Question

#First, I divide the age group as follow ,  
 # 1. group A: 0-17years  old;
 # 2. group B: 18-35years old 
 # 3. group C: 36-50years old 
 # 4. group D: 51-65years old 
 # 5. group E: above 66 years old 


 #Then I begin to write code extact the CVC data

 Passenger_Age={"PassengerId":titanic["PassengerId"][:],"Age":titanic["Age"][:]}

 Passenger_Age_df = pd.DataFrame(Passenger_Age,columns =["Age","PassengerId"])

Passenger_Survived={"PassengerId":titanic["PassengerId"[:],"Survived":titanic["Survived"][:]}

Passenger_Survived_df = pd.DataFrame(Passenger_Survived,columns = ["Survived","PassengerId"])

 # consider there are some NAN in Age, so wirte the blow cod to drop the Age data

cleaned_Passenger_Age_df = Passenger_Age_df.dropna()

关于下一步，我想合并两个数据框，＆＃34; cleaning_Passenger_Age_df＆＃34;和＆＃34; Passenger_Survived_df＆＃34;。
之后，使用applymap函数将年龄转换为ABCDE
然后根据那个找到幸存的年龄组的比率我的问题是smy想法很明确，但我不知道写代码，有人可以帮我吗？ THX！

Answer 1

您可以使用pd.cut()对年龄进行分组，例如：

group_names = ['A','B','C','D','E']
bins = [0,17,35,50,65,1000]
df['Age_Group'] = pd.cut(df['Age'], bins=bins, labels=group_names)

更多细节： pandas.cut

至于计算幸存率，你可以使用 group by ，例如：

df.groupby(['Age_Group','Survived']).count() /  total_numbers

分析年龄组与存活率之间的相关性

1 个答案: