Python/sklearn - preprocessing.MinMaxScaler 1d弃用

Question

我想缩放一个数据帧的列,使其值介于0和1之间.为此,我使用的是一个MinMaxScaler正常工作,但是向我发送混合消息.我正在做:

x = df['Activity'].values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df['Activity'] = pd.Series(x_scaled)

此代码的消息numero uno是一个警告:

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.

好吧,所以具有1d数组的四胞胎将会很快成为现实,所以让我们按照建议重新塑造它:

x = df['Activity'].values.reshape(-1, 1)

现在代码甚至没有运行:Exception: Data must be 1-dimensional抛出.所以我很困惑.1d即将被弃用,但数据也必须是1d ?? 如何安全地做到这一点？这是什么问题？

按照@sascha的要求编辑

x 看起来像这样:

array([ 0.00568953,  0.00634314,  0.00718003, ...,  0.01976002,
        0.00575024,  0.00183782])

重塑后:

array([[ 0.00568953],
       [ 0.00634314],
       [ 0.00718003],
       ..., 
       [ 0.01976002],
       [ 0.00575024],
       [ 0.00183782]])

整个警告:

/usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/data.py:321: 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)
/usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/data.py:356: 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)

我重塑时的错误:

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-132-df180aae2d1a> in <module>()
      2 min_max_scaler = preprocessing.MinMaxScaler()
      3 x_scaled = min_max_scaler.fit_transform(x)
----> 4 telecom['Activity'] = pd.Series(x_scaled)

/usr/local/lib/python3.5/dist-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
    225             else:
    226                 data = _sanitize_array(data, index, dtype, copy,
--> 227                                        raise_cast_failure=True)
    228 
    229                 data = SingleBlockManager(data, index, fastpath=True)

/usr/local/lib/python3.5/dist-packages/pandas/core/series.py in _sanitize_array(data, index, dtype, copy, raise_cast_failure)
   2918     elif subarr.ndim > 1:
   2919         if isinstance(data, np.ndarray):
-> 2920             raise Exception('Data must be 1-dimensional')
   2921         else:
   2922             subarr = _asarray_tuplesafe(data, dtype=dtype)

Exception: Data must be 1-dimensional

Answer 1

你可以简单地放弃pd.Series:

import pandas as pd
from sklearn import preprocessing
df = pd.DataFrame({'Activity': [ 0.00568953,  0.00634314,  0.00718003, 
                                0.01976002, 0.00575024,  0.00183782]})
x = df['Activity'].values.reshape(-1, 1) #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df['Activity'] = x_scaled

或者您可以明确地获得第一列x_scaled:

df['Activity'] = pd.Series(x_scaled[:, 0])