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)]

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)

您可以使用df.as_matrix()函数并创建Numpy-array并传递它。

Y = df.pop()
X = df.as_matrix()
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2)
model.fit(x_train, y_train)
model.test(x_test)

shuffle = np.random.permutation(len(df))
test_size = int(len(df) * 0.2)
test_aux = shuffle[:test_size]
train_aux = shuffle[test_size:]
TRAIN_DF =df.iloc[train_aux]
TEST_DF = df.iloc[test_aux]

熊猫随机抽样也可以

train=df.sample(frac=0.8,random_state=200)
test=df.drop(train.index)

对于相同的random_state值，您将始终在训练集和测试集中获得相同的确切数据。这带来了一定程度的可重复性，同时还随机分离训练和测试数据。