Pandas IndexError报错解决：使用Matplotlib绘制实际值与预测值时的索引问题

Let's break down what's causing this error and how to fix it quickly.

The Root Cause #

Your error happens because you're passing the entire X_test DataFrame to .iloc in these lines:

plt.scatter(data['timestamp'].iloc[X_test], y_test, s=5,label="Actual")
plt.scatter(data['timestamp'].iloc[X_test], y_pred, s=5, color='red', label="Predicted")

.iloc only accepts integer positions, lists/arrays of integers, or boolean arrays—it can't handle a full DataFrame as an indexer. Luckily, train_test_split preserves the original index from your data DataFrame, so X_test.index already holds the exact row labels you need to fetch the corresponding timestamps.

The Fix #

Replace .iloc[X_test] with .loc[X_test.index] in your scatter plot lines. This uses the original index labels to pull the correct timestamp values that align with your test data. Here's the corrected snippet:

plt.scatter(data['timestamp'].loc[X_test.index], y_test, s=5, label="Actual")
plt.scatter(data['timestamp'].loc[X_test.index], y_pred, s=5, color='red', label="Predicted")

Full Corrected Code #

Here's your complete code with the fix applied:

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

path = '/content/drive/MyDrive/ML_DATASETS/energy.csv'
data = pd.read_csv(path)
data['timestamp'] = pd.to_datetime(data['timestamp'])
data['time_num'] = range(len(data))

X = data[['time_num', 'temp']]
y = data['load']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

plt.figure(figsize=(15, 5))
# Fixed indexer to use X_test's original row labels
plt.scatter(data['timestamp'].loc[X_test.index], y_test, s=5, label="Actual")
plt.scatter(data['timestamp'].loc[X_test.index], y_pred, s=5, color='red', label="Predicted")
plt.xlabel("Datetime")
plt.ylabel("Load")
plt.title("Energy Load:Actual vs Predicted")
plt.legend()
plt.tight_layout()
plt.show()

Why This Works #

• X_test.index retains the original row labels from your data DataFrame that were assigned to the test set.
• .loc uses these labels to fetch the exact timestamp values that match your y_test and y_pred data points, ensuring proper alignment for your plot.

内容的提问来源于stack exchange，提问作者Rima 123