無法import Imputer

from sklearn.preprocessing import Imputer

from sklearn.preprocessing import Imputer

from sklearn.impute import SimpleImputer

from sklearn.impute import SimpleImputer

from sklearn.preprocessing import MinMaxScaler
from sklearn.impute import SimpleImputer


# 特徵欄位清單
train = app_train
features = list(train.columns)

# 複製 test 資料
test = app_test.copy()

# 填補器 : 設定缺失值補中位數
imputer = SimpleImputer(strategy = 'median')

# 縮放器 : 設定特徵縮放到 0~1 區間
scaler = MinMaxScaler(feature_range = (0, 1))

# 填補器載入個欄中位數
imputer.fit(train)

# 將中位數回填 train, test 資料中的空缺值
train = imputer.transform(train)
test = imputer.transform(app_test)

# 縮放器載入 train 的上下限, 對 train, test 進行縮放轉換
scaler.fit(train)
train = scaler.transform(train)
test = scaler.transform(test)

print('Training data shape: ', train.shape)
print('Testing data shape: ', test.shape)

from sklearn.preprocessing import MinMaxScaler
from sklearn.impute import SimpleImputer


# 特徵欄位清單
train = app_train
features = list(train.columns)

# 複製 test 資料
test = app_test.copy()

# 填補器 : 設定缺失值補中位數
imputer = SimpleImputer(strategy = 'median')

# 縮放器 : 設定特徵縮放到 0~1 區間
scaler = MinMaxScaler(feature_range = (0, 1))

# 填補器載入個欄中位數
imputer.fit(train)

# 將中位數回填 train, test 資料中的空缺值
train = imputer.transform(train)
test = imputer.transform(app_test)

# 縮放器載入 train 的上下限, 對 train, test 進行縮放轉換
scaler.fit(train)
train = scaler.transform(train)
test = scaler.transform(test)

print('Training data shape: ', train.shape)
print('Testing data shape: ', test.shape)

TypeError: unorderable types: str() < float()

TypeError: unorderable types: str() < float()

ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''