如何将sklearn fit_transform与pandas一起使用并返回数据框而不是numpy数组?

问题描述

我想将缩放比例(使用来自sklearn.preprocessing的StandardScaler())应用于熊猫数据框.以下代码返回一个numpy数组，因此我丢失了所有列名和索引.这不是我想要的.

I want to apply scaling (using StandardScaler() from sklearn.preprocessing) to a pandas dataframe. The following code returns a numpy array, so I lose all the column names and indeces. This is not what I want.

features = df[["col1", "col2", "col3", "col4"]]
autoscaler = StandardScaler()
features = autoscaler.fit_transform(features)

我在网上找到的解决方案"是:

A "solution" I found online is:

features = features.apply(lambda x: autoscaler.fit_transform(x))

它似乎可以工作，但会导致弃用警告:

It appears to work, but leads to a deprecationwarning:

我因此尝试:

features = features.apply(lambda x: autoscaler.fit_transform(x.reshape(-1, 1)))

但这给出了:

如何将缩放应用于熊猫数据框，而使数据框完整无缺?尽可能不复制数据.

How do I apply scaling to the pandas dataframe, leaving the dataframe intact? Without copying the data if possible.

推荐答案

您可以使用 as_matrix() .随机数据集上的示例:

You could convert the DataFrame as a numpy array using as_matrix(). Example on a random dataset:

根据上述as_matrix()文档的最后一句话将as_matrix()更改为values(不会更改结果):

Changing as_matrix() to values, (it doesn't change the result) per the last sentence of the as_matrix() docs above:

import pandas as pd
import numpy as np #for the random integer example
df = pd.DataFrame(np.random.randint(0.0,100.0,size=(10,4)),
              index=range(10,20),
              columns=['col1','col2','col3','col4'],
              dtype='float64')

注意，索引为10-19:

Note, indices are 10-19:

In [14]: df.head(3)
Out[14]:
    col1    col2    col3    col4
    10  3   38  86  65
    11  98  3   66  68
    12  88  46  35  68

现在fit_transform DataFrame以获得scaled_features array:

Now fit_transform the DataFrame to get the scaled_features array:

from sklearn.preprocessing import StandardScaler
scaled_features = StandardScaler().fit_transform(df.values)

In [15]: scaled_features[:3,:] #lost the indices
Out[15]:
array([[-1.89007341,  0.05636005,  1.74514417,  0.46669562],
       [ 1.26558518, -1.35264122,  0.82178747,  0.59282958],
       [ 0.93341059,  0.37841748, -0.60941542,  0.59282958]])

将缩放后的数据分配给DataFrame(注意:使用index和columns关键字参数来保留原始索引和列名:

Assign the scaled data to a DataFrame (Note: use the index and columns keyword arguments to keep your original indices and column names:

scaled_features_df = pd.DataFrame(scaled_features, index=df.index, columns=df.columns)

In [17]:  scaled_features_df.head(3)
Out[17]:
    col1    col2    col3    col4
10  -1.890073   0.056360    1.745144    0.466696
11  1.265585    -1.352641   0.821787    0.592830
12  0.933411    0.378417    -0.609415   0.592830

---

遍历 sklearn-pandas 包.它致力于使scikit-learn更易于与熊猫一起使用.当您需要将一种以上类型的转换应用于DataFrame的列子集(一种更常见的情况)时，sklearn-pandas特别有用.它已记录在案，但这是您实现我们刚刚执行的转换的方式.

Came across the sklearn-pandas package. It's focused on making scikit-learn easier to use with pandas. sklearn-pandas is especially useful when you need to apply more than one type of transformation to column subsets of the DataFrame, a more common scenario. It's documented, but this is how you'd achieve the transformation we just performed.

from sklearn_pandas import DataFrameMapper

mapper = DataFrameMapper([(df.columns, StandardScaler())])
scaled_features = mapper.fit_transform(df.copy(), 4)
scaled_features_df = pd.DataFrame(scaled_features, index=df.index, columns=df.columns)

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