print("type of data{}format(type(iris_dataset['data']))out[ ]type of datathe rows in the data array correspond to flowerswhile the columns represent the four measurements that were taken for each flowerin[ ]print("shape of data{}format(iris_dataset['data'shape)out[ ]shape of data( we see that the array contains measurements for different flowers remember that the individual items are called samples in machine learningand their properties are called features the shape of the data array is the number of samples multiplied by the number of features this is convention in scikit-learnand your data will always be assumed to be in this shape here are the feature values for the first five samplesin[ ]print("first five columns of data:\ {}format(iris_dataset['data'][: ])out[ ]first five columns of data[ ]from this datawe can see that all of the first five flowers have petal width of cm and that the first flower has the longest sepalat cm the target array contains the species of each of the flowers that were measuredalso as numpy arrayin[ ]print("type of target{}format(type(iris_dataset['target']))out[ ]type of targettarget is one-dimensional arraywith one entry per flower introduction