Retain feature names after Scikit Feature Selection
<p>After running a Variance Threshold from Scikit-Learn on a set of data, it removes a couple of features. I feel I'm doing something simple yet stupid, but I'd like to retain the names of the remaining features. The following code: </p>

<pre><code>def VarianceThreshold_selector(data):
    selector = VarianceThreshold(.5) 
    selector.fit(data)
    selector = (pd.DataFrame(selector.transform(data)))
    return selector
x = VarianceThreshold_selector(data)
print(x)
</code></pre>

<p>changes the following data (this is just a small subset of the rows):</p>

<pre><code>Survived    Pclass  Sex Age SibSp   Parch   Nonsense
0             3      1  22   1        0        0
1             1      2  38   1        0        0
1             3      2  26   0        0        0
</code></pre>

<p>into this (again just a small subset of the rows)</p>

<pre><code>     0         1      2     3
0    3      22.0      1     0
1    1      38.0      1     0
2    3      26.0      0     0
</code></pre>

<p>Using the get_support method, I know that these are Pclass, Age, Sibsp, and Parch, so I'd rather this return something more like :</p>

<pre><code>     Pclass         Age      Sibsp     Parch
0        3          22.0         1         0
1        1          38.0         1         0
2        3          26.0         0         0
</code></pre>

<p>Is there an easy way to do this? I'm very new with Scikit Learn, so I'm probably just doing something silly. </p>

<p>There's probably better ways to do this, but for those interested here's how I did:</p>

<pre><code>def VarianceThreshold_selector(data):

    #Select Model
    selector = VarianceThreshold(0) #Defaults to 0.0, e.g. only remove features with the same value in all samples

    #Fit the Model
    selector.fit(data)
    features = selector.get_support(indices = True) #returns an array of integers corresponding to nonremoved features
    features = [column for column in data[features]] #Array of all nonremoved features' names

    #Format and Return
    selector = pd.DataFrame(selector.transform(data))
    selector.columns = features
    return selector
</code></pre>

python pandas scikit-learn output feature-selection