您需要DataFrame.iloc来按位置选择行：

样本：

np.random.seed(100)
df = pd.DataFrame(np.random.random((10,5)), columns=list('ABCDE'))
#changed default index values
df.index = df.index * 10
print (df)
           A         B         C         D         E
0   0.543405  0.278369  0.424518  0.844776  0.004719
10  0.121569  0.670749  0.825853  0.136707  0.575093
20  0.891322  0.209202  0.185328  0.108377  0.219697
30  0.978624  0.811683  0.171941  0.816225  0.274074
40  0.431704  0.940030  0.817649  0.336112  0.175410
50  0.372832  0.005689  0.252426  0.795663  0.015255
60  0.598843  0.603805  0.105148  0.381943  0.036476
70  0.890412  0.980921  0.059942  0.890546  0.576901
80  0.742480  0.630184  0.581842  0.020439  0.210027
90  0.544685  0.769115  0.250695  0.285896  0.852395

from sklearn.model_selection import KFold

#added some parameters
kf = KFold(n_splits = 5, shuffle = True, random_state = 2)
kf_split = kf.split(df) 
result = next(kf_split)
print (result)
(array([0, 2, 3, 5, 6, 7, 8, 9]), array([1, 4]))

train = df.iloc[result[0]]
test =  df.iloc[result[1]]

print (train)
           A         B         C         D         E
0   0.543405  0.278369  0.424518  0.844776  0.004719
20  0.891322  0.209202  0.185328  0.108377  0.219697
30  0.978624  0.811683  0.171941  0.816225  0.274074
50  0.372832  0.005689  0.252426  0.795663  0.015255
60  0.598843  0.603805  0.105148  0.381943  0.036476
70  0.890412  0.980921  0.059942  0.890546  0.576901
80  0.742480  0.630184  0.581842  0.020439  0.210027
90  0.544685  0.769115  0.250695  0.285896  0.852395

print (test)
           A         B         C         D         E
10  0.121569  0.670749  0.825853  0.136707  0.575093
40  0.431704  0.940030  0.817649  0.336112  0.175410