调整认知的得分

CHARLS_P/CHARLS_preprocess_main.py

@@ -134,12 +134,12 @@ if __name__ == "__main__":
     data_2011 = pd.merge(data_2011, health_status_select, on = ["ID", 'householdID', 'communityID'], how="left")
 
     
-    #计算认知功能得分，分成三部分：电话问卷10分，词语回忆20分、画图1分
+    #计算认知功能得分，分成三部分：电话问卷9分，词语回忆20分、画图1分
     health_status["dc001s1_score"] = health_status["dc001s1"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
     health_status["dc001s2_score"] = health_status["dc001s2"].apply(lambda x : 1 if x==2 else 0 if pd.isna(x) else 0)
     health_status["dc001s3_score"] = health_status["dc001s3"].apply(lambda x : 1 if x==3 else 0 if pd.isna(x) else 0)
     health_status["dc002_score"] = health_status["dc002"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
-    health_status["dc003_score"] = health_status["dc003"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
+    # health_status["dc003_score"] = health_status["dc003"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
     health_status["dc019_score"] = health_status["dc019"].apply(lambda x : 1 if x==93 else 0 if pd.isna(x) else 0) 
     health_status["dc020_score"] = health_status["dc020"].apply(lambda x : 1 if x==86 else 0 if pd.isna(x) else 0) 
     health_status["dc021_score"] = health_status["dc021"].apply(lambda x : 1 if x==79 else 0 if pd.isna(x) else 0)
@@ -354,12 +354,12 @@ if __name__ == "__main__":
     
     data_2013 = pd.merge(data_2013, health_status_select, on = ["ID", 'householdID', 'communityID'], how="left")
 
-    #计算认知功能得分，分成三部分：电话问卷10分，词语回忆10分、画图1分
+    #计算认知功能得分，分成三部分：电话问卷9分，词语回忆10分、画图1分
     health_status["dc001s1_score"] = health_status["dc001s1"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
     health_status["dc001s2_score"] = health_status["dc001s2"].apply(lambda x : 1 if x==2 else 0 if pd.isna(x) else 0)
     health_status["dc001s3_score"] = health_status["dc001s3"].apply(lambda x : 1 if x==3 else 0 if pd.isna(x) else 0)
     health_status["dc002_score"] = health_status["dc002"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
-    health_status["dc003_score"] = health_status["dc003"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
+    # health_status["dc003_score"] = health_status["dc003"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
     health_status["dc019_score"] = health_status["dc019"].apply(lambda x : 1 if x==93 else 0 if pd.isna(x) else 0) 
     health_status["dc020_score"] = health_status["dc020"].apply(lambda x : 1 if x==86 else 0 if pd.isna(x) else 0) 
     health_status["dc021_score"] = health_status["dc021"].apply(lambda x : 1 if x==79 else 0 if pd.isna(x) else 0)
@@ -550,12 +550,12 @@ if __name__ == "__main__":
     
     data_2015 = pd.merge(data_2015, health_status_select, on = ["ID", 'householdID', 'communityID'], how="left")
 
-    #计算认知功能得分，分成三部分：电话问卷10分，词语回忆10分、画图1分
+    #计算认知功能得分，分成三部分：电话问卷9分，词语回忆10分、画图1分
     health_status["dc001s1_score"] = health_status["dc001s1"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
     health_status["dc001s2_score"] = health_status["dc001s2"].apply(lambda x : 1 if x==2 else 0 if pd.isna(x) else 0)
     health_status["dc001s3_score"] = health_status["dc001s3"].apply(lambda x : 1 if x==3 else 0 if pd.isna(x) else 0)
     health_status["dc002_score"] = health_status["dc002"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
-    health_status["dc003_score"] = health_status["dc003"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
+    # health_status["dc003_score"] = health_status["dc003"].apply(lambda x : 1 if x==1 else 0 if pd.isna(x) else 0)
     health_status["dc019_score"] = health_status["dc019"].apply(lambda x : 1 if x==93 else 0 if pd.isna(x) else 0) 
     health_status["dc020_score"] = health_status["dc020"].apply(lambda x : 1 if x==86 else 0 if pd.isna(x) else 0) 
     health_status["dc021_score"] = health_status["dc021"].apply(lambda x : 1 if x==79 else 0 if pd.isna(x) else 0)
@@ -723,12 +723,12 @@ if __name__ == "__main__":
 
     data_2018 = pd.merge(data_2018, health_status_select, on = ["ID", 'householdID', 'communityID'], how="left")
 
-    #计算认知功能得分，分成三部分：电话问卷10分，词语回忆10分、画图1分
+    #计算认知功能得分，分成三部分：电话问卷9分，词语回忆10分、画图1分
     cognition["dc001s1_score"] = cognition["dc001_w4"].apply(lambda x : 1 if x==1 else 0 if x==5 else np.nan)
     cognition["dc001s2_score"] = cognition["dc006_w4"].apply(lambda x : 1 if x==1 else 0 if x==5 else np.nan)
     cognition["dc001s3_score"] = cognition["dc003_w4"].apply(lambda x : 1 if x==1 else 0 if x==5 else np.nan)
     cognition["dc002_score"] = cognition["dc005_w4"].apply(lambda x : 1 if x==1 else 0 if x==5 else np.nan)
-    cognition["dc003_score"] = cognition["dc002_w4"].apply(lambda x : 1 if x==1 else 0 if x==5 else np.nan)
+    # cognition["dc003_score"] = cognition["dc002_w4"].apply(lambda x : 1 if x==1 else 0 if x==5 else np.nan)
     cognition["dc019_score"] = cognition.apply(lambda x : 0 if x["dc014_w4_1"]==97 else 1 if pd.isna(x["dc014_w4_1"]) and x["dc014_w4_1_1"]==93 else 0 if pd.isna(x["dc014_w4_1"]) and (not x["dc014_w4_1_1"]==93) else np.nan, axis=1) 
     cognition["dc020_score"] = cognition.apply(lambda x : 0 if x["dc014_w4_2"]==97 else 1 if pd.isna(x["dc014_w4_2"]) and x["dc014_w4_2_1"]==86 else 0 if pd.isna(x["dc014_w4_2"]) and (not x["dc014_w4_2_1"]==86) else np.nan, axis=1) 
     cognition["dc021_score"] = cognition.apply(lambda x : 0 if x["dc014_w4_3"]==97 else 1 if pd.isna(x["dc014_w4_3"]) and x["dc014_w4_3_1"]==79 else 0 if pd.isna(x["dc014_w4_3"]) and (not x["dc014_w4_3_1"]==79) else np.nan, axis=1)
@@ -906,7 +906,7 @@ if __name__ == "__main__":
     
     data_2020 = pd.merge(data_2020, health_status_select, on = ["ID", 'householdID', 'communityID'], how="left")
 
-    #计算认知功能得分，分成三部分：电话问卷10分，词语回忆10分、画图1分
+    #计算认知功能得分，分成三部分：电话问卷9分，词语回忆10分、画图1分
     health_status["dc001s1_score"] = health_status["dc001"].apply(lambda x : 1 if x==1 else 0 if x==2 else np.nan)
     health_status["dc001s2_score"] = health_status["dc005"].apply(lambda x : 1 if x==1 else 0 if x==2 else np.nan)
     health_status["dc001s3_score"] = health_status["dc003"].apply(lambda x : 1 if x==1 else 0 if x==2 else np.nan)