saleprice_scaled = /StandardScaler().fit_transform(df_train['SalePrice'][:,np.newaxis]);

为什么这里使用newaxis？我知道newaxis，但我无法理解它在这种特定情况下的用途。

回答：

df_train['SalePrice']是一个Pandas.Series（向量/一维数组），形状为：(N个元素,)

现代（版本：0.17+）的SKLearn方法不喜欢一维数组（向量），它们期望二维数组。

df_train['SalePrice'][:,np.newaxis]

将一维数组（形状：N个元素）转换为二维数组（形状：N行，1列）。

演示：

In [21]: df = pd.DataFrame(np.random.randint(10, size=(5, 3)), columns=list('abc'))In [22]: dfOut[22]:   a  b  c0  4  3  81  7  5  62  1  3  93  7  5  74  7  0  6In [23]: from sklearn.preprocessing import StandardScalerIn [24]: df['a'].shapeOut[24]: (5,)      # <--- 一维数组In [25]: df['a'][:, np.newaxis].shapeOut[25]: (5, 1)    # <--- 二维数组

还有Pandas的方法可以做到同样的事情：

In [26]: df[['a']].shapeOut[26]: (5, 1)    # <--- 二维数组In [27]: StandardScaler().fit_transform(df[['a']])Out[27]:array([[-0.5 ],       [ 0.75],       [-1.75],       [ 0.75],       [ 0.75]])

如果我们传递一维数组会发生什么：

In [28]: StandardScaler().fit_transform(df['a'])C:\Users\Max\Anaconda4\lib\site-packages\sklearn\utils\validation.py:429: DataConversionWarning: Data with input dtype int32 was converted to float64 by StandardScaler.  warnings.warn(msg, _DataConversionWarning)C:\Users\Max\Anaconda4\lib\site-packages\sklearn\preprocessing\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)C:\Users\Max\Anaconda4\lib\site-packages\sklearn\preprocessing\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)Out[28]: array([-0.5 ,  0.75, -1.75,  0.75,  0.75])