Joining multiple columns and coalesce and when, then cases ('pace_&YrMn.'- simple variable )

Question

create table pace_&YrMn. as
select a.hotel_cd, a.blk_dt, 
       case when a.arr_date <= '31DEC09'd then coalesce(c.mkt_seg_dsc, m.mkt_seg_dsc1) 
            else coalesce(s.quoteaccountmarketsegment, c.mkt_seg_dsc, m.mkt_seg_dsc1)
            end as mkt_seg_dsc,

       case when a.blk_dt-c.definite_dt<=365 then'  0-365'
            when a.blk_dt-c.definite_dt<=730 then'366-730'
            else '730+   '
            end as bkg_window format=$7.,

       case when a.blk_dt <= &monend. then 'in_pd'
            when a.blk_dt-&monend.<=365 then'  0-365'
            when a.blk_dt-&monend.<=730 then'366-730'
                                       else'730+   '
            end as arr_window format=$7.,

       case when b.peak_rm<=100 then'  1-100 '
            when b.peak_rm<=300 then'101-300 '
            when b.peak_rm<=500 then'301-500 '
            when b.peak_rm<=1000 then'501-1000'
            else'1000+   '
            end as peak_rm format=$8.,

       sum(proj_rms) as ty_rm, sum(proj_rms_rev) as ty_rev
       
from Bk_Rmblk_&YrMn. a
     inner join Bk_Rmblk_&YrMn._peak b
     on a.hotel_cd=b.hotel_cd
     and a.book_key=b.book_key
     inner join share.Bkdata2006 c
     on a.hotel_cd=c.hotel_cd
     and a.book_key=c.book_key
     left join share.mktsegcodes m
     on c.mkt_seg=m.mkt_seg
     left join share_gd.marketsegmentcurrent s
     on a.hotel_cd=s.marshacode
     and a.book_key=s.quoteid
where c.definite_dt <= &monend./*back out new bkg during the period end weekend*/
and not(c.lst_reas in ('ENTRY ERROR','CONVERSION DUPLICATE','SYSTEM UPDATE'))
group by a.hotel_cd, a.blk_dt, calculated mkt_seg_dsc, calculated bkg_window, calculated arr_window, calculated peak_rm;

so basically it is used in sas language through proc sql and now being converted into python dataframe which im unable to various error coming, please help
the result should be codes in python dataframes in place of tables

Asked By: Zafar Imam

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Source

Answer 1

This is the python pandas code:

a = globals()['Bk_Rmblk_'+YrMn]
b = globals()['Bk_Rmblk_'+YrMn._peak]
c = globals()[share.Bkdata2006]
m = share.mktsegcodes
s = share_gd.marketsegmentcurrent

x = a[['hotel','blk_dt']] 

globals()['pace_'+YrMn] = x[ x['arr_date'] <= '31DEC09'd]


r = globals()['pace_'+YrMn]
r = a[['hotel', 'blk_dt']].merge(b, how='inner', left_on=['hotel_cd', 'book_key'], right_on=['hotel_cd', 'book_key']).merge(c, left_on=['hotel_cd', 'book_key'], right_on=['hotel_cd','book_key'], how = 'inner').merge(m, on='mkt_seg', how='left').merge(s, left_on=['hotel', 'book_key'], right_on=['marshacode', 'quoteid'], how='left')

# back out new bkg during the period end weekend
# if else condition
r['mkt_seg_dsc'] =np.where((r['arr_date']<= '31DEC09', r.mkt_seg_dsc.combine_first(r.mkt_seg_dsc1), r.quoteaccountmarketsegment.combine_first(r.mkt_seg_dsc.combine_first(r.mkt_seg_dsc1)))

r['bkg_window'] =np.where((r['arr_date']- r['definite_df']<= 365, '0-365', np.where(r['blk_dt'] - r['definie_dt'] <= 730, '366-730', '730+')))

r['arr_window'] = np.where((r['blk_dt'] <= monend, 'in_pd', np.where(r['blk_dt'] - monend <= 365, '0-365', np.where(r['blk_dt']- monend <= 730,'366-730', '730+')))

r['peak_rm'] = np.where(r['peak_rm'] <= 100, '1-100', np.where(r['peak_rm']<= 300, '101-300', np.where(r['peak_rm'] <= 500, '301-500', np.where(r['peak_rm']<=10000, '501-1000','1000+'))))

r = r.groupby(['hotel_cd', 'blk_dt', 'mkt_seg_dsc', 'bkg_window', 'arr_window', 'peak_rm']).agg({'proj_rms':'sum','proj_rms_rev':'sum'})
         
r.rename(columns = {'proj_rms':'ty_rm'}, inplace = True)
  
r.rename(columns = {'proj_rms_rev': 'ty_rev'})
                           
 
r = r[r['definite_dt'] <= monend]
r = r[r['lst_reas'] not in  ['ENTRY ERROR','CONVERSION DUPLICATE','SYSTEM UPDATE']]

                           
# final dataframe
globals()['pace_'+YrMn] = r['hotel_cd', 'blk_dt', 'mkt_seg_dsc', 'bkg_window', 'arr_window', 'peak_rm', 'ty_rm', 'ty_rev']

Answered By: Zafar Imam

Joining multiple columns and coalesce and when, then cases ('pace_&YrMn.'- simple variable )

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