Kaggle competition: SIIM-ACR Pneumothorax Segmentation
%matplotlib inline 
<load_from_csv>
xf2 = pd.read_csv('.. /input/lungv1/leaky_unet_submission.csv')
 xf1 = pd.read_csv('.. /input/unet-with-efficientnet-encoder-in-keras/orig_submission.csv')
train = pd.read_csv('.. /input/digit-recognizer/train.csv')
 test = pd.read_csv('.. /input/digit-recognizer/test.csv' )<categorify>
xf1.columns = ['ImageId', 'enc1']
xf2.columns = ['ImageId', 'enc2']
xf3 = pd.merge(left = xf1, right = xf2, on = 'ImageId', how = 'inner')
 print(xf1.shape, xf2.shape, xf3.shape)
 xf3[xf3['enc1'] != xf3['enc2']]
id1 = np.where(xf3['enc1'] == '-1')[0]
id2 = np.where(xf3['enc2'] != '-1')[0]
idx = np.intersect1d(id1,id2)
 xf3['EncodedPixels'] = xf3['enc1']
xf3['EncodedPixels'][idx] = xf3['enc2'][idx]
<split><EOS> 
xf3[['ImageId','EncodedPixels']].to_csv('hybrid_1_2.csv', index = False )