

In [1]:

    
import numpy as np
import pandas as pd
from sklearn.utils import resample

original data



In [2]:

    
mat = np.arange(45).reshape(-1,3)
mat









    Out[2]:





array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11],
       [12, 13, 14],
       [15, 16, 17],
       [18, 19, 20],
       [21, 22, 23],
       [24, 25, 26],
       [27, 28, 29],
       [30, 31, 32],
       [33, 34, 35],
       [36, 37, 38],
       [39, 40, 41],
       [42, 43, 44]])

dataframe to hold bootstrapped dataset



In [3]:

    
df = pd.DataFrame(np.nan, index = range(1500), columns = list('abc'))

simple bootstrap with replacement



In [4]:

    
cnt = 5
for i in range(300):
    df.iloc[cnt-5:cnt,:] = resample(mat,n_samples = 5, replace = True)
    cnt += 5
df.head()

simple bootstrap with replacement and added noise



In [5]:

    
cnt = 5
for i in range(300):
    df.iloc[cnt-5:cnt,:] = resample(mat,n_samples = 5, replace = True) + np.random.randint(3, size=(5,3))
    cnt += 5
df.head()

	a	b	c
0	27.0	28.0	29.0
1	0.0	1.0	2.0
2	0.0	1.0	2.0
3	42.0	43.0	44.0
4	27.0	28.0	29.0

	a	b	c
0	41.0	41.0	41.0
1	5.0	6.0	5.0
2	44.0	45.0	45.0
3	23.0	22.0	24.0
4	34.0	34.0	36.0