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]

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)

在我的例子中，我想用特定的数字分割训练、测试和开发中的数据帧。我在这里分享我的解决方案

首先，为数据帧分配一个唯一的id(如果已经不存在的话)

import uuid
df['id'] = [uuid.uuid4() for i in range(len(df))]

以下是我的分割数字:

train = 120765
test  = 4134
dev   = 2816

分裂函数

def df_split(df, n):
    
    first  = df.sample(n)
    second = df[~df.id.isin(list(first['id']))]
    first.reset_index(drop=True, inplace = True)
    second.reset_index(drop=True, inplace = True)
    return first, second

现在分成培训，测试，开发

train, test = df_split(df, 120765)
test, dev   = df_split(test, 4134)

有很多有效的答案。又多了一个。 从sklearn。交叉验证导入train_test_split

#gets a random 80% of the entire set
X_train = X.sample(frac=0.8, random_state=1)
#gets the left out portion of the dataset
X_test = X.loc[~df_model.index.isin(X_train.index)]

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。

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