Pop_OS下VSC运行Python代码读取下载目录数据集报FileNotFoundError如何解决

1. 文件路径拼写错误 #

你当前代码中读取文件的路径末尾多了一个多余的英文句号，是触发FileNotFoundError的核心原因：
错误写法：

df= pd.read_csv('/home/morgankemp/Downloads/parkinsons.data.') 

删除路径末尾多余的.即可匹配到真实文件parkinsons.data。如果修改后仍报错，可以在读取文件前加入以下代码验证路径与文件是否匹配：

import os
target_path = "/home/morgankemp/Downloads/parkinsons.data"
print(os.path.exists(target_path)) # 输出True说明路径正确，输出False需要核对文件实际存储位置
# 打印下载目录下所有文件，确认文件名是否拼写正确
print(os.listdir("/home/morgankemp/Downloads"))

2. 缺失函数导入 #

你的代码末尾调用了accuracy_score计算准确率，但没有提前导入该函数，路径问题修复后运行仍会抛出名称错误，需要在导入部分新增一行代码：

from sklearn.metrics import accuracy_score

修正后可直接运行的完整代码 #

import numpy as np 
import pandas as pd 
import os, sys 
from sklearn.preprocessing import MinMaxScaler 
from xgboost import XGBClassifier 
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score # 新增缺失的导入

#DataFlair - Read the data
df= pd.read_csv('/home/morgankemp/Downloads/parkinsons.data') 
df.head()

#DataFlair - Get the features and labels
features=df.loc[:,df.columns!='status'].values[:,1:]
labels=df.loc[:,'status'].values

#DataFlair - Get the count of each label (0 and 1) in labels
print(labels[labels==1].shape[0], labels[labels==0].shape[0])

#DataFlair - Scale the features to between -1 and 1
scaler=MinMaxScaler((-1,1))
x=scaler.fit_transform(features)
y=labels

#DataFlair - Split the dataset
x_train,x_test,y_train,y_test=train_test_split(x, y, test_size=0.2, random_state=7)

#DataFlair - Train the model
model=XGBClassifier()
model.fit(x_train,y_train)

# DataFlair - Calculate the accuracy
y_pred=model.predict(x_test)
print(accuracy_score(y_test, y_pred)*100)

内容的提问来源于stack exchange，提问作者780_APM