函数实现将 DataFrame 数据直接划分为测试集训练集

 虽然 Scikit-Learn 有可以划分数据集的函数 train_test_split ，但在有些特殊情况我们只希望它将 DataFrame 数据直接划分为 train, test 而不是像 train_test_split 返回四个值。这里写了一个类似功能的函数：

import numpy as np
import pandas as pd
from sklearn.utils import shuffle as reset
def train_test_split(data, test_size=0.3, shuffle=True, random_state=None):
    '''Split DataFrame into random train and test subsets
    Parameters
    ----------
    data : pandas dataframe, need to split dataset.
    test_size : float
        If float, should be between 0.0 and 1.0 and represent the
        proportion of the dataset to include in the train split.
    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.
    shuffle : boolean, optional (default=None)
        Whether or not to shuffle the data before splitting. If shuffle=False
        then stratify must be None.
    '''
    if shuffle:
        data = reset(data, random_state=random_state)
    train = data[int(len(data)*test_size):].reset_index(drop = True)
    test  = data[:int(len(data)*test_size)].reset_index(drop = True)
    return train, test

效果如下：