Question

我正在尝试使用sklearn MinMaxScaler重新缩放如下所示的python列：

scaler = MinMaxScaler()
y = scaler.fit(df['total_amount'])

但是出现以下错误：

Traceback (most recent call last):
  File "/Users/edamame/workspace/git/my-analysis/experiments/my_seq.py", line 54, in <module>
    y = scaler.fit(df['total_amount'])
  File "/Users/edamame/workspace/git/my-analysis/venv/lib/python3.4/site-packages/sklearn/preprocessing/data.py", line 308, in fit
    return self.partial_fit(X, y)
  File "/Users/edamame/workspace/git/my-analysis/venv/lib/python3.4/site-packages/sklearn/preprocessing/data.py", line 334, in partial_fit
    estimator=self, dtype=FLOAT_DTYPES)
  File "/Users/edamame/workspace/git/my-analysis/venv/lib/python3.4/site-packages/sklearn/utils/validation.py", line 441, in check_array
    "if it contains a single sample.".format(array))
ValueError: Expected 2D array, got 1D array instead:
array=[3.180000e+00 2.937450e+03 6.023850e+03 2.216292e+04 1.074589e+04
   :
 0.000000e+00 0.000000e+00 9.000000e+01 1.260000e+03].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

有什么想法吗？

Answer 1

MinMaxScaler的输入必须是类似shape [n_samples, n_features]的数组。因此，您可以将其作为 dataframe 而不是 series （使用双方括号而不是单个方括号）应用于列：

y = scaler.fit(df[['total_amount']])

通过您的描述，听起来您想fit_transform，而不只是fit（但我可能是错的）：

y = scaler.fit_transform(df[['total_amount']])

更多说明：

如果您的数据框有100行，请在将列转换为数组时考虑形状的差异：

>>> np.array(df[['total_amount']]).shape
(100, 1)

>>> np.array(df['total_amount']).shape
(100,)

第一个返回与[n_samples, n_features]匹配的形状（MinMaxScaler要求），而第二个不匹配。

Answer 2

尝试用这种方式：

import pandas as pd
from sklearn import preprocessing

x = df.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df = pd.DataFrame(x_scaled)

在熊猫列上应用MinMaxScaler（）

2 个答案: