更改python中的索引后使用df.loc时缺少值

将索引更改为“PassengerId”，然后尝试使用

df.loc

函数根据新索引检索信息，但结果包含缺少的值

正在探索泰坦尼克号数据集

在新的_行中附加一些值

将索引更改为PassengerId

尝试使用df.loc进行搜索

获得的结果值在现有行中消失，但显示新附加行的值

得到以下结果：

NaN for all the rows except for the new_row(892)



FutureWarning: Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative`

See the documentation here:
https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike

预期结果：

    PassengerId Survived    Pclass  Name    Sex Age SibSp   Parch   Ticket  Fare    Cabin   Embarked
PassengerId                                             
890 890 1   1   Behr, Mr. Karl Howell male 26   0.0 0.0 111369  30.00 C148 C
891 891 0   3   Dooley, Mr. Patrick male 32 0.0 0.0 370376  7.75    NaN Q
892 892 0   1   NA  NA  NA  NaN NaN NaN NaN NaN NaN

部分答覆：

正在运行测试

import pandas as pd
import numpy as np
dataset= pd.DataFrame(columns=["PassengerId","Survived","Pclass","Name","Sex","Age","SibSp","Parch","Ticket","Fare","Cabin","Embarked"],data=[[891,1,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan],[892,2,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]])
print(dataset)
# Add rows
new_row=pd.Series(data=['890','0','1','NA','NA','NA'], index=['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age'])
dataset=dataset.append(new_row, ignore_index=True)

# Setting PassengerId as Index
dataset= dataset.set_index(dataset['PassengerId'])
dataset.loc[[892,891,890]]
print(dataset)

并得出以下结果：

  PassengerId  Survived  Pclass  Name  Sex  Age  SibSp  Parch  Ticket  Fare  \
0          891         1     NaN   NaN  NaN  NaN    NaN    NaN     NaN   NaN   
1          892         2     NaN   NaN  NaN  NaN    NaN    NaN     NaN   NaN   

   Cabin  Embarked  
0    NaN       NaN  
1    NaN       NaN  
            PassengerId Survived Pclass Name  Sex  Age  SibSp  Parch  Ticket  \
PassengerId                                                                    
891                 891        1    NaN  NaN  NaN  NaN    NaN    NaN     NaN   
892                 892        2    NaN  NaN  NaN  NaN    NaN    NaN     NaN   
890                 890        0      1   NA   NA   NA    NaN    NaN     NaN   

             Fare  Cabin  Embarked  
PassengerId                         
891           NaN    NaN       NaN  
892           NaN    NaN       NaN  
890           NaN    NaN       NaN 

---

似乎正是添加我提到的int类型列的值时所要查找的

“PassengerId”、“Age”等改为“892”，而不仅仅是892。这将大多数列的类型从int更改为object。删除倒逗号解决了这个问题。

但是891和892中的所有值都消失了。我希望得到一个在我的数据集中有现有值.虚拟数据的结果。我只在“幸存”列中插入了值。尽管如此，仍在试图复制您的问题…谢谢@Kryesec，我刚刚发现了我犯的错误。它解决了这个问题。在添加新行时，我提到了int类型列的值，如“PassengerId”、“Age”等，而不是仅892。这将大多数列的类型从int改为object。删除倒逗号解决了这个问题。\np！数据类型可能会把任何人的一天都搞砸。\uuuuuu u;。

import pandas as pd
import numpy as np
dataset= pd.DataFrame(columns=["PassengerId","Survived","Pclass","Name","Sex","Age","SibSp","Parch","Ticket","Fare","Cabin","Embarked"],data=[[891,1,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan],[892,2,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]])
print(dataset)
# Add rows
new_row=pd.Series(data=['890','0','1','NA','NA','NA'], index=['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age'])
dataset=dataset.append(new_row, ignore_index=True)

# Setting PassengerId as Index
dataset= dataset.set_index(dataset['PassengerId'])
dataset.loc[[892,891,890]]
print(dataset)

  PassengerId  Survived  Pclass  Name  Sex  Age  SibSp  Parch  Ticket  Fare  \
0          891         1     NaN   NaN  NaN  NaN    NaN    NaN     NaN   NaN   
1          892         2     NaN   NaN  NaN  NaN    NaN    NaN     NaN   NaN   

   Cabin  Embarked  
0    NaN       NaN  
1    NaN       NaN  
            PassengerId Survived Pclass Name  Sex  Age  SibSp  Parch  Ticket  \
PassengerId                                                                    
891                 891        1    NaN  NaN  NaN  NaN    NaN    NaN     NaN   
892                 892        2    NaN  NaN  NaN  NaN    NaN    NaN     NaN   
890                 890        0      1   NA   NA   NA    NaN    NaN     NaN   

             Fare  Cabin  Embarked  
PassengerId                         
891           NaN    NaN       NaN  
892           NaN    NaN       NaN  
890           NaN    NaN       NaN