试试这个

train_data = train_data.values.tolist()
validation_data = validation_data.values.tolist()
test_data = test_data.values.tolist()
d = {'train_data ' : train_data ,
'validation_data ' : validation_data ,
'test_data ' : test_data 
}
df = pd.DataFrame(data = d)

值得注意的是，如果这些 Dataframe 只有一列，则.values.tolist()有效，如果没有列，则指定为EX。：train_data ['COLUMN'].values.tolist()

试试这个

import pandas as pd
import os

from google.colab import drive
drive.mount('/content/drive')

os.chdir('/content/drive/My Drive/NLP-Datasets/Question2_Data/')

train_data = 'train.xlsx'
validation_data = 'valid.xlsx'
test_data = 'test.xlsx'

paths = [train_data, validation_data , test_data ]
dfs = {p: pd.read_excel(p) for p in paths}

更新：可以使用Python中的datasets库从磁盘上的三个文件创建数据集，如下所示：

from google.colab import drive
from datasets import Dataset
import pandas as pd

# Mount Google Drive
drive.mount('/content/drive')

# Load train, validation, and test data
train_data = pd.read_excel('/content/drive/My Drive/NLP-Datasets/Question2_Data/train.xlsx')
validation_data = pd.read_excel('/content/drive/My Drive/NLP-Datasets/Question2_Data/valid.xlsx')
test_data = pd.read_excel('/content/drive/My Drive/NLP-Datasets/Question2_Data/test.xlsx')

# Convert data to dictionary format
train_dict = train_data.to_dict(orient='list')
validation_dict = validation_data.to_dict(orient='list')
test_dict = test_data.to_dict(orient='list')

# Create a dataset from the data
dataset = Dataset.from_dict({
    'train': train_dict,
    'validation': validation_dict,
    'test': test_dict
})

# Print the shapes of the data
print(dataset['train'].shape)
print(dataset['validation'].shape)
print(dataset['test'].shape)