<aside> 📌 Task : 결측치 & 이상치 통합

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• 세 개의 데이터프레임을 병합

  
  df_merged = pd.merge(df_user, df_act, on='user_id', how='inner')
  df_merged = pd.merge(df_merged, df_stat, on='user_id', how='inner')
  
  

user_demographics

• birthday : null 값 채우기 → 평균값

  df_user['birthday_filled']= pd.to_datetime(df_user['birthday'])
  birth_avg = df_user['birthday_filled'].mean()
  df_user['birthday_filled']= df_user['birthday_filled'].fillna(birth_avg)
  

  df_user['birthday_filled']= pd.to_datetime(df_user['birthday'])
  df_user['birthday'].dtype  # datetime64[ns]여야 정상
  df_user['birthday'].isna().sum()  # NaT 개수 확인 -> 20 
  
  birth_avg = df_user['birthday'].mean() # 평균 생일로 결측치 대체 
  
  df_user['birthday_filled_mean']= df_user['birthday'].fillna(birth_avg) 
  
  df_user['birthday_filled_mean'].isnull().sum()
  

• gender : 카테고리 통일하기 → 소문자

  df_user['user_demographics_gender'] = df_user['gender'].str.lower().str.strip().tolist()
  

  df_user['gender'].value_counts()
  
  df_user['gender'].unique()
  
  df_user['gender'] = df_user['gender'].str.lower().str.strip().tolist()
  
  df_user['gender'].value_counts()
  

• theme_mode : 카테고리 통일하기 → 소문자

  df_user['theme_mode'] = df_user['theme_mode'].replace({
      'customized' : 'custom', 
      'dark_mode' : 'dark',
      'Light' : 'light'
      })
  

  df_user['theme_mode'].value_counts()
  
  df_user['theme_mode'].unique()
  
  df_user['theme_mode'] = df_user['theme_mode'].replace({
      'customized' : 'custom', 
      'dark_mode' : 'dark',
      'Light' : 'light'
      })
      
  df_user['theme_mode'].value_counts()
  

book_reading_status

• dropout_reason_detail : 오타 수정 → 데이터 삽입

  df_stat['dropout_reason_detail_filled'] = df_stat['dropout_reason_detail'].fillna(df_stat['dropout_reason_category'])
  df_stat['dropout_reason_detail_filled'] = df_stat['dropout_reason_detail_filled'].replace({'금한일' :'급한일'})
  

  df_stat.isnull().sum()
  
  df_stat['dropout_reason_detail_filled'] = df_stat['dropout_reason_detail'].fillna(df_stat['dropout_reason_category'])
  
  df_stat['dropout_reason_detail_filled'].unique()
  
  df_stat['dropout_reason_detail_filled'] = df_stat['dropout_reason_detail_filled'].replace({'금한일' :'급한일'})
  

user_activity

• last_access_timestamp → 평균값

  df_act['last_access_timestamp'] = pd.to_datetime(df_act['last_access_timestamp'], format='%Y.%m.%d %H:%M', errors='coerce')
  timestamp_avg = df_act['last_access_timestamp'].mean()
  df_act['last_access_timestamp_filled']= df_act['last_access_timestamp'].fillna(timestamp_avg)
  

  df_act['last_access_timestamp'].dtype  # datetime64[ns]여야 정상
  
  df_act['last_access_timestamp'] = pd.to_datetime(df_act['last_access_timestamp'], format='%Y.%m.%d %H:%M', errors='coerce')
  
  df_act['last_access_timestamp'].isna().sum()  # NaT 개수 확인 -> 20 
  
  timestamp_avg = df_act['last_access_timestamp'].mean() # 평균 접속날짜로 결측치 대체 
  df_act['last_access_timestamp_filled']= df_act['last_access_timestamp'].fillna(timestamp_avg)
  

<aside> 📌 개인별 이상치 처리 방법

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• 노시현
• 윤수민
• 정준영
• 김승인
• 임문수