How to retain column headers of data frame after Pre-processing in scikit-learn

Question

I have a pandas data frame which has some rows and columns. Each column has a header. Now as long as I keep doing data manipulation operations in pandas, my variable headers are retained. But if I try some data pre-processing feature of Sci-kit-learn lib, I end up losing all my headers and the frame gets converted to just a matrix of numbers.

I understand why it happens because scikit-learn gives a numpy ndarray as output. And numpy ndarray being just matrix would not have column names.

But here is the thing. If I am building some model on my dataset, even after initial data pre-processing and trying some model, I might have to do some more data manipulation tasks to run some other model for better fit. Without being able to access column header makes it difficult to do data manipulation as I might not know what is the index of a particular variable, but it's easier to remember variable name or even look up by doing df.columns.

How to overcome that?

EDIT1: Editing with sample data snapshot.

    Pclass  Sex Age SibSp   Parch   Fare    Embarked
0   3   0   22  1   0   7.2500  1
1   1   1   38  1   0   71.2833 2
2   3   1   26  0   0   7.9250  1
3   1   1   35  1   0   53.1000 1
4   3   0   35  0   0   8.0500  1
5   3   0   NaN 0   0   8.4583  3
6   1   0   54  0   0   51.8625 1
7   3   0   2   3   1   21.0750 1
8   3   1   27  0   2   11.1333 1
9   2   1   14  1   0   30.0708 2
10  3   1   4   1   1   16.7000 1
11  1   1   58  0   0   26.5500 1
12  3   0   20  0   0   8.0500  1
13  3   0   39  1   5   31.2750 1
14  3   1   14  0   0   7.8542  1
15  2   1   55  0   0   16.0000 1

The above is basically the pandas data frame. Now when I do this on this data frame it will strip the column headers.

from sklearn import preprocessing 
X_imputed=preprocessing.Imputer().fit_transform(X_train) 
X_imputed

New data is of numpy array and hence the column names are stripped.

array([[  3.        ,   0.        ,  22.        , ...,   0.        ,
          7.25      ,   1.        ],
       [  1.        ,   1.        ,  38.        , ...,   0.        ,
         71.2833    ,   2.        ],
       [  3.        ,   1.        ,  26.        , ...,   0.        ,
          7.925     ,   1.        ],
       ..., 
       [  3.        ,   1.        ,  29.69911765, ...,   2.        ,
         23.45      ,   1.        ],
       [  1.        ,   0.        ,  26.        , ...,   0.        ,
         30.        ,   2.        ],
       [  3.        ,   0.        ,  32.        , ...,   0.        ,
          7.75      ,   3.        ]])

So I want to retain the column names when I do some data manipulation on my pandas data frame.

Solution 1

scikit-learn indeed strips the column headers in most cases, so just add them back on afterward. In your example, with X_imputed as the sklearn.preprocessing output and X_train as the original dataframe, you can put the column headers back on with:

X_imputed_df = pd.DataFrame(X_imputed, columns = X_train.columns)

Solution 2

The above answers still do not resolve the main question. There are two implicit assumptions here

1. That all the features of the dataset will be retained which might not be true. E.g. some kind of feature selection function.
2. That all the features will be retained in the same order, again there might be implicit sorting in some feature selection transformations.

There is a "get_support()" method in at least some of the fit and transform functions that save the information on which columns(features) are retained and in what order.

You can check the basics of the function and how to use it here ... Find get_support() function description here

This would be the most preferred and official way to get the information needed here.

Solution 3

According to Ami Tavory's reply here, per documentation, Imputer omits empty columns or rows (however you run it).
Thus, before running the Imputer and setting the column names as described above, run something like this (for columns):

X_train=X_train.dropna(axis=1, how='all')

df.dropna described here.