Python 如何将 sklearn fit_transform 与 pandas 一起使用并返回数据帧而不是 numpy 数组？

How to use sklearn fit_transform with pandas and return dataframe instead of numpy array?

提问by louic

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.

我想将缩放（使用 sklearn.preprocessing 中的 StandardScaler()）应用于 Pandas 数据帧。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。

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

A "solution" I found online is:

我在网上找到的一个“解决方案”是：

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

It appears to work, but leads to a deprecationwarning:

它似乎有效，但会导致弃用警告：

/usr/lib/python3.5/site-packages/sklearn/preprocessing/data.py:583: 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.

/usr/lib/python3.5/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: 将一维数组作为数据在 0.17 中被弃用，并将在 0.19 中引发 ValueError。如果您的数据具有单个特征，则使用 X.reshape(-1, 1) 或 X.reshape(1, -1) 如果它包含单个样本来重塑您的数据。

I therefore tried:

因此，我尝试：

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

But this gives:

但这给出了：

Traceback (most recent call last): File "./analyse.py", line 91, in features = features.apply(lambda x: autoscaler.fit_transform(x.reshape(-1, 1))) File "/usr/lib/python3.5/site-packages/pandas/core/frame.py", line 3972, in apply return self._apply_standard(f, axis, reduce=reduce) File "/usr/lib/python3.5/site-packages/pandas/core/frame.py", line 4081, in _apply_standard result = self._constructor(data=results, index=index) File "/usr/lib/python3.5/site-packages/pandas/core/frame.py", line 226, in initmgr = self._init_dict(data, index, columns, dtype=dtype) File "/usr/lib/python3.5/site-packages/pandas/core/frame.py", line 363, in _init_dict dtype=dtype) File "/usr/lib/python3.5/site-packages/pandas/core/frame.py", line 5163, in _arrays_to_mgr arrays = _homogenize(arrays, index, dtype) File "/usr/lib/python3.5/site-packages/pandas/core/frame.py", line 5477, in _homogenize raise_cast_failure=False) File "/usr/lib/python3.5/site-packages/pandas/core/series.py", line 2885, in _sanitize_array raise Exception('Data must be 1-dimensional') Exception: Data must be 1-dimensional

回溯（最近一次调用）：文件“./analysis.py”，第 91 行，在 features = features.apply(lambda x: autoscaler.fit_transform(x.reshape(-1, 1))) 文件“/usr/ lib/python3.5/site-packages/pandas/core/frame.py", line 3972, in apply return self._apply_standard(f, axis, reduce=reduce) File "/usr/lib/python3.5/site- package/pandas/core/frame.py”，第 4081 行，在 _apply_standard 结果 = self._constructor(data=results, index=index) 文件“/usr/lib/python3.5/site-packages/pandas/core/frame .py”，第 226 行，在 init 中mgr = self._init_dict(data, index, columns, dtype=dtype) 文件 "/usr/lib/python3.5/site-packages/pandas/core/frame.py", line 363, in _init_dict dtype=dtype) 文件"/usr/lib/python3.5/site-packages/pandas/core/frame.py", line 5163, in _arrays_to_mgr arrays = _homogenize(arrays, index, dtype) File "/usr/lib/python3.5/site -packages/pandas/core/frame.py”，第 5477 行，在 _homogenize raise_cast_failure=False）文件“/usr/lib/python3.5/site-packages/pandas/core/series.py”，第 2885 行，在 _sanitize_array raise Exception('数据必须是一维的') 异常：数据必须是一维的

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

如何对 Pandas 数据框应用缩放，而保持数据框完好无损？如果可能，不复制数据。

回答by Kevin

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

您可以使用 将 DataFrame 转换为 numpy 数组as_matrix()。随机数据集的示例：

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

编辑：根据上述文档的最后一句 更改as_matrix()为values,（它不会更改结果）as_matrix()：

Generally, it is recommended to use ‘.values'.

