百木园前言
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
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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]))+3 y_min = int(min(list(data_bj[\'最低温度\'])[0:i+1]))-3 title_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()
来源：https://www.cnblogs.com/qshhl/p/16426623.html
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