The output of Model from Decision Tree and Random Forests are different?
Your df row count is 14,999

Your test data is 33% ~ 4950

So, your y_predict should be (4950,1) i.e. a binary prediction for all test rows.

This is what I am getting when running your code which you have posted here.

import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/bhaskoro-muthohar/DataScienceLearning/master/HR_comma_sep.csv')

print(df.size)
# Map salary to 0,1,2
df.salary = df.salary.map({"low": 0, "medium": 1, "high": 2})
# dropping left and sales X for the df, y for the left
X = df.drop(["left", "sales"], axis=1)
y = df["left"]

#splitting the train and test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0, stratify=y)
# Train a decision tree.
from sklearn.tree import DecisionTreeClassifier
clftree = DecisionTreeClassifier(max_depth=3)
clftree.fit(X_train, y_train)
y_pred = clftree.predict(X_test) 
print(y_pred.size)
from sklearn.ensemble import RandomForestClassifier
# random forest model creation
rfc = RandomForestClassifier()
rfc.fit(X_train,y_train)
# predictions
rfc_predict = rfc.predict(X_test)
print(rfc_predict.size)