundRect',orient = 'vertical'),))timeline.add(line, '{}'.format(x_data[d_time]))timeline.add_schema(play_interval=1000,          # 轮播速度is_timeline_show=True,      # 是否显示 timeline 组件is_auto_play=True,          # 是否自动播放pos_left="0",pos_right="0"
)
timeline.render_notebook()