Separate pandas dataframe using sklearn's KFold

Question

I had obtained the index of training set and testing set with code below.

df = pandas.read_pickle(filepath + filename)
kf = KFold(n_splits = n_splits, shuffl         


        

Answer 1

You need DataFrame.iloc for select rows by positions:

Sample:

np.random.seed(100)
df = pd.DataFrame(np.random.random((10,5)), columns=list('ABCDE'))
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)
result = next(kf.split(df), None)
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