Python 月次・年次データ集計（雨量）

日雨量を月・年毎に合計したもの。 各月の雨量の単位は mm/momth （月累計雨量）、 各年の雨量の単位は mm/year（年累計雨量）である。

#==============================
# Rainfall
# Dayly => Monthly, Yearly data
#==============================
import pandas as pd
import numpy as np
import datetime


def rdata():
    fnameR='xls_rdata_20190630.xlsx' # input file name
    sname='daily'
    df=pd.read_excel(fnameR, sheet_name=sname,header=0, index_col=0) # read excel data
    df.index = pd.to_datetime(df.index, format='%Y/%m/%d')
    return df    
    

def cal_y(rf,lyyy):
    rfy=np.zeros(len(lyyy),dtype=np.float)
    for i,ss in enumerate(lyyy):
        rfy[i]=rf[ss].sum() # yearly rainfall
    return rfy


def cal_m(rf,lyyy,lmmm):
    rfm=np.zeros((len(lyyy),len(lmmm)+1),dtype=np.float)
    for i,yy in enumerate(lyyy):
        for j,mm in enumerate(lmmm):
            ss=yy+'/'+mm
            try:
                rfm[i,j]=rf[ss].sum() # monthly rainfall
            except KeyError:
                rfm[i,j]=np.nan
        rfm[i,-1]=np.sum(rfm[i,:])
    return rfm


def rf_year(lyy,la,df0):
    rfy=np.zeros((len(lyy),len(la)),dtype=np.float64)
    for i,ss in enumerate(la):
        rf=pd.Series(df0[ss], index=df0.index)
        rfy[:,i]=cal_y(rf,lyy)

    ss='{0:4s}'.format('Year')
    for k in [1,2,3,4,5,6,7]:
        ss=ss+',{0:>8s}'.format('Area-'+str(k))
    ss=ss+',{0:>8s}'.format('Dam')
    ss=ss+',{0:>8s}'.format('Teromu')
    print(ss)
    for j,yy in enumerate(lyy):
        ss='{0:4s}'.format(yy)
        for i in range(len(la)):
            ss=ss+',{0:8.0f}'.format(rfy[j,i])
        print(ss)               
    ss='{0:4s}'.format('Ave.')
    for i in range(len(la)):
        ss=ss+',{0:8.0f}'.format(np.mean(rfy[:,i]))
    print(ss)


def rf_mon(lyy,lmm,rf):
    rfm=cal_m(rf,lyy,lmm)
    ss='{0:4s}'.format('Year')
    for m in ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aig','Sep','Oct','Nov','Dec','Sum']:
        ss=ss+',{0:>6s}'.format(m)
    print(ss)
    for i,yy in enumerate(lyy):
        ss='{0:4s}'.format(yy)
        for j in range(len(lmm)+1):
            ss=ss+',{0:6.0f}'.format(rfm[i,j])
        print(ss)               
    ss='{0:4s}'.format('Ave.')
    for j in range(len(lmm)+1):
        ss=ss+',{0:6.0f}'.format(np.nanmean(rfm[:,j]))
    print(ss)


def main():
    df0=rdata() # daily rainfall at each area (2000.03-2019.06)
    lyy=['2001','2002','2003','2004','2005','2006','2007','2008','2009',
         '2010','2011','2012','2013','2014','2015','2016','2017','2018']
    la=['area1','area2','area3','area4','area5','area6','area7','Dam','Teromu']
    rf_year(lyy,la,df0)
    
    lyy=['2000','2001','2002','2003','2004','2005','2006','2007','2008','2009',
         '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019']
    lmm=['01','02','03','04','05','06','07','08','09','10','11','12']

    print('* Monthly rainfall at Dam')
    rf=pd.Series(df0['Dam'], index=df0.index)
    rf_mon(lyy,lmm,rf)

    print('* Monthly rainfall at Teromu')
    rf=pd.Series(df0['Teromu'], index=df0.index)
    rf_mon(lyy,lmm,rf)

        
#==============
# Execution
#==============
if __name__ == '__main__': main()

以　上