python求平均

如果计算同一个东西，可以思考一下批量处理。

计算日均、月均、年均，含缺测值，缺测天
求污染等级有点。。。。

import re
import sys
import numpy as np
import pandas as pd

xlsx = pd.read_excel('站点2018年1月1日-12月31日小时均值.xls','站点小时数据')
xlsx.head()  #日期（年月日小时）和6项污染物

#算PM25和PM10的比值，将大于1的设为空
xlsx['bz']=xlsx['PM2.5'].div(xlsx['PM10'])   
xlsx[xlsx['bz']>=1]
xlsx.loc[xlsx.bz >= 1, ['PM2.5','PM10','bz'] ]= np.nan

xlsx[xlsx['bz']>=1]  #复查

#切片前先排序
xlsx = xlsx.sort_index(level=0)

#把年月日挑出来
xlsx['year']=pd.to_datetime(xlsx['时间']).dt.year
xlsx['month']=pd.to_datetime(xlsx['时间']).dt.month
xlsx['day']=pd.to_datetime(xlsx['时间']).dt.day
xlsx['hour']=pd.to_datetime(xlsx['时间']).dt.hour

xlsx.drop(['城市名称','区县名称','站点名称','时间'],axis=1,inplace=True)
xlsx.drop([0,], inplace=True)  #第一行不在时间范围内，去掉
xlsx.head()

xlsx = xlsx.set_index([xlsx['year'],xlsx['month'],xlsx['day'],xlsx['hour']])    #设置多重索引
xlsx.drop(['year','month','day','hour'],axis=1,inplace=True)
xlsx.head()

# 算日均值
day_data = xlsx.mean(level=[0,1,2])
day_data.columns=["PM10","SO2","NO2","CO","O3","PM25","bz"]
day_data.to_csv('day_data.csv', index=True, sep=',')

#计算污染等级天数
pm11=day_data[["PM10","PM25",'bz']][(day_data.PM10<=50)]
pm11.count(level=0)

pm12=day_data[["PM10","PM25",'bz']][(day_data.PM10>50)&(day_data.PM10<=150)]
pm12.count(level=0)

pm13=day_data[["PM10","PM25",'bz']][(day_data.PM10>150)&(day_data.PM10<=250)]
pm13.count(level=0)

pm14=day_data[["PM10","PM25",'bz']][(day_data.PM10>250)&(day_data.PM10<=350)]
pm14.count(level=0)

pm15=day_data[["PM10","PM25",'bz']][(day_data.PM10>350)&(day_data.PM10<=420)]
pm15.count(level=0)

pm16=day_data[["PM10","PM25",'bz']][(day_data.PM10>420)]
pm16.count(level=0)


pm21=day_data[["PM10","PM25",'bz']][(day_data.PM25<=35)]
pm21.count(level=0)

pm22=day_data[["PM10","PM25",'bz']][(day_data.PM25>35)&(day_data.PM25<=75)]
pm22.count(level=0)

pm23=day_data[["PM10","PM25",'bz']][(day_data.PM25>75)&(day_data.PM25<=115)]
pm23.count(level=0)

pm24=day_data[["PM10","PM25",'bz']][(day_data.PM25>115)&(day_data.PM25<=150)]
pm24.count(level=0)

pm25=day_data[["PM10","PM25",'bz']][(day_data.PM25>150)&(day_data.PM25<=250)]
pm25.count(level=0)

pm26=day_data[["PM10","PM25",'bz']][(day_data.PM25>250)]
pm26.count(level=0)

#算月均
mon_data = xlsx.mean(level=[0,1])
mon_data.to_csv('月均.csv', index=True, sep=',')
mon_data

#算年均
year_data = xlsx.mean(level=0)
year_data.to_csv('年均.csv', index=False, sep=',')
year_data

#算小时平均
hour_data = xlsx.mean(level=[0,1,3])
hour_data.to_csv('小时平均.csv', index=True, sep=',')
hour_data

hour_spr=hour_data.loc[(slice(2018,2018),slice(3,5), slice(None)), :].mean(level=2)
hour_spr.to_csv('春季.csv', index=True, sep=',')
hour_spr

hour_sum=hour_data.loc[(slice(2018,2018),slice(6,8), slice(None)), :].mean(level=2)
hour_sum.to_csv('夏季.csv', index=True, sep=',')
hour_sum

hour_aut=hour_data.loc[(slice(2018,2018),slice(9,11), slice(None)), :].mean(level=2)
hour_aut.to_csv('秋季.csv', index=True, sep=',')
hour_aut

win1=hour_data.loc[(slice(2018,2018), slice(12,12), slice(None)), :].mean(level=2)
win2 = hour_data.loc[(slice(2019,2019), slice(1,2), slice(None)), :].sum(level=2)
win=(win1+win2)/3
win.to_csv('冬季.csv', index=True, sep=',')

计算臭氧滑动平均

import re
import sys
import numpy as np
import pandas as pd

S1 = pd.read_excel('190308.S1','Sheet1') #时间、臭氧
S1.head()

S1['year']=pd.to_datetime(S1['时间']).dt.year
S1['month']=pd.to_datetime(S1['时间']).dt.month
S1['day']=pd.to_datetime(S1['时间']).dt.day
S1['hour']=pd.to_datetime(S1['时间']).dt.hour

S1 = S1.set_index([S1['year'],S1['month'],S1['day'],S1['hour']])
S1.drop(['二氧化硫','PM10','时间','year','month','day','hour','二氧化氮','一氧化碳','PM25'],axis=1,inplace=True)
S1.head()

#初始滑动平均
O3_pre1=S1.rolling(window=8).mean()

#上移一位
O3_pre2=O3_pre1.shift(-1)  #上调一位

#算日均最大8小时平均
O3_f=O3_pre2.loc[(slice(None), slice(None),slice(None),slice(7,23)), :].max(level=[0,1,2])
O3_f.to_csv('臭氧日最大8小时滑动平均.csv', index=True, sep=',')

O3_f[O3_f>160].count(level=1)

#算月均、年均
monave=O3_f.mean(level=[0,1])
monave.to_csv('臭氧8小时滑动平均月均.csv', index=True, sep=',')
monave

yearave=O3_f.mean(level=[0])
yearave.to_csv('臭氧8小时滑动平均年均.csv', index=True, sep=',')
yearave

Ospr=SO1.loc[(slice(2018,2018),slice(3,5), slice(None)), :].mean(level=0)
Ospr.to_csv('春季.csv', index=True, sep=',')

Osum=SO1.loc[(slice(2018,2018),slice(6,8), slice(None)), :].mean(level=0)
Osum.to_csv('夏季.csv', index=True, sep=',')

Oaut=SO1.loc[(slice(2018,2018),slice(9,11), slice(None)), :].mean(level=0)
Oaut.to_csv('秋季.csv', index=True, sep=',')
Oaut