datawig库在留下一些未输入的同时划分了一些列
我一直在尝试使用datawig库在数据集中估算丢失值。但是,当我使用datawig库将数据集中的丢失值算时。它在留下两列的同时,将每一列均屈服。这两个列都是dtype:对象。但是,它施加其他对象列。这是我的数据集的可视化:
df.tail(155)
import datawig
df = datawig.SimpleImputer.complete(df)
df.isnull().sum()
PassengerId 0
HomePlanet 0
CryoSleep 0
Cabin 199
Destination 0
Age 0
VIP 0
RoomService 0
FoodCourt 0
ShoppingMall 0
Spa 0
VRDeck 0
Name 200
Transported 0
dtype: int64
我不知道什么原因。同样,在应用DataWig归纳之前,名称和机舱列中缺少值的数量相同。
I have been trying to impute missing values in my dataset using datawig library. However when I use datawig library to impute the missing values in my dataset. It imputes each and every other column while leaving behind two columns. Both of the columns are of dtype: object. However, it imputes other object columns. Here is the visualization of my dataset:
df.tail(155)
The code to impute the missing values is as follows:
import datawig
df = datawig.SimpleImputer.complete(df)
These are the missing values left behind:
df.isnull().sum()
PassengerId 0
HomePlanet 0
CryoSleep 0
Cabin 199
Destination 0
Age 0
VIP 0
RoomService 0
FoodCourt 0
ShoppingMall 0
Spa 0
VRDeck 0
Name 200
Transported 0
dtype: int64
The missing values for the column named Cabin and Name were left and were not imputed for I do not know what reason. Also before applying datawig imputation the number of missing values in Name and Cabin column were the same.
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