pandas own:

shuffle_data = df. Sample (frac = 1). Reset_index (drop = True)

sklearn:

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