If loop and test samples in Python

I don't understand the range of test_idx=range(105,150) in

plot_decision_regions(X=X_combined_std, y=y_combined, classifier=ppn, test_idx=range(105,150))

which graphs the test samples in

    if test_idx:
    X_test, y_test= X[test_idx,:], y[test_idx]
    # print( X[test_idx,:])



    plt.scatter(X_test[:,0], X_test[:,1], c='', edgecolor= 'black', alpha= 0.9, linewidth=1, marker='o', s=100, label='test set' )

If someone could explain to me how that "if loop" works and why print( X[test_idx,:]) prints X_test_std vector and not X_test vector, I would appreciate it. I'm new to machine learning.

iris = datasets.load_iris()
X=iris.data[:,[2,3]]
y=iris.target

X_train, X_test, y_train, y_test= train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
sc= StandardScaler()
sc.fit(X_train)
X_train_std=sc.transform(X_train)
X_test_std= sc.transform(X_test)

ppn= Perceptron( max_iter=40,eta0= 0.1, random_state=1)
ppn.fit(X_train_std, y_train)

y_pred= ppn.predict(X_test_std)
def plot_decision_regions(X, y, classifier,test_idx=None, resolution = 0.02):
    markers = ('s', 'x', 'o', '^','v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])

    x1_min, x1_max = X[:, 0].min() -1, X[:,0].max() + 1
    x2_min, x2_max = X[:, 1].min() -1, X[:,1].max() + 1
    xx1, xx2= np.meshgrid (np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha= 0.3, cmap = cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())


    for idx, cl in enumerate (np.unique(y)):
        plt.scatter (x=X[y == cl, 0], y= X[y == cl, 1], alpha=0.8, c=colors[idx], marker= markers [idx], label = cl, edgecolor = 'black')

    if test_idx:
        X_test, y_test= X[test_idx,:], y[test_idx]


        plt.scatter(X_test[:,0], X_test[:,1], c='', edgecolor= 'black', alpha= 0.9, linewidth=1, marker='o', s=100, label='test set' )


X_combined_std= np.vstack((X_train_std, X_test_std))
y_combined=np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined_std, y=y_combined, classifier=ppn, test_idx=range(105,150))
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.show()