网站开发公司 重庆,我的百度账号,微信网站模板,家居网站建设素材目录 沈阳市的空气质量 华夫图 柱状图 总结 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 沈阳市的空气质量
华夫图
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
… 目录 沈阳市的空气质量 华夫图 柱状图 总结 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 沈阳市的空气质量
华夫图
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pywaffle import Waffle
#读入文件
sy pd.read_csv(./Shenyang.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(sy,str3):sy_dist_pm sy.loc[:, [str3]]sy_dist1_pm sy_dist_pm.dropna(axis0, subset[str3])sy_dist1_pm np.array(sy_dist1_pm[str3])sy_good_count len(good(sy_dist1_pm))sy_moderate_count len(moderate(sy_dist1_pm))sy_lightlyP_count len(lightlyP(sy_dist1_pm))sy_moderatelyP_count len(moderatelyP(sy_dist1_pm))sy_heavilyP_count len(heavilyP(sy_dist1_pm))sy_severelyP_count len(severelyP(sy_dist1_pm))a {优:sy_good_count,良:sy_moderate_count,轻度污染:sy_lightlyP_count,中度污染:sy_moderatelyP_count,重度污染:sy_heavilyP_count,严重污染:sy_severelyP_count}pm pd.DataFrame(pd.Series(a),columns[daysum])pm pm.reset_index().rename(columns{index:level})return pm
#沈阳
#PM_Taiyuanjie列
sy_tyj PM(sy,PM_Taiyuanjie)
PMday_Taiyuanjie np.array(sy_tyj[daysum])
#PM_Xiaoheyan列
sy_xhy PM(sy,PM_Xiaoheyan)
PMday_Xiaoheyan np.array(sy_xhy[daysum])
sy_pm_daysum (PMday_XiaoheyanPMday_Taiyuanjie)/2
sum 0
for i in sy_pm_daysum:sum i
sy_pm_daysum1 np.array(sy_pm_daysum)
data {优:int((sy_pm_daysum[0]/sum)*100), 良:int((sy_pm_daysum[1]/sum)*100), 轻度污染: int(sy_pm_daysum[2]/sum*100),中度污染:int((sy_pm_daysum[3]/sum)*100),重度污染:int((sy_pm_daysum[4]/sum)*100),严重污染:int((sy_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: 沈阳市污染情况,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()柱状图
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt#读入文件
sy pd.read_csv(./Shenyang.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(sy,str3):sy_dist_pm sy.loc[:, [str3]]sy_dist1_pm sy_dist_pm.dropna(axis0, subset[str3])sy_dist1_pm np.array(sy_dist1_pm[str3])sy_good_count len(good(sy_dist1_pm))sy_moderate_count len(moderate(sy_dist1_pm))sy_lightlyP_count len(lightlyP(sy_dist1_pm))sy_moderatelyP_count len(moderatelyP(sy_dist1_pm))sy_heavilyP_count len(heavilyP(sy_dist1_pm))sy_severelyP_count len(severelyP(sy_dist1_pm))a {优:sy_good_count,良:sy_moderate_count,轻度污染:sy_lightlyP_count,中度污染:sy_moderatelyP_count,重度污染:sy_heavilyP_count,严重污染:sy_severelyP_count}pm pd.DataFrame(pd.Series(a),columns[daysum])pm pm.reset_index().rename(columns{index:level})return pm
#沈阳
#PM_Taiyuanjie列
sy_tyj PM(sy,PM_Taiyuanjie)
PMday_Taiyuanjie np.array(sy_tyj[daysum])
#PM_Xiaoheyan列
sy_xhy PM(sy,PM_Xiaoheyan)
PMday_Xiaoheyan np.array(sy_xhy[daysum])
sy_pm_daysum (PMday_XiaoheyanPMday_Taiyuanjie)/2
sum 0
for i in sy_pm_daysum:sum i
sy_pm_daysum1 np.array(sy_pm_daysum)
#图像
bar_width 0.1
plt.bar(0.2,sy_pm_daysum[0]/sum,widthbar_width,coloraqua,label优)
plt.bar(0.4,sy_pm_daysum[1]/sum,widthbar_width,colordeepskyblue,label良)
plt.bar(0.6,sy_pm_daysum[2]/sum,widthbar_width,colorcornflowerblue,label轻度污染)
plt.bar(0.8,sy_pm_daysum[3]/sum,widthbar_width,colorskyblue,label中度污染)
plt.bar(1,sy_pm_daysum[4]/sum,widthbar_width,colorlightsteelblue,label重度污染)
plt.bar(1.2,sy_pm_daysum[5]/sum,widthbar_width,colorsilver,label严重污染)
x [0.2,0.4,0.6,0.8,1,1.2]
for a,b in zip(x,sy_pm_daysum):plt.text(a, (b/sum) 0.02,%.1f%(b/sum*100)%, hacenter, vabottom, fontsize10)
plt.xticks([0.2,0.4,0.6,0.8,1,1.2])
plt.xlabel([优,良,轻度污染,中度污染,重度污染,严重污染])
plt.yticks([0.2,0.4,0.6,0.8,1])
plt.ylabel([20%,40%,60%,80%,100%])
plt.ylabel(u百分比,fontsize12,rotationhorizontal,verticalalignmenttop,horizontalalignmentleft, x2,y1.1)
plt.xlabel(u污染程度,fontsize12,verticalalignmenttop,horizontalalignmentleft,x0.9, y1.2)
plt.legend()
fig.suptitle(沈阳市污染情况,fontsize15,x0.5,y1)
plt.grid(alpha0.4)
plt.rcParams[font.sans-serif] [Microsoft YaHei]
plt.show() 沈阳市总体空气质量较差空气污染程度占比超过35%——其中轻度污染占比约16%中度污染占比约7%重度污染占比约8%严重污染占比约3%。
总结 总体来讲广州市的空气质量最好上海次之北京市的空气质量最差严重污染占比远远超过其他四座城市。
