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In this article, the editor introduces "python application case analysis" in detail, the content is detailed, the steps are clear, and the details are handled properly. I hope this "python application case analysis" article can help you solve your doubts.

In this quarter, how many new customers of milk powder have been driven by buying probiotics?

# naifen_vipflow.columnsss1= naifen_vipflow [['flow_no','shopid_cardid','item_name']] .rename (columns= {' item_name':'item_name_naifen'}) ss2 = ysj_vipflow [['flow_no','item_name']] .rename (columns= {' item_name':'item_name_ysj'}) flow_no_naifen_ysj= ss1.merge (ss2, on='flow_no') # orders and members for simultaneous purchase of probiotics and milk powder

# flow_no_naifen_ysjformer_quarter_start_end= ['2019-10-01 00 groupby_list_vip groupby_list_vip = [' shopid_cardid','shopid_branch',' Rank'] groupby_list_branch= ['2020-01-01 00 groupby_list_vip] 'Rank'] # naifen_vipflow.columns# milk powder new customer # each member buys for the first time That is, the new customer's pipelining saleflow= naifen_vipflowsaleflow_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]))] # the new customers in the next quarter. That is, the current quarter after_new = saleflow_first [(saleflow_first ['date_1st'] pd.to_datetime (after_quarter_start_ end [0])] # selects the flow of the next quarter, that is, the current quarter after_flow = saleflow [(saleflow [' oper_date'] pd.to_datetime (after_quarter_start_ end [0]))] # # the new customers in the next quarter Bought the order number after_new_naifen_ysj= after_new.merge (flow_no_naifen_ysj) #. Shopid_cardid.nunique () of milk powder and probiotics at the same time.

Organize it into a function

Def ysj_naifen_new (ysj_hsy_vipflow, naifen_vipflow, former_quarter_start_end=], groupby_list_vip = ['2019-10-01 00lv 00L'], after_quarter_start_end= ['2020-01-01 0000RV 00L'], groupby_list_vip = [''shopid_cardid'' 'shopid_branch',' Rank']: "" Parameter: ysj_hsy_vipflow: member flow of probiotics naifen_vipflow: milk powder member flow former_quarter_start_end: range of the previous time period after_quarter_start_end: range of the next time period groupby_list_vip: member level grouping Included in groupby () That is, groupby (groupby_list_vip) "" ss1= naifen_vipflow [['flow_no','shopid_cardid','item_name']] .rename (columns= {' item_name':'item_name_naifen'}) ss2 = ysj_vipflow [['flow_no','item_name']] .rename (columns= {' item_name':'item_name_ysj'}) flow_no_naifen_ysj= ss1.merge (ss2 On='flow_no') # order number saleflow= naifen_vipflow 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])] # New customers in the second quarter after_new = saleflow_first [(saleflow_first ['date_1st'] pd.to_datetime (after_quarter_start_ end [0]))] # pick out the pipelining after_flow = saleflow in the next quarter [(saleflow [' oper_date'] pd.to_datetime (after_quarter_start_end [0])) )] # among the new customers in the first quarter Bought milk powder and probiotics after_new_naifen_ysj= after_new.merge (flow_no_naifen_ysj) #. Shopid_cardid.nunique () return after_new_naifen_ysj

Count the id of members in the result

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