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)
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返回: 请求成功

2. 获取数据

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>
- # ::text: 我需要这个 标签里面的文本内容
- td = tr.css('td::text').getall()
- print(td)
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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)
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数据可视化效果
读取数据
- data = pd.read_csv('天气数据.csv')
- data
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分割日期/星期
- data[['日期','星期']] = data['日期'].str.split(' ',expand=True,n=1)
- data
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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()
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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()
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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()
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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()
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