Python探秘:在成都需要戴口罩防霾吗？

#导入包import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns#导入数据data_cd = pd.read_excel(r'成都空气质量.xlsx')

#查看数据data_cd.head()

data_cd.tail()

data_cd=data_cd.iloc[59:71] #只取2018-11到2019-10的数据data_cd.drop('时间_链接',1) #删除“时间_链接”列data_cd_aqi=data_cd[['时间_文本','AQI']] #只取“时间_文本”和“AQI”列绘制折线图# matplotlib字体的默认设置中没有中文字体，运行如下代码添加中文字体的名称from pylab import * mpl.rcParams['font.sans-serif'] = ['SimHei'] plt.figure(figsize=(2, 2)) #图片长宽plt.rcParams['savefig.dpi'] = 500 #图片像素plt.rcParams['figure.dpi'] = 300 #分辨率data_cd_aqi_plot=data_cd_aqi.plot(    kind="line",    title="成都市近一年月平均空气质量指数（截止2019年10月）",    ylim=50,    color='limegreen')data_cd_aqi_plot.set_ylabel('AQI')data_cd_aqi_plot.set_xlabel('月份')#设置x轴x_ticks=[]for i in range(59,71):    x_ticks.append(data_cd_aqi['时间_文本'][i])x = range(59,71,1)plt.xticks(x, x_ticks,rotation=-60)#在图中区分季节winter_end=62.5plt.axvline(x=winter_end,color="powderblue",ls="--",lw=0.5)plt.text(data_cd_aqi[data_cd_aqi["时间_文本"]=="2019-01"].index[0], 90,"冬",fontsize=12,color="powderblue")spring_end=65.6plt.axvline(x=spring_end,color="palegreen",ls="--",lw=0.5)plt.text(data_cd_aqi[data_cd_aqi["时间_文本"]=="2019-04"].index[0], 90,"春",fontsize=12,color="palegreen")summer_end=68.5plt.axvline(x=summer_end,color="lightcoral",ls="--",lw=0.5)plt.text(data_cd_aqi[data_cd_aqi["时间_文本"]=="2019-07"].index[0], 90,"夏",fontsize=12,color="lightcoral")autumn_end=59.5plt.axvline(x=autumn_end,color="bisque",ls="--",lw=0.5)plt.text(69.5, 90,"秋",fontsize=12,color="bisque")plt.savefig('成都近一年月空气质量指数.jpg', dpi=500,bbox_inches = 'tight')plt.show()

#导入包import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns#导入数据data_cd_day = pd.read_excel(r'成都日空气质量.xlsx')查看数据data_cd_day.head()

data_cd_day.tail()

data_cd_day=data_cd_day.iloc[1795:2160] #只取2018-11-01到2019-10-31的数据#处理缺失值data_cd_day[data_cd_day["空气质量等级"]=="无"] #查找缺失值

