肇庆网站建设方案外包,拓客团队怎么联系,网站建设项目经理,做gif的网站目录 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;使用华夫图分析各个城市的情况 1.北京市的空气质量 2.广州市的空气质量 【上海市和成都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… 目录 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、成都市使用华夫图分析各个城市的情况 1.北京市的空气质量 2.广州市的空气质量 【上海市和成都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、成都市使用华夫图分析各个城市的情况
1.北京市的空气质量
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pywaffle import Waffle
#读入文件
bj pd.read_csv(./Beijing.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 degree
def PM(bj,str3):bj_dist_pm bj.loc[:, [str3]]bj_dist1_pm bj_dist_pm.dropna(axis0, subset[str3])bj_dist1_pm np.array(bj_dist1_pm[str3])bj_good_count len(good(bj_dist1_pm))bj_moderate_count len(moderate(bj_dist1_pm))bj_lightlyP_count len(lightlyP(bj_dist1_pm))bj_moderatelyP_count len(moderatelyP(bj_dist1_pm))bj_heavilyP_count len(heavilyP(bj_dist1_pm))bj_severelyP_count len(severelyP(bj_dist1_pm))a {优:bj_good_count,良:bj_moderate_count,轻度污染:bj_lightlyP_count,中度污染:bj_moderatelyP_count,重度污染:bj_heavilyP_count,严重污染:bj_severelyP_count}pm pd.DataFrame(pd.Series(a),columns[daysum])pm pm.reset_index().rename(columns{index:level})return pm
#北京
#PM_Dongsi列
bj_ds PM(bj,PM_Dongsi)
PMday_Dongsi np.array(bj_ds[daysum])
#PM_Dongsihuan列
bj_dsh PM(bj,PM_Dongsihuan)
PMday_Dongsihuan np.array(bj_dsh[daysum])
#PM_Nongzhanguan列
bj_nzg PM(bj,PM_Nongzhanguan)
PMday_Nongzhanguan np.array(bj_nzg[daysum])
bj_pm_daysum (PMday_DongsiPMday_DongsihuanPMday_Nongzhanguan)/3
sum 0
for i in bj_pm_daysum:sum i
bj_pm_daysum1 np.array(bj_pm_daysum)data {优:int((bj_pm_daysum[0]/sum)*100), 良:int((bj_pm_daysum[1]/sum)*100), 轻度污染: int(bj_pm_daysum[2]/sum*100),中度污染:int((bj_pm_daysum[3]/sum)*100),重度污染:int((bj_pm_daysum[4]/sum)*100),严重污染:int((bj_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: 北京市污染情况,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()北京市总体空气质量差有约16%的轻度污染约12%的重度污染和6%的严重污染中度污染的比例也相对较大占比约9%。 2.广州市的空气质量
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pywaffle import Waffle
import math
#读入文件
gz pd.read_csv(./Guangzhou.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(gz,str3):gz_dist_pm gz.loc[:, [str3]]gz_dist1_pm gz_dist_pm.dropna(axis0, subset[str3])gz_dist1_pm np.array(gz_dist1_pm[str3])gz_good_count len(good(gz_dist1_pm))gz_moderate_count len(moderate(gz_dist1_pm))gz_lightlyP_count len(lightlyP(gz_dist1_pm))gz_moderatelyP_count len(moderatelyP(gz_dist1_pm))gz_heavilyP_count len(heavilyP(gz_dist1_pm))gz_severelyP_count len(severelyP(gz_dist1_pm))a {优:gz_good_count,良:gz_moderate_count,轻度污染:gz_lightlyP_count,中度污染:gz_moderatelyP_count,重度污染:gz_heavilyP_count,严重污染:gz_severelyP_count}pm pd.DataFrame(pd.Series(a),columns[daysum])pm pm.reset_index().rename(columns{index:level})return pm
#广州
#PM_City Station列
gz_cs PM(gz,PM_City Station)
PMday_CityStation np.array(gz_cs[daysum])
#PM_5th Middle School列
gz_ms PM(gz,PM_5th Middle School)
PMday_5thMiddleSchool np.array(gz_ms[daysum])
gz_pm_daysum (PMday_CityStationPMday_5thMiddleSchool)/2
sum 0
for i in gz_pm_daysum:sum i
gz_pm_daysum1 np.array(gz_pm_daysum)data {优:int((gz_pm_daysum[0]/sum)*100), 良:int((gz_pm_daysum[1]/sum)*100), 轻度污染: int(gz_pm_daysum[2]/sum*100),中度污染:int((gz_pm_daysum[3]/sum)*100),重度污染:int((gz_pm_daysum[4]/sum)*100),严重污染:int((gz_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: 广州市污染情况,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()广州市总体空气质量优秀优和良的空气质量占比超过80%严重污染的天气情况少之甚少污染天气——轻度污染、中度污染、重度污染和严重污染占比总和不超过20%。 【上海市和成都市空气质量情况详见下期】
