Pandas IndexError: DataFrame indexer is not allowed for .iloc

You may need Xtest.index

data['timestamp'].iloc[Xtest.index]

but it can be safer to use .loc because index sometimes may not have numbers 0..len(data) but something else - e.g. 10..len(data)+10 (data.index = range(10, len(data) + 10)) and then iloc will search in wrong place.

data['timestamp'].loc[Xtest.index]

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.modelselection import traintestsplit
from sklearn.linearmodel import LinearRegression
path = '/content/drive/MyDrive/MLDATASETS/energy.csv'
data = pd.readcsv(path)
import io
text = """timestamp,temp,load
2026.04.01,12,1
2026.04.02,9,0
2026.04.03,15,1
2026.04.04,4,0
2026.04.05,10,1
2026.04.06,10,1
2026.04.07,15,1
2026.04.08,7,0
2026.04.09,5,0
2026.04.10,11,1
"""
data = pd.readcsv(io.StringIO(text))
data["timestamp"] = pd.todatetime(data["timestamp"])
data["timenum"] = range(10, len(data) + 10)
data.index = range(10, len(data) + 10)  # to show that iloc is wrong idea
print(data)
X = data[["timenum", "temp"]]
y = data["load"]
Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, testsize=0.4)
model = LinearRegression()
model.fit(Xtrain, ytrain)
ypred = model.predict(Xtest)
print(Xtest)
plt.figure(figsize=(15, 5))
plt.scatter(data["timestamp"].loc[Xtest.index], ytest, s=5, label="Actual")
plt.scatter(data["timestamp"].loc[Xtest.index], ypred, s=5, color="red", label="Predicted")
plt.xlabel("Datetime")
plt.ylabel("Load")
plt.title("Energy Load:Actual vs Predicted")
plt.legend()
plt.tightlayout()
plt.show()

Result (for 10 values and test_size=0.4):