一般建议使用'.values'。

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')

Note, indices are 10-19:

注意，索引是 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

Now fit_transformthe DataFrame to get the scaled_featuresarray:

现在fit_transformDataFrame 来获取scaled_featuresarray：

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]])

Assign the scaled data to a DataFrame (Note: use the indexand columnskeyword arguments to keep your original indices and column names:

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：

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

---

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Edit 2:

编辑2：

Came across the sklearn-pandaspackage. It's focused on making scikit-learn easier to use with pandas. sklearn-pandasis 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.

遇到了sklearn-pandas包。它专注于使 scikit-learn 更易于与 Pandas 一起使用。 sklearn-pandas当您需要对 的列子集应用不止一种类型的转换时特别有用DataFrame，这是一种更常见的场景。它已记录在案，但这就是您实现我们刚刚执行的转换的方式。

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)

回答by Joe

import pandas as pd    
from sklearn.preprocessing import StandardScaler

df = pd.read_csv('your file here')
ss = StandardScaler()
df_scaled = pd.DataFrame(ss.fit_transform(df),columns = df.columns)

The df_scaled will be the 'same' dataframe, only now with the scaled values

df_scaled 将是“相同”的数据帧，只有现在具有缩放值

回答by zzHQzz

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

回答by Guillaume Chevalier

You can mix multiple data types in scikit-learn using Neuraxle:

您可以使用Neuraxle在 scikit-learn 中混合多种数据类型：

Option 1: discard the row names and column names

选项 1：丢弃行名和列名

from neuraxle.pipeline import Pipeline
from neuraxle.base import NonFittableMixin, BaseStep

class PandasToNumpy(NonFittableMixin, BaseStep):
    def transform(self, data_inputs, expected_outputs): 
        return data_inputs.values

pipeline = Pipeline([
    PandasToNumpy(),
    StandardScaler(),
])

Then, you proceed as you intended:

然后，您按预期进行：

features = df[["col1", "col2", "col3", "col4"]]  # ... your df data
pipeline, scaled_features = pipeline.fit_transform(features)

Option 2: to keep the original column names and row names

选项2：保留原来的列名和行名

You could even do this with a wrapper as such:

你甚至可以用这样的包装器来做到这一点：

from neuraxle.pipeline import Pipeline
from neuraxle.base import MetaStepMixin, BaseStep

class PandasValuesChangerOf(MetaStepMixin, BaseStep):
    def transform(self, data_inputs, expected_outputs): 
        new_data_inputs = self.wrapped.transform(data_inputs.values)
        new_data_inputs = self._merge(data_inputs, new_data_inputs)
        return new_data_inputs

    def fit_transform(self, data_inputs, expected_outputs): 
        self.wrapped, new_data_inputs = self.wrapped.fit_transform(data_inputs.values)
        new_data_inputs = self._merge(data_inputs, new_data_inputs)
        return self, new_data_inputs

    def _merge(self, data_inputs, new_data_inputs): 
        new_data_inputs = pd.DataFrame(
            new_data_inputs,
            index=data_inputs.index,
            columns=data_inputs.columns
        )
        return new_data_inputs

df_scaler = PandasValuesChangerOf(StandardScaler())

Then, you proceed as you intended:

然后，您按预期进行：

features = df[["col1", "col2", "col3", "col4"]]  # ... your df data
df_scaler, scaled_features = df_scaler.fit_transform(features)

回答by Hassan K

You can try this code, this will give you a DataFrame with indexes

你可以试试这个代码，这会给你一个带索引的 DataFrame

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_boston # boston housing dataset

dt= load_boston().data
col= load_boston().feature_names

# Make a dataframe
df = pd.DataFrame(data=dt, columns=col)

# define a method to scale data, looping thru the columns, and passing a scaler
def scale_data(data, columns, scaler):
    for col in columns:
        data[col] = scaler.fit_transform(data[col].values.reshape(-1, 1))
    return data

# specify a scaler, and call the method on boston data
scaler = StandardScaler()
df_scaled = scale_data(df, col, scaler)

# view first 10 rows of the scaled dataframe
df_scaled[0:10]