what is the one hot encoding for cancer data classification I am working on a project to classify lung CT dataset using CNN and tensorflow, I know that the order for the category is cancer/no-cancer (only 2 classes), in more than one Github repository I see that they did one hot encoding like the code below:

if label == 1:
    label = np.array([0, 1])
elif label == 0:
    label = np.array([1, 0])


what makes me confused is: 1 means cancer and 0 means no-cancer, as I understand it should be:

if label == 1:
    label = np.array([1, 0])
elif label == 0:
    label = np.array([0, 1])


but why they did one hot encoding like this, I don't know I am wrong or there is another thing that I did not understand, can anyone explain it for? or give me a better way to do encoding for my data, but with code?
Both ways would work equally, but the way you see in the github repo is more standard. 

The standard way of converting a integer label $y_i$ (from 0 to K-1) into a one-hot vector encoding is by creating a all-zero vector of length K, and set the element indexed by $y_i$ to be 1, i.e.

label_one_hot = np.zeros(k)
label_one_hot[label] = 1