import pandas as pd

from sklearn.datasets import load_iris

colnames = ['sepallength', 'sepalwidth', 'petallength', 'petalwidth']

iris = load_iris()

x = iris.data    # take independent variables in x
y = iris.target  # take target variable in y

x = pd.DataFrame(x, columns=colnames) # make a dataframe of independent variables
y = pd.Series(y, name='class')        # make a pandas series of target variable

iris_data = pd.concat([x, y], axis=1) # concatenate both x & y to get complete dataset

Let's see data's first 5 rows

iris_data.head()

	sepallength	sepalwidth	petallength	petalwidth	class
0	5.1	3.5	1.4	0.2	0
1	4.9	3.0	1.4	0.2	0
2	4.7	3.2	1.3	0.2	0
3	4.6	3.1	1.5	0.2	0
4	5.0	3.6	1.4	0.2	0

Let's see the shape of data

iris_data.shape

(150, 5)

from sklearn.model_selection import train_test_split

train_test_split shuffles the rows befor splitting data.

Splitting data into train & test dataset in the proportion of 70 : 30 respectively.

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,random_state=9)

Printing shape of training data below

print('shapes of training data:')
print('shape of x_train : ', x_train.shape)
print('shape of y_train : ', len(y_train))

shapes of training data:
shape of x_train :  (105, 4)
shape of y_train :  105

Printing shape of test data below

print('shapes of test data:')
print('shape of x_test : ', x_test.shape)
print('shape of y_test : ', len(y_test))

shapes of test data:
shape of x_test :  (45, 4)
shape of y_test :  45