【空气质量数据分析专题七】污染物浓度月变化分析_python

前言

对空气质量日级别五年数据进行月变化分析，可以看出污染物浓度随月份变化的特征。

分析流程

对数据进行专题二的预处理后，计算出各污染物全时段的各月平均浓度，最后进行可视化分析。

月变化分析方式有多种，这里通过峰峦图进行分析。

核心代码

这部分使用Python处理数据，然后使用R进行绘图，其中a_January代表1月，后面的以此类推，在月份英文名前加字母主要是确保月份有序，从而峰峦图才能按月份排列，更为直观。

（1）处理数据（Python代码）

def month_trend_analysis(self, df_station, year_list):
     """
     日记别浓度月变化分析
     :param df_station: 站点数据
     :param year_list: 年份列表
     :return:
     """
     df_station['month'][df_station['month'] == 1] = 'a_January'
     df_station['month'][df_station['month'] == 2] = 'b_February'
     df_station['month'][df_station['month'] == 3] = 'c_March'
     df_station['month'][df_station['month'] == 4] = 'd_April'
     df_station['month'][df_station['month'] == 5] = 'e_May'
     df_station['month'][df_station['month'] == 6] = 'f_June'
     df_station['month'][df_station['month'] == 7] = 'g_July'
     df_station['month'][df_station['month'] == 8] = 'h_August'
     df_station['month'][df_station['month'] == 9] = 'i_September'
     df_station['month'][df_station['month'] == 10] = 'j_October'
     df_station['month'][df_station['month'] == 11] = 'k_November'
     df_station['month'][df_station['month'] == 12] = 'l_December'
     result2 = pd.pivot_table(df_station, index=['month', 'day'], aggfunc=np.mean,values=['PM10', 'PM2.5', 'SO2', 'NO2', 'O3', 'CO'])
     for i in result2.index[:]:
         result2.loc[i, 'month1'] = i[0]
         result2.loc[i, 'day1'] = i[1]
     pic_loc0 = Path(self.cf_info['output']['picture']).joinpath(df_station['city'].values[0])
     pic_loc = pic_loc0.joinpath('污染物月变化特征')
     if not os.path.exists(pic_loc):
         os.mkdir(pic_loc)
     result2.to_excel(pic_loc / (
             df_station['station'].values[0] + str(year_list[0]) + '-' + str(year_list[-1]) + '年各污染物月浓度变化.xls'),
                      encoding='gbk')

（2）可视化分析（R代码，省去了路径）

micefiles <- list.files(micepath, full.names = TRUE)

cols=c("green","yellow","orange","red","purple","maroon")

for(i in 1:length(micefiles)){
  file_full <- strsplit(micefiles[i], '/')
  f_names <- strsplit(file_full[[1]][2], '各')
  name <- f_names[[1]][1]

  data <- read.xlsx(micefiles[i], sheetIndex=1)
  ggplot(data, aes(x=`PM2.5`, y=`month1`, fill = stat(x)))+geom_density_ridges_gradient(scale=3, rel_min_height=0.01, gradient_lwd =.6)+scale_x_continuous(expand = c(0.01, 0))+scale_y_discrete(expand = c(0.01,0))+scale_fill_viridis(name="PM2.5浓度（微克/立方米）", option = "D")+labs(title="PM2.5浓度月变化")+theme_ridges(font_size = 13, grid = TRUE)+theme(axis.title.y = element_blank())
  x1 = paste(name, 'pm2_5', sep = "")
  ggsave(paste(x1,"jpeg",sep="."))
  
  ggplot(data, aes(x=`PM10`, y=`month1`, fill = stat(x)))+geom_density_ridges_gradient(scale=3, rel_min_height=0.01, gradient_lwd =.6)+scale_x_continuous(expand = c(0.01, 0))+scale_y_discrete(expand = c(0.01,0))+scale_fill_viridis(name="PM10浓度（微克/立方米）", option = "D")+labs(title="PM10浓度月变化")+theme_ridges(font_size = 13, grid = TRUE)+theme(axis.title.y = element_blank())
  x1 = paste(name, 'pm10', sep = "")
  ggsave(paste(x1,"jpeg",sep="."))

  ggplot(data, aes(x=`SO2`, y=`month1`, fill = stat(x)))+geom_density_ridges_gradient(scale=3, rel_min_height=0.01, gradient_lwd =.6)+scale_x_continuous(expand = c(0.01, 0))+scale_y_discrete(expand = c(0.01,0))+scale_fill_viridis(name="SO2浓度（微克/立方米）", option = "D")+labs(title="SO2浓度月变化")+theme_ridges(font_size = 13, grid = TRUE)+theme(axis.title.y = element_blank())
  x1 = paste(name, 'so2', sep = "")
  ggsave(paste(x1,"jpeg",sep="."))

  ggplot(data, aes(x=`NO2`, y=`month1`, fill = stat(x)))+geom_density_ridges_gradient(scale=3, rel_min_height=0.01, gradient_lwd =.6)+scale_x_continuous(expand = c(0.01, 0))+scale_y_discrete(expand = c(0.01,0))+scale_fill_viridis(name="NO2浓度（微克/立方米）", option = "D")+labs(title="NO2浓度月变化")+theme_ridges(font_size = 13, grid = TRUE)+theme(axis.title.y = element_blank())
  x1 = paste(name, 'no2', sep = "")
  ggsave(paste(x1,"jpeg",sep="."))

  ggplot(data, aes(x=`O3`, y=`month1`, fill = stat(x)))+geom_density_ridges_gradient(scale=3, rel_min_height=0.01, gradient_lwd =.6)+scale_x_continuous(expand = c(0.01, 0))+scale_y_discrete(expand = c(0.01,0))+scale_fill_viridis(name="O3浓度（微克/立方米）", option = "D")+labs(title="O3浓度月变化")+theme_ridges(font_size = 13, grid = TRUE)+theme(axis.title.y = element_blank())
  x1 = paste(name, 'o3', sep = "")
  ggsave(paste(x1,"jpeg",sep="."))

  ggplot(data, aes(x=`CO`, y=`month1`, fill = stat(x)))+geom_density_ridges_gradient(scale=3, rel_min_height=0.01, gradient_lwd =.6)+scale_x_continuous(expand = c(0.01, 0))+scale_y_discrete(expand = c(0.01,0))+scale_fill_viridis(name="CO浓度（毫克/立方米）", option = "D")+labs(title="CO浓度月变化")+theme_ridges(font_size = 13, grid = TRUE)+theme(axis.title.y = element_blank())
  x1 = paste(name, 'co', sep = "")
  ggsave(paste(x1,"jpeg",sep="."))