Performing PCA on a dataframe with Python with sklearn
Question: I have a sample input file that has many rows of all variants, and columns
represent the number of components.

    
    
    A01_01  A01_02  A01_03  A01_04  A01_05  A01_06  A01_07  A01_08  A01_09  A01_10 A01_11   A01_12  A01_13  A01_14  A01_15  A01_16  A01_17  A01_18  A01_19  A01_20  A01_21  A01_22  A01_23  A01_24  A01_25  A01_26  A01_27  A01_28  A01_29  A01_30  A01_31  A01_32  A01_33  A01_34  A01_35  A01_36  A01_37  A01_38  A01_39  A01_40  A01_41  A01_42  A01_43  A01_44  A01_45  A01_46  A01_47  A01_48  A01_49  A01_50  A01_51  A01_52  A01_53  A01_54  A01_55  A01_56  A01_57  A01_58  A01_59  A01_60  A01_61  A01_62  A01_63  A01_64  A01_65  A01_66  A01_67  A01_69  A01_70  A01_71
    0   1   0   0   1   1   1   1   1   0   0   0   0   0   0   0   1   1   0   1   1   1   0   1   0   1   0   0   1   0   1   0   0   0   0   0   0   1   1   1   0   1   0   0   0   0   1   0   1   1   0   1   1   0   0   1   1   1   1   1   1   1   1   0   0   1   0   0   0   1
    0   1   0   0   1   1   1   1   1   0   0   0   0   0   0   0   1   1   0   1   1   1   0   1   0   1   0   0   1   0   1   0   0   0   0   0   0   1   1   1   0   1   0   0   0   0   1   0   1   1   0   1   1   0   0   1   1   1   1   1   1   1   1   0   0   1   0   0   0   1
    0   1   0   0   1   1   1   1   1   0   0   0   0   0   0   0   1   1   0   1   1   1   0   1   0   1   0   0   1   0   1   0   0   0   0   0   0   1   1   1   0   1   0   0   0   0   1   0   1   1   0   1   1   0   0   1   1   1   1   1   1   1   1   0   0   1   0   0   0   1
    0   1   0   0   1   1   1   1   1   0   0   0   0   0   0   0   1   1   0   1   1   1   0   1   0   1   0   0   1   0   1   0   0   0   0   0   0   1   1   1   0   1   0   0   0   0   1   0   1   1   0   1   1   0   0   1   1   1   1   1   1   1   1   0   0   1   0   0   0   1 
    0   1   0   0   1   1   1   1   1   0   0   0   0   0   0   0   1   1   0   1   1   1   0   1   0   1   0   0   1   0   1   0   0   0   0   0   0   1   1   1   0   1   0   0   0   0   1   0   1   1   0   1   1   0   0   1   1   1   1   1   1   1   1   0   0   1   0   0   0   1
    0   1   0   0   1   1   1   1   1   0   0   0   0   0   0   0   1   1   0   1   1   1   0   1   0   1   0   0   1   0   1   0   0   0   0   0   0   1   1   1   0   1   0   0   0   0   1   0   1   1   0   1   1   0   0   1   1   1   1   1   1   1   1   0   0   1   0   0   0   1
    

I first import this .txt file as:

    
    
    #!/usr/bin/env python
    from sklearn.decomposition import PCA
    
    inputfile=vcf=open('sample_input_file', 'r')
    

**I would like to performing principal component analysis and plotting the
first two components (meaning the first two columns)**

I am not sure if this the way to go about it after reading about

    
    
    sklearn 
    

PCA for two components:

    
    
    pca = PCA(n_components=2)
    pca.fit(inputfile) #not sure how this read in this file
    

_Therefore, I need help importing my input file as a dataframe for Python to
perform PCA on it_


Answer: `sklearn` works with numpy arrays.

So you want to use `numpy.loadtxt`:

    
    
    data = numpy.loadtxt('sample_input_file', skiprows=1)
    pca = PCA(n_components=2)
    pca.fit(data)
    

