target in cluster analisys (PCA) i m doing dimensionaly reduction using PCA. I don't understand why some dataset already had a target ad example in Iris database or other like this (https://scikit-learn.org/stable/datasets/index.html)
had a target_names useful when plot data.

Ad example in iris database choose like color= target_names to do this 



i found this code online in example.

fig = plt.figure(figsize = (8,8))
ax = fig.add_subplot(1,1,1) 
ax.set_xlabel('Principal Component 1', fontsize = 15)
ax.set_ylabel('Principal Component 2', fontsize = 15)
ax.set_title('2 component PCA', fontsize = 20)
targets = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
colors = ['r', 'g', 'b']
for target, color in zip(targets,colors):
    indicesToKeep = finalDf['target'] == target
    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']
               , finalDf.loc[indicesToKeep, 'principal component 2']
               , c = color
               , s = 50)
ax.legend(targets)
ax.grid()


I m trying to do the same with my dataset, where i dont have a target

i have a table in this way 

User    Movie
        0 1 2 3 4 
      0 2 0 5 0 0
      1 0 1 1 0 0
      2 0 5 5 5 0


for each user i have all film and him review (0 if don't review) 

When plot my graph i tried to do this 

fig = plt.figure(figsize = (16,12))
ax = fig.add_subplot(111)


ax.scatter(a,b, alpha = 1)
plt.title('Method: PCA')
#plt.savefig('PCA.png', dpi = 300)
plt.show()


but i really don't know where is my target. I try to add another column to my dataset with gender for user for cluster user for gender but give an error of the shape because i just have a 2 gender but 6000 user. 

I really don't know to apply a target in this way,
Same suggestion?
Iris data set comes with a label, it is a supervised learning problem. The task is to predict the species of the flowers.

Your problem need not have a label, it really depends on your task and data. If your goal is to predict the gender and you want to see if you can see a pattern in lower dimensional space, you can give it a try to treat gender as a target. 

If your problem is an unsupervised one, You can also just plot out your data and see whether you see any clustering in your dataset. 

Remark: You might want to be careful about the $0$ values, $0$ doesn't mean the movie is good or bad. It really means no feedback.