要分成两个以上的类，如训练、测试和验证，可以这样做:

probs = np.random.rand(len(df))
training_mask = probs < 0.7
test_mask = (probs>=0.7) & (probs < 0.85)
validatoin_mask = probs >= 0.85


df_training = df[training_mask]
df_test = df[test_mask]
df_validation = df[validatoin_mask]

这将把大约70%的数据用于训练，15%用于测试，15%用于验证。

如果你希望有一个数据帧和两个数据帧(不是numpy数组)，这应该可以做到:

def split_data(df, train_perc = 0.8):

   df['train'] = np.random.rand(len(df)) < train_perc

   train = df[df.train == 1]

   test = df[df.train == 0]

   split_data ={'train': train, 'test': test}

   return split_data

Scikit Learn的train_test_split就是一个很好的例子。它将拆分numpy数组和数据框架。

from sklearn.model_selection import train_test_split

train, test = train_test_split(df, test_size=0.2)

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

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