How can I evenly split up a pandas.DataFrame into n-groups?

Question

I need to perform n-fold (in my particular case, a 5-fold) cross validation on a dataset that I've stored in a pandas.DataFrame. My current way seems to rearrange the row labels;

spreadsheet1 = pd.ExcelFile("Testing dataset.xlsx") 
dataset = spreadsheet1.parse('Sheet1') 

data = 5 * [pd.DataFrame()]

i = 0
while(i  < len(dataset)):
    j = 0
    while(j < 5 and i < len(dataset)):
        data[j] = (data[j].append(dataset.iloc[i])).reset_index(drop = True)
        i += 1
        j += 1

How can I split my DataFrame efficiently/intelligently without tampering with the order of the columns?

Answer 1

Use np.array_split to break it up into a list of "evenly" sized DataFrames. You can shuffle too if you sample the full DataFrame

import pandas as pd
import numpy as np

df = pd.DataFrame(np.arange(24).reshape(-1,2), columns=['A', 'B'])
N = 5    

np.array_split(df, N)
#np.array_split(df.sample(frac=1), N)  # Shuffle and split

---

[   A  B
 0  0  1
 1  2  3
 2  4  5,
     A   B
 3   6   7
 4   8   9
 5  10  11,
     A   B
 6  12  13
 7  14  15,
     A   B
 8  16  17
 9  18  19,
      A   B
 10  20  21
 11  22  23]