网站开发需要学习什么技术,wordpress修复缩略图,佛山新网站建设服务,合肥小程序建设目录 1.上海市的空气质量 2.成都市的空气质量 【沈阳市空气质量情况详见下期】 五城P.M.2.5数据分析与可视化——北京市、上海市、广州市、沈阳市、成都市#xff0c;使用华夫图和柱状图分析各个城市的情况 1.上海市的空气质量
import numpy as np
import pandas as pd
impor… 目录 1.上海市的空气质量 2.成都市的空气质量 【沈阳市空气质量情况详见下期】 五城P.M.2.5数据分析与可视化——北京市、上海市、广州市、沈阳市、成都市使用华夫图和柱状图分析各个城市的情况 1.上海市的空气质量
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pywaffle import Waffle
import math
#读入文件
sh pd.read_csv(./Shanghai.csv)
fig plt.figure(dpi100,figsize(5,5))def good(pm):#优degree []for i in pm:if 0 i 35:degree.append(i)return degree
def moderate(pm):#良degree []for i in pm:if 35 i 75:degree.append(i)return degree
def lightlyP(pm):#轻度污染degree []for i in pm:if 75 i 115:degree.append(i)return degree
def moderatelyP(pm):#中度污染degree []for i in pm:if 115 i 150:degree.append(i)return degree
def heavilyP(pm):#重度污染degree []for i in pm:if 150 i 250:degree.append(i)return degree
def severelyP(pm):#严重污染degree []for i in pm:if 250 i:degree.append(i)return degreedef PM(sh,str3):sh_dist_pm sh.loc[:, [str3]]sh_dist1_pm sh_dist_pm.dropna(axis0, subset[str3])sh_dist1_pm np.array(sh_dist1_pm[str3])sh_good_count len(good(sh_dist1_pm))sh_moderate_count len(moderate(sh_dist1_pm))sh_lightlyP_count len(lightlyP(sh_dist1_pm))sh_moderatelyP_count len(moderatelyP(sh_dist1_pm))sh_heavilyP_count len(heavilyP(sh_dist1_pm))sh_severelyP_count len(severelyP(sh_dist1_pm))a {优:sh_good_count,良:sh_moderate_count,轻度污染:sh_lightlyP_count,中度污染:sh_moderatelyP_count,重度污染:sh_heavilyP_count,严重污染:sh_severelyP_count}pm pd.DataFrame(pd.Series(a),columns[daysum])pm pm.reset_index().rename(columns{index:level})return pm
#上海
#PM_Jingan列
sh_jg PM(sh,PM_Jingan)
PMday_Jingan np.array(sh_jg[daysum])
#PM_Xuhui列
sh_xh PM(sh,PM_Xuhui)
PMday_Xuhui np.array(sh_xh[daysum])
sh_pm_daysum (PMday_JinganPMday_Xuhui)/2
sum 0
for i in sh_pm_daysum:sum i
sh_pm_daysum1 np.array(sh_pm_daysum)data {优:int((sh_pm_daysum[0]/sum)*100), 良:int((sh_pm_daysum[1]/sum)*100), 轻度污染: int(sh_pm_daysum[2]/sum*100),中度污染:int((sh_pm_daysum[3]/sum)*100),重度污染:int((sh_pm_daysum[4]/sum)*100),严重污染:int((sh_pm_daysum[5]/sum)*100)}
total np.sum(list(data.values()))
plt.figure(FigureClassWaffle,rows 5, # 列数自动调整values data,# 设置titletitle {label: 上海市污染情况,loc: center,fontdict:{fontsize: 13,}},labels [{} {:.1f}%.format(k, (v/total*100)) for k, v in data.items()],# 设置标签图例的样式legend {loc: lower left,bbox_to_anchor: (0, -0.4),ncol: len(data),framealpha: 0,fontsize: 6},dpi120
)
plt.rcParams[font.sans-serif] [Microsoft YaHei]
plt.show()上海市总体空气质量良好优和良的空气质量占比超过70%只有不到1%的严重污染中度污染和重度污染占比总和不超过10%。 2.成都市的空气质量
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pywaffle import Waffle#读入文件
cd pd.read_csv(./Chengdu.csv)
fig plt.figure(dpi100,figsize(5,5))def good(pm):#优degree []for i in pm:if 0 i 35:degree.append(i)return degree
def moderate(pm):#良degree []for i in pm:if 35 i 75:degree.append(i)return degree
def lightlyP(pm):#轻度污染degree []for i in pm:if 75 i 115:degree.append(i)return degree
def moderatelyP(pm):#中度污染degree []for i in pm:if 115 i 150:degree.append(i)return degree
def heavilyP(pm):#重度污染degree []for i in pm:if 150 i 250:degree.append(i)return degree
def severelyP(pm):#严重污染degree []for i in pm:if 250 i:degree.append(i)return degreedef PM(cd,str3):cd_dist_pm cd.loc[:, [str3]]cd_dist1_pm cd_dist_pm.dropna(axis0, subset[str3])cd_dist1_pm np.array(cd_dist1_pm[str3])cd_good_count len(good(cd_dist1_pm))cd_moderate_count len(moderate(cd_dist1_pm))cd_lightlyP_count len(lightlyP(cd_dist1_pm))cd_moderatelyP_count len(moderatelyP(cd_dist1_pm))cd_heavilyP_count len(heavilyP(cd_dist1_pm))cd_severelyP_count len(severelyP(cd_dist1_pm))a {优:cd_good_count,良:cd_moderate_count,轻度污染:cd_lightlyP_count,中度污染:cd_moderatelyP_count,重度污染:cd_heavilyP_count,严重污染:cd_severelyP_count}pm pd.DataFrame(pd.Series(a),columns[daysum])pm pm.reset_index().rename(columns{index:level})return pm
#成都
#PM_Caotangsi列
cd_cts PM(cd,PM_Caotangsi)
PMday_Caotangsi np.array(cd_cts[daysum])
#PM_Shahepu列
cd_shp PM(cd,PM_Shahepu)
PMday_Shahepu np.array(cd_shp[daysum])
cd_pm_daysum (PMday_ShahepuPMday_Caotangsi)/2
sum 0
for i in cd_pm_daysum:sum i
cd_pm_daysum1 np.array(cd_pm_daysum)data {优:int((cd_pm_daysum[0]/sum)*100), 良:int((cd_pm_daysum[1]/sum)*100), 轻度污染: int(cd_pm_daysum[2]/sum*100),中度污染:int((cd_pm_daysum[3]/sum)*100),重度污染:int((cd_pm_daysum[4]/sum)*100),严重污染:int((cd_pm_daysum[5]/sum)*100)}
total np.sum(list(data.values()))
plt.figure(FigureClassWaffle,rows 5, # 列数自动调整values data,# 设置titletitle {label: 成都市污染情况,loc: center,fontdict:{fontsize: 13,}},labels [{} {:.1f}%.format(k, (v/total*100)) for k, v in data.items()],# 设置标签图例的样式legend {loc: lower left,bbox_to_anchor: (0, -0.4),ncol: len(data),framealpha: 0,fontsize: 6},dpi120
)
plt.rcParams[font.sans-serif] [Microsoft YaHei]
plt.show()成都市总体空气质量较差空气污染程度占比约35%——其中轻度污染占比约17%中度污染占比约8%重度污染占比约8%严重污染占比约2%。 【沈阳市空气质量情况详见下期】
