我认为你还需要一个副本，而不是一个切片的数据框架，如果你想以后添加列。

msk = np.random.rand(len(df)) < 0.8
train, test = df[msk].copy(deep = True), df[~msk].copy(deep = True)

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

如果你需要根据你的数据集中的lables列来分割你的数据，你可以使用这个:

def split_to_train_test(df, label_column, train_frac=0.8):
    train_df, test_df = pd.DataFrame(), pd.DataFrame()
    labels = df[label_column].unique()
    for lbl in labels:
        lbl_df = df[df[label_column] == lbl]
        lbl_train_df = lbl_df.sample(frac=train_frac)
        lbl_test_df = lbl_df.drop(lbl_train_df.index)
        print '\n%s:\n---------\ntotal:%d\ntrain_df:%d\ntest_df:%d' % (lbl, len(lbl_df), len(lbl_train_df), len(lbl_test_df))
        train_df = train_df.append(lbl_train_df)
        test_df = test_df.append(lbl_test_df)

    return train_df, test_df

并使用它:

train, test = split_to_train_test(data, 'class', 0.7)

如果你想控制分割随机性或使用一些全局随机种子，你也可以传递random_state。

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

你也可以考虑分层划分为训练集和测试集。设定划分也随机生成训练集和测试集，但保留了原始的类比例。这使得训练集和测试集更好地反映原始数据集的属性。

import numpy as np  

def get_train_test_inds(y,train_proportion=0.7):
    '''Generates indices, making random stratified split into training set and testing sets
    with proportions train_proportion and (1-train_proportion) of initial sample.
    y is any iterable indicating classes of each observation in the sample.
    Initial proportions of classes inside training and 
    testing sets are preserved (stratified sampling).
    '''

    y=np.array(y)
    train_inds = np.zeros(len(y),dtype=bool)
    test_inds = np.zeros(len(y),dtype=bool)
    values = np.unique(y)
    for value in values:
        value_inds = np.nonzero(y==value)[0]
        np.random.shuffle(value_inds)
        n = int(train_proportion*len(value_inds))

        train_inds[value_inds[:n]]=True
        test_inds[value_inds[n:]]=True

    return train_inds,test_inds

df[train_inds]和df[test_inds]为您提供原始DataFrame df的训练和测试集。

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)