自学教程：python采集天气数据并做数据可视化

import requests     # 第三方模块 提前安装  发送请求 (Python里面浏览器)  爆红是因为你没有安装模块# 如果安装了 但还是爆红是因为什么呢? 解释器在pycharm里面配置的不对import parselimport csv# 翻译插件

with open('天气.csv', mode='a', encoding='utf-8', newline='') as f:    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 = [54511, 58362, 59287, 59493]for city in city_list:    for year in range(2011, 2022):        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}'

            response = requests.get(url=url)            # <Response [200]>: 请求成功

            # json数据传输格式            json_data = response.json()            # 字典类型数据

            # 结构化数据解析            html_data = json_data['data']            selector = parsel.Selector(html_data)            # 正则 css xpath json字典数据解析            tr_list = selector.css('.history-table tr')            # tr_list[1:] 从列表的第二个元素开始取            for tr in tr_list[1:]:                # <X>fhwaeuifhwiuf</X>                td = tr.css('td::text').getall()                if td[2] == '°':                    td[2] = td[1]                if city == 54511:                    td.append("北京")                elif city == 58362:                    td.append("上海")                elif city == 59287:                    td.append("广州")                elif city == 59493:                    td.append("深圳")                print(td)                # 文件名 写入方式 追加写入  编码方式 utf-8  数据空行                with open('天气.csv', mode='a', encoding='utf-8', newline='') as f:                    csv_writer = csv.writer(f)                    csv_writer.writerow(td)

import pandas as pdimport datetimefrom pyecharts import options as optsfrom pyecharts.charts import *from pyecharts.commons.utils import JsCode

data = pd.read_csv('天气.csv')data

data.sample(5)data.info()

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

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()

data.loc[data['天气'].str.contains('雪'),'下雪吗']='是'data.fillna('否',inplace=True)

data['日期'] = pd.to_datetime(data['日期'])data[['最高温度','最低温度']] = data[['最高温度','最低温度']].astype('int')data['年份'] = data['日期'].dt.yeardata['月份'] = data['日期'].dt.monthdata['日'] = data['日期'].dt.day# 预览data.sample(5)

s_data = data[data['下雪吗']=='是']s_data[(s_data['月份']>=9)].groupby('年份').first().reset_index()

s_data.groupby(['城市','年份'])['日期'].count().to_frame('下雪天数').reset_index()

data_bj = data[(data['年份'] == 2021) & (data['城市'] == '北京')]data_bj = data_bj.groupby(['月份','天气'], as_index=False)['日期'].count()data_pivot =  pd.pivot(data_bj,                values='日期',                index='月份',                columns='天气')data_pivot = data_pivot.astype('float')# 按照 索引年月倒序排序data_pivot.sort_index(ascending=False,inplace=True)# 资料、解答、教程可加Q ：261823976免费领      data_pivot

import matplotlib.pyplot as pltimport matplotlib.colors as mcolorsimport 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 dpiplt.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()data_gz= data[(data['年份'] == 2021) & (data['城市'] == '广州')]data_gz = data_gz.groupby(['月份','天气'], as_index=False)['日期'].count()data_sz= data[(data['年份'] == 2021) & (data['城市'] == '深圳')]data_sz = data_sz.groupby(['月份','天气'], as_index=False)['日期'].count()data_sh= data[(data['年份'] == 2021) & (data['城市'] == '上海')]data_sh = data_sh.groupby(['月份','天气'], as_index=False)['日期'].count()data_pivot_sz =  pd.pivot(data_sz,                values='日期',                index='月份',                columns='天气')data_pivot_sz = data_pivot_sz.astype('float')# 按照 索引年月倒序排序data_pivot_sz.sort_index(ascending=False,inplace=True)#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 dpiplt.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_sz = sns.heatmap(data_pivot_sz, cmap=cmap, vmax=31,                  annot=True, # 热力图上显示数值                 linewidths=0.5,                ) # 将x轴刻度放在最上面ax_sz.xaxis.set_ticks_position('top') plt.title('深圳最近10个月天气分布',fontsize=16) #图片标题文本和字体大小plt.show()data_pivot_gz =  pd.pivot(data_gz,                values='日期',                index='月份',                columns='天气')data_pivot_gz = data_pivot_gz.astype('float')# 按照 索引年月倒序排序data_pivot_gz.sort_index(ascending=False,inplace=True)#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 dpiplt.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_sz = sns.heatmap(data_pivot_gz, cmap=cmap, vmax=31,                  annot=True, # 热力图上显示数值                 linewidths=0.5,                ) # 将x轴刻度放在最上面ax_sz.xaxis.set_ticks_position('top') plt.title('广州最近10个月天气分布',fontsize=16) #图片标题文本和字体大小plt.show()# 资料、解答、教程可加Q ：261823976免费领      data_pivot_sh =  pd.pivot(data_sh,                values='日期',                index='月份',                columns='天气')data_pivot_sh = data_pivot_sh.astype('float')# 按照 索引年月倒序排序data_pivot_sh.sort_index(ascending=False,inplace=True)#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 dpiplt.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_sz = sns.heatmap(data_pivot_sh, cmap=cmap, vmax=31,                  annot=True, # 热力图上显示数值                 linewidths=0.5,                ) # 将x轴刻度放在最上面ax_sz.xaxis.set_ticks_position('top') plt.title('上海最近10个月天气分布',fontsize=16) #图片标题文本和字体大小plt.show()data_bj = data[(data['城市']=='北京') & (data['年份'] == 2021)]data_bj['日期'] = pd.to_datetime(data_bj.日期,format="%Y年%m月%d日")data_bj = data_bj.sort_values(by='日期',ascending=True)

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()# 资料、解答、教程可加Q ：261823976免费领      data_10 = data[(data['年份'] == 2021) & ( data['月份'] == 10)]data_10.head()