英文:
sklearn.impute.SimpleImputer: Unable to fill in the most common value for a list of dataframe columns
问题
以下是代码的翻译部分：
# 我有一个数据框的列列表，其中包含 NA 值（如下所示）。所有这些列的 'dtype' 都是 'str'。
X_train_objects = ['HomePlanet',
'Destination',
'Name',
'Cabin_letter',
'Cabin_number',
'Cabin_letter_2']
# 我想使用 'SimpleImputer' 来填充 NA 值，使用最常见的值（众数）。但是，我遇到了 'ValueError: Columns must be same length as key'。这是为什么，我的代码对我来说似乎是正确的？
# 数据框示例（名为 'X_train'）中 'Destination' 列的示例为 'np.NA'：
{
'PassengerId': {47: '0045_02',
128: '0138_02',
139: '0152_01',
347: '0382_01',
430: '0462_01'},
'HomePlanet': {47: 'Mars',
128: 'Earth',
139: 'Earth',
347: nan,
430: 'Earth'},
'CryoSleep': {47: 1, 128: 0, 139: 0, 347: 0, 430: 1},
'Destination': {47: nan, 128: nan, 139: nan, 347: nan, 430: nan},
'Age': {47: 19.0, 128: 34.0, 139: 41.0, 347: 23.0, 430: 50.0},
'VIP': {47: 0, 128: 0, 139: 0, 347: 0, 430: 0},
'RoomService': {47: 0.0, 128: 0.0, 139: 0.0, 347: 348.0, 430: 0.0},
'FoodCourt': {47: 0.0, 128: 22.0, 139: 0.0, 347: 0.0, 430: 0.0},
'ShoppingMall': {47: 0.0, 128: 0.0, 139: 0.0, 347: 0.0, 430: 0.0},
'Spa': {47: 0.0, 128: 564.0, 139: 0.0, 347: 4.0, 430: 0.0},
'VRDeck': {47: 0.0, 128: 207.0, 139: 607.0, 347: 368.0, 430: 0.0},
'Name': {47: 'Mass Chmad',
128: 'Monah Gambs',
139: 'Andan Estron',
347: 'Blanie Floydendley',
430: 'Ronia Sosanturney'},
'Transported': {47: 1, 128: 0, 139: 0, 347: 0, 430: 0},
'Cabin_letter': {47: 'F', 128: 'E', 139: 'F', 347: 'G', 430: 'G'},
'Cabin_number': {47: '10', 128: '5', 139: '32', 347: '64', 430: '67'},
'Cabin_letter_2': {47: 'P', 128: 'P', 139: 'P', 347: 'P', 430: 'S'}
}
# 我的代码：
imputer = SimpleImputer(missing_values=np.NaN, strategy='most_frequent')
X_train[X_train_objects] = imputer.fit_transform(X_train[X_train_objects].values.reshape(-1,1))[:,0]
希望这有帮助。如果你需要进一步的解释或帮助，请告诉我。
英文:
I have a list of columns of a dataframe that have NA's in them (below). The dtype of all these columns is str.
X_train_objects = ['HomePlanet',
'Destination',
'Name',
'Cabin_letter',
'Cabin_number',
'Cabin_letter_2']
I would like to use SimpleImputer to fill in the NA's will the most common value (mode). However, I am getting a ValueError: Columns must be same length as key. What is the reason for this, my code seems correct to me?
