Converting labels to one-hot encoding

Question

So I was learning one-hot encoding using iris dataset

iris = load_iris()
X = iris['data'] # the complete data -2D
Y = iris['target'] # 1-D only the 150 rows 
names = iris['target_names'] #['setosa','versicolor','viginica']
feature_names = iris['feature_names']# [sl,sw,pl,pw]
isamples = np.random.randint(len(Y), size = 5)
Ny = len(np.unique(Y))
Y = keras.utils.to_categorical(Y[:], num_classes = Ny)
print('X:', X[isamples,:])
print('Y:', Y[isamples])

I am confused in this part: Y = keras.utils.to_categorical(Y[:], num_classes = Ny)

what does Y[:] mean and what is the use of : in print(X[isamples,:])

Answer 1

The iris data set consists of 150 samples from each of three species of Iris flower (Iris setosa, Iris Virginia, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. in your code, the X represents the set of features to train your model on which you can get from iris.data, and y represents the target label for each row on the X set of features which you can get from iris.target. the labels are represented by using numerical value (e.g. 0 for setosa class, 1 for Virginia class, and 2 for versicolor class) you can get the name of each class by using iris.target_names. the colon you see between brackets called the slice operator in Python which let you take a subset of elements from the elemenst of the list for example if you have a list l = [1,2,3,4] if you want just the second and the third element of the list you can just use l[1:3]. ok now using the colon operator without using numbers like this l[:] will give you a copy of the whole list so Y[:] mean give me a copy of the Y list and for print(X[isamples,:]) isamples is a list of 5 randomly generated Indices between 0 and 600 to get a sample of features from the X list print(X[isamples,:]) means take 5 random samples from the list of features and print all of the four features for each sample