NumPy Data Set Exercises

(1) Installing the Scipy,numpy,sklearn package

(2) The IRIS data set is read from the data set in the Sklearn package

(3) View data type

# Load NumPy Package Import NumPy # Load Sklearn Package  from Import  # Read the iris DataSet datadata=load_iris ()# View data type print  (Type (data))# View data content print(Data.keys ())

Operation Result:

(4) Remove the iris feature and Iris category data to see its shape and data type

# Four features of Iris #data_feature= data.feature_namesiris_data=data.dataprint(data_ Feature)print(iris_data)# Iris three categories #Data_target =data.target_names  Print(data_target) iris_data=data.targetprint(iris_data)  # data type #type (iris_data)

Operation Result:

(5) Extract the data of the calyx length (cm) of all flowers

# iris calyx Length Data #  for  in data['data'))print(sepal_length)

Operation Result:

(6) Remove all flower petal length (cm) + petal width (cm) data

# the length of all flower petals #  for  in data['data']# width of all flower petals # for in data['data']# The length and width of all flower petals Data_petal_l_ W=np.array ([Data_petal_l,data_petal_w])

Operation Result:

(7) Take out four characteristics and categories of a flower

(8) Divide all flowers into three groups, 50 per group

(9) Generate new Ganso, each group includes features and categories

#(7) four characters and categories of a flowerData_flower= (data['Data'][0],data['Target_names'][0]) data_flower#(8) Define three lists to hold the categories of different types of flowers #Data_setosa=[]#storing flowers of the class 0Data_versicolor=[]#storing flowers of the Class 1Data_virginica=[]#storing flowers of the class 2Len (data['Data']) forIinchRange (0,150):    ifdata['Target'][i]==0:#category is Setosadatas=data['Data'][i].tolist () datas.append ('Setosa')        Print(Data_setosa.append (datas))elifdata['Target'][i]==1:#category is Versicolordatas=data['Data'][i].tolist () datas.append ('versicolor') data_versicolor.append (datas)Else: Datas=data['Data'][i].tolist ()#category is VirginicaDatas.append ('virginica') data_virginica.append (datas)#(9) forming a new array to hold three categories of flowers #New_data=(Np.array ([Data_setosa,data_versicolor,data_virginica]))Print(New_data)

Operation Result:

NumPy Data Set Exercises