Dataframe sample (called X_train) of the Destination column being np.NAs:
{'PassengerId': {47: '0045_02',
128: '0138_02',
139: '0152_01',
347: '0382_01',
430: '0462_01'},
'HomePlanet': {47: 'Mars',
128: 'Earth',
139: 'Earth',
347: nan,
430: 'Earth'},
'CryoSleep': {47: 1, 128: 0, 139: 0, 347: 0, 430: 1},
'Destination': {47: nan, 128: nan, 139: nan, 347: nan, 430: nan},
'Age': {47: 19.0, 128: 34.0, 139: 41.0, 347: 23.0, 430: 50.0},
'VIP': {47: 0, 128: 0, 139: 0, 347: 0, 430: 0},
'RoomService': {47: 0.0, 128: 0.0, 139: 0.0, 347: 348.0, 430: 0.0},
'FoodCourt': {47: 0.0, 128: 22.0, 139: 0.0, 347: 0.0, 430: 0.0},
'ShoppingMall': {47: 0.0, 128: 0.0, 139: 0.0, 347: 0.0, 430: 0.0},
'Spa': {47: 0.0, 128: 564.0, 139: 0.0, 347: 4.0, 430: 0.0},
'VRDeck': {47: 0.0, 128: 207.0, 139: 607.0, 347: 368.0, 430: 0.0},
'Name': {47: 'Mass Chmad',
128: 'Monah Gambs',
139: 'Andan Estron',
347: 'Blanie Floydendley',
430: 'Ronia Sosanturney'},
'Transported': {47: 1, 128: 0, 139: 0, 347: 0, 430: 0},
'Cabin_letter': {47: 'F', 128: 'E', 139: 'F', 347: 'G', 430: 'G'},
'Cabin_number': {47: '10', 128: '5', 139: '32', 347: '64', 430: '67'},
'Cabin_letter_2': {47: 'P', 128: 'P', 139: 'P', 347: 'P', 430: 'S'}}
My Code:
imputer = SimpleImputer(missing_values=np.NaN, strategy='most_frequent')
X_train[X_train_objects] = imputer.fit_transform(X_train[X_train_objects].values.reshape(-1,1))[:,0]
答案1
得分: 2
更新：
根据提问者的反馈，能够得到所需结果的策略是执行以下操作：
X_train[X_train_objects] = imputer.fit_transform(X_train[X_train_objects].values)
原始答案：
你的问题中的代码实际执行以下操作：
- 处理
X_train[X_train_objects]，其形状为 (5, 6) - 通过
values将其转换为一个NumPy数组，并使用.reshape(-1,1)[:,0]将其变为一个长度为30的一维数组 - 将这个一维数组作为参数传递给
imputer.fit_transform，它返回的结果的形状与其输入相同 - 尝试使用这个长度为30的一维数组来更新
X_train[X_train_objects]中的所有行，而X_train[X_train_objects]（如前所述）的形状为 (5, 6)，具体来说，它只有6列
这会导致错误：ValueError: Columns must be same length as key
我认为你的意图是，在处理了最初在 X_train[X_train_objects] 中找到的值之后，通过使用处理后的值来更新原始对象，覆盖原始值。为了实现这一目标，我认为以下代码应该有效：
X_train[X_train_objects] = (
imputer.fit_transform(X_train[X_train_objects].values.reshape(-1,1))[:,0]
.reshape(-1,len(X_train_objects)) )
英文:
UPDATE:
Based on feedback from OP, the strategy that gives the desired result is to do this:
X_train[X_train_objects] = imputer.fit_transform(X_train[X_train_objects].values)
ORIGINAL ANSWER:
Here's what the code in your question does:
- works with
X_train[X_train_objects], which has shape (5, 6) - converts it to a numpy array (via
values) and changes it to a 1D array of length 30 using.reshape(-1,1)[:,0] - passes this as an argument to
imputer.fit_transformwhich returns a result whose shape is the same as its input - attempts to use this 1D array of length 30 to update all rows in
X_train[X_train_objects]which (as mentioned above) has shape (5, 6), or specifically, has only 6 columns
This gives rise to the error: ValueError: Columns must be same length as key
What I believe you intend is, having massaged the values originally found in X_train[X_train_objects], to update the original object by overwriting the original values with the massaged ones. To do this, I think the following should work:
X_train[X_train_objects] = (
imputer.fit_transform(X_train[X_train_objects].values.reshape(-1,1))[:,0]
.reshape(-1,len(X_train_objects)) )
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