진우(여기다가 본인이 분석할 페르소나 적기)

# mid_50k_150k + new + 검색 유입 + 리뷰 클릭 X + 할인 노출 O 만 보기 import pandas as pd df5 = pd.read_csv("df5.csv") filtered_df = df5[ (df5['price_band'] == 'mid_50k_150k') & (df5['user_type'] == 'new') & (df5['traffic_source'] == 'search') & (df5['review_clicked'] == False) & (df5['discount_exposed'] == True) ] filtered_df # 상품 타입별 이탈률 보기 # 'churned' 플래그 생성: add_to_cart == 'No' 인 경우 True filtered_df = filtered_df.copy() filtered_df['churned'] = filtered_df['add_to_cart'] == 'No' # product_category별 이탈률 계산 churn_summary = ( filtered_df .groupby('product_category') .agg( total_users=('user_id', 'count'), churned_users=('churned', 'sum') ) .assign(dropout_rate=lambda x: (x['churned_users'] / x['total_users']) * 100) .reset_index() ) # 결과 출력 print(churn_summary)

# pdp # 1. 분석 타겟 필터링 target_users = df5[ (df5['price_band'] == 'mid_50k_150k') & (df5['user_type'] == 'new') & (df5['traffic_source'] == 'search') & (df5['review_clicked'] == False) & (df5['discount_exposed'] == True) ] # 2. 체류시간 + 상품 카테고리별 분석 summary = target_users.groupby(['duration_group_label', 'product_category']).agg( total_users=('user_id', 'count'), avg_pdp_duration=('pdp_duration_sec', 'mean'), churned_users=('add_to_cart', lambda x: (x == 'No').sum()), converted_users=('purchase_completed', lambda x: (x == 'Yes').sum()) ).reset_index() # 3. 비율 계산 summary['dropout_rate'] = (summary['churned_users'] / summary['total_users']) * 100 summary['conversion_rate'] = (summary['converted_users'] / summary['total_users']) * 100 # 4. 반올림 summary['avg_pdp_duration'] = summary['avg_pdp_duration'].round(1) summary['dropout_rate'] = summary['dropout_rate'].round(1) summary['conversion_rate'] = summary['conversion_rate'].round(1) summary