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This article mainly introduces the relevant knowledge of "how python calculates the repurchase rate". The editor shows you the operation process through the actual case. The operation method is simple, fast and practical. I hope this article "how to calculate the repurchase rate of python" can help you solve the problem.

Calculate the repurchase rate of new customers in period An in period B.

Def huigou (saleflow,former_quarter_start_end= ['2019-10-01 00 groupby_list_vip groupby_list_vip = [' shopid_cardid','shopid_branch',' Rank'], groupby_list_branch= ['2019-01-01 00 def huigou], groupby_list_vip = [' shopid_cardid','shopid_branch',' Rank'], groupby_list_branch= [ 'Rank']): # each first purchase That is, the new customer's pipeline saleflow_first = saleflow.groupby (groupby_list_vip). Oper_date.min (). Reset_index (). Rename (columns= {'oper_date':'date_1st'}) # pick out the new customers in the previous quarter former_new = saleflow_first [(saleflow_first [' date_1st'] pd.to_datetime (former_quarter_start_ end [0])] # after picking out After_flow = saleflow in the first quarter [(saleflow ['oper_date'] pd.to_datetime (after_quarter_start_ end [0])] # # join shows the pipelining fugou_flow = former_new.merge (after_flow) of members in the previous quarter How='right') # count former_NEW_count=saleflow_first.groupby (groupby_list_branch). Shopid_cardid.nunique (). Reset_index ()\ .rename (columns= {'shopid_cardid':'shopid_cardid_new_count'})

After_BACK_count= fugou_flow.groupby (groupby_list_branch). Shopid_cardid.nunique (). Reset_index ().\ rename (columns= {'shopid_cardid':'shopid_cardid_back_count'}) df = former_NEW_count.merge (after_BACK_count,how='left') return df

This function can be used to calculate the repurchase rate of new customers in the following month. If you want to calculate the repurchase rate for multiple months, you need to cycle.

First generate a list of months with a month interval of 1, and the repurchase rate column name is' next_'+ str (n_space) +'_ month_rebuy_rate'

Create a new empty dataframe

# calculate the monthly return rate of each month, that is, the January return rate import arrowmonth_list = [each.format ('YYYY-MM-DD HH:mm:SS') for each in arrow.Arrow.range (' month', arrow.get ('2019-07-01 00purl 00lv 00lv 00lb HH:mm:SS') YYYYYMMMI DD HH:mm:SS') Arrow.get ('2020-05-01 00 Velcro 00D HH:mm:SS')] nasty spaceways 1huigoufang name =' next_'+ str (n_space) +'_ month_rebuy_rate'df_empty = pd.DataFrame (columns= ('pinpai',' shopid_cardid_new_count')) 'shopid_cardid_back_count',huigou_name,' current month', 'shopId'))

Let's start the loop

For i in range (len (month_list)-n_space-1):

Print (print 'current month:', month_ list [I]) ss = huigou (yyp_vipflow, former_quarter_start_end= [month _ list [I], month_ list [I + n_space]], after_quarter_start_end= [month_ list [I + n_space], month_ list [I + n_space+1]], groupby_list_vip = ['shopid_cardid','shopid_branch','pinpai'] Groupby_list_branch= ['shopid_branch','pinpai'])

New_sum = ss.groupby (['pinpai']). Shopid_cardid_new_count.sum (). Reset_index ()

Back_sum = ss.groupby (['pinpai']). Shopid_cardid_back_count.sum (). Reset_index ()

Result = new_sum.merge (back_sum) result [Huigou _ name] = result ['shopid_cardid_back_count'] / result [' shopid_cardid_new_count'] result ['current month'] = month_ list [I] [0:10] result ['shopId'] = shopId df_empty=df_empty.append (result)

If you want to calculate the repurchase rate of this month's new customers in the second month, then n_space=2

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