How to merge back k-means clustering outputs to the corresponding units in a dataframe - python-3.x

Question

I just wonder this strategy is the correct way to merge back the k-means clustering outputs to the corresponding units in the existing dataframe.
For example, I have a data set which includes user ID, age, income, gender and I want to run a k-means clustering algorithm to find a set of clusters where each cluster has similar users in terms of these characteristics (age, income, gender). Note that I disregard the value difference among the characteristics for the brevity.
existing_dataframe
user_id age income gender
1 13 10 1 (female)
2 34 50 1
3 75 40 0 (male)
4 23 29 0
5 80 45 1
... ... ... ...
existing_dataframe_for_analysis
(Based on my understanding after referring number of tutorials from online sources,
I should not include user_id variable, so I use the below dataframe for the analysis;
please let me know if I am wrong)
age income gender
13 10 1 (female)
34 50 1
75 40 0 (male)
23 29 0
80 45 1
... ... ... ...
Assume that I found the optimal number of clusters from the dataset is 3. So I decided to set it as 3 and predict in which cluster each user is categorized using the below code.
from sklearn.cluster import KMeans
model = KMeans(n_clusters=3,
init='k-means++',
max_iter=20,
n_init=10)
model.fit(existing_dataframe_for_analysis)
predicted=model.predict(existing_dataframe_for_analysis)
print (predicted[:5])
The expected out can be shown below:
[0 1 2 1 2]
If I run the below code where I create a new column called 'cluster' which represents the analysis outputs and add the colum to the existing dataframe, does it gaurantee that nth element from the output list corresponds to the nth observation (user id) in the existing dataframe? Please advice.
existing_dataframe['cluster']=predicted
print (existing_dataframe)
output:
user_id age income gender cluster
1 13 10 1 (female) 0
2 34 50 1 1
3 75 40 0 (male) 2
4 23 29 0 1
5 80 45 1 2
... ... ... ... ...

Answer 1

Your approach to rejoin the predictions is correct. Your assumption to not include any ids is also correct. However, I strongly advise you to scale your input variables before doing any clustering, as your variables have different units.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_features = scaler.fit_transform(existing_dataframe_for_analysis)
Then continue working with this new object as you did before.