您需要将pandas数据帧转换为numpy数组，然后将numpy数组转换回数据帧

 import pandas as pd
df=pd.read_csv('/content/drive/My Drive/snippet.csv', sep='\t')
from sklearn.model_selection import train_test_split

train, test = train_test_split(df, test_size=0.2)
train1=pd.DataFrame(train)
test1=pd.DataFrame(test)
train1.to_csv('/content/drive/My Drive/train.csv',sep="\t",header=None, encoding='utf-8', index = False)
test1.to_csv('/content/drive/My Drive/test.csv',sep="\t",header=None, encoding='utf-8', index = False)

我会用K-fold交叉验证。 它已被证明比train_test_split提供更好的结果。下面是一篇关于如何在sklearn中应用它的文章，来自文档本身:https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

我将使用scikit-learn自己的training_test_split，并从索引生成它

from sklearn.model_selection import train_test_split


y = df.pop('output')
X = df

X_train,X_test,y_train,y_test = train_test_split(X.index,y,test_size=0.2)
X.iloc[X_train] # return dataframe train

import pandas as pd

from sklearn.model_selection import train_test_split

datafile_name = 'path_to_data_file'

data = pd.read_csv(datafile_name)

target_attribute = data['column_name']

X_train, X_test, y_train, y_test = train_test_split(data, target_attribute, test_size=0.8)

可以使用~(波浪符)排除使用df.sample()采样的行，让pandas单独处理索引的采样和过滤，以获得两个集。

train_df = df.sample(frac=0.8, random_state=100)
test_df = df[~df.index.isin(train_df.index)]

如果你想把它分成训练集、测试集和验证集，你可以使用这个函数:

from sklearn.model_selection import train_test_split
import pandas as pd

def train_test_val_split(df, test_size=0.15, val_size=0.45):
    temp, test = train_test_split(df, test_size=test_size)
    total_items_count = len(df.index)
    val_length = total_items_count * val_size
    new_val_propotion = val_length / len(temp.index) 
    train, val = train_test_split(temp, test_size=new_val_propotion)
    return train, test, val