#删除缺失值data_cd_day=data_cd_day.drop(index=data_cd_day[data_cd_day['空气质量等级']=='无'].index)data_cd_day[data_cd_day["空气质量等级"]=="无"]data_cd_day_aqi=data_cd_day[['日期','AQI']] #只取“日期”和“AQI”列data_cd_day_aqi=data_cd_day_aqi.reset_index(drop=True)#reset_index:重新设置索引,drop=True:删除原索引绘制折线图plt.figure(figsize=(2, 1))plt.rcParams['savefig.dpi'] = 500plt.rcParams['figure.dpi'] = 300data_cd_day_aqi_plot=data_cd_day_aqi.plot(    kind="line",    title="成都市近一年日平均空气质量指数（截止2019年10月31日）",    ylim=0,    color="cornflowerblue")data_cd_day_aqi_plot.set_ylabel('AQI')data_cd_day_aqi_plot.set_xlabel('日期')#设置x轴x_ticks=[]for i in range(0,365,30):    x_ticks.append(data_cd_day_aqi['日期'][i])x = range(0,365,30)plt.xticks(x, x_ticks,rotation=-70)#在图中标记AQI均值aqi_ave=np.mean(data_cd_day_aqi["AQI"])plt.axhline(y=aqi_ave,color="lightsteelblue",ls="--")#在图中标记最值点max_x=data_cd_day_aqi[data_cd_day_aqi["AQI"]==data_cd_day_aqi["AQI"].max()].index[0]max_y=data_cd_day_aqi["AQI"].max()plot(max_x, max_y,'.',color="coral")plt.text(max_x, max_y, max_y,fontsize=9,color="coral")min_x=data_cd_day_aqi[data_cd_day_aqi["AQI"]==data_cd_day_aqi["AQI"].min()].index[0]min_y=data_cd_day_aqi["AQI"].min()plot(min_x, min_y,'.',color="seagreen")plt.text(min_x, min_y, min_y,fontsize=9,color="seagreen")#在图中区分季节winter_end=data_cd_day_aqi[data_cd_day_aqi["日期"]=="2019-02-28"].index[0]plt.axvline(x=winter_end,color="powderblue",ls="--",lw=0.5)plt.text(data_cd_day_aqi[data_cd_day_aqi["日期"]=="2019-01-07"].index[0], 190,"冬",fontsize=12,color="powderblue")spring_end=data_cd_day_aqi[data_cd_day_aqi["日期"]=="2019-05-31"].index[0]plt.axvline(x=spring_end,color="palegreen",ls="--",lw=0.5)plt.text(data_cd_day_aqi[data_cd_day_aqi["日期"]=="2019-04-07"].index[0], 190,"春",fontsize=12,color="palegreen")summer_end=data_cd_day_aqi[data_cd_day_aqi["日期"]=="2019-08-31"].index[0]plt.axvline(x=summer_end,color="lightcoral",ls="--",lw=0.5)plt.text(data_cd_day_aqi[data_cd_day_aqi["日期"]=="2019-07-07"].index[0], 190,"夏",fontsize=12,color="lightcoral")autumn_end=data_cd_day_aqi[data_cd_day_aqi["日期"]=="2018-11-30"].index[0]plt.axvline(x=autumn_end,color="bisque",ls="--",lw=0.5)plt.text(data_cd_day_aqi[data_cd_day_aqi["日期"]=="2019-10-07"].index[0], 190,"秋",fontsize=12,color="bisque")plt.show()

aqi_list=[]for i in range(0,data_cd_day_aqi.shape[0]):    aqi_list.append(data_cd_day_aqi["AQI"][i])aqi_array=np.array(aqi_list) #列表转数组aqi_array.resize(18,20)import matplotlib.colors as colcpool = [ '#3EE48B', '#FBFB9A', '#FDA43B', '#FD433B', '#972E70', '#972E70' ]cmap_c = col.ListedColormap(cpool[0:6], 'indexed')fig, ax = plt.subplots(figsize = (9,9))sns.heatmap(    aqi_array,    cmap=cmap_c,    vmax=300,    vmin=0,    xticklabels=False,    yticklabels=False,)ax.set_title('成都近一年（截止到2019年10月底）空气质量等级热力图', fontsize = 15)

#分组统计不同空气质量等级对应的天数data_aqi_rank=data_cd_day.groupby('空气质量等级').count()

# 生成环形图数据a=data_aqi_rank.iloc[1,0]b=data_aqi_rank.iloc[2,0]c=data_aqi_rank.iloc[3,0]d=data_aqi_rank.iloc[0,0]e=data_aqi_rank.iloc[4,0]plt.figure(figsize = (10, 8))labels = [    '优（0≤AQI≤50）',    '良（51≤AQI≤100）',    '轻度污染（101≤AQI≤150）',    '中毒污染（151≤AQI≤200）',    '重度污染（201≤AQI≤300）']data_rank_count = [a,b,c,d,e]colors = ['limegreen', 'gold', 'orange', 'red', 'mediumvioletred','maroon']# 画环形图wedges1, texts1, autotexts1 = plt.pie(    data_rank_count,    autopct = '%3.1f%%',    radius = 1,    pctdistance = 0.85,    colors = colors,    startangle = 180,    textprops = {'color': 'w'},    wedgeprops = {'width': 0.55, 'edgecolor': 'w'})# 图例plt.legend(wedges1,          labels,          fontsize = 12,          title = '空气质量等级',          loc = 'lower right',          bbox_to_anchor = (1, 0.6))# 设置文本样式plt.setp(autotexts1, size=20, weight='bold')# 标题plt.title('成都近一年空气质量等级情况（截止2019年10月底）', fontsize=20)plt.show()