有人可以告诉我最后一个循环在做什么吗？

import os
import tarfile
from six.moves import urllib
import pandas as pd
import hashlib
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit 

DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/"
HOUSING_PATH = os.path.join("datasets", "housing")
HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"

def fetch_housing_data(housing_url=HOUSING_URL, housing_path= HOUSING_PATH):
    if not os.path.isdir(housing_path):
        os.makedirs(housing_path)
    tgz_path = os.path.join(housing_path, "housing.tgz")
    urllib.request.urlretrieve(housing_url, tgz_path)
    housing_tgz = tarfile.open(tgz_path)
    housing_tgz.extractall(path=housing_path)
    housing_tgz.close()
#getting the housing data 


def load_housing_data(housing_path=HOUSING_PATH):
    csv_path = os.path.join(housing_path, "housing.csv")
    return pd.read_csv(csv_path)
#that function loaded the data in a panda datafrome object 


#need to call the function to get the housing data 
fetch_housing_data()
housing = load_housing_data()
housing.head()

#total bedrooms doesnt match entries deal with later 
#ocean proximity holds an object, since its in csv file still can contain text
housing.describe()
#describes the output of the housing information 


%matplotlib inline 
import matplotlib.pyplot as plt 
housing.hist(bins=50,figsize=(20,15))
plt.show()
#creates a histogram of the data set, x axis is the range of hosuing prices, y axis number of instances of housing prices at that 
#given range 
#income data has been scaled by max 15 and .5 for lower 

#since the data of housing prices has been capped at 500k posssible delete that data set 
#thus so our model wont learn those bad values because it may not be 500k thus labels could be off 
#tail heavy because its 200K plus for example so just barel a dollar more would make it (left)

import numpy as np 

def split_train_test(data,test_ratio):
    shuffled_indices = np.random.permutation(len(data))
    #a randomized array with the same length as the input data so all data 
    test_set_size = int(len(data)*test_ratio)
    #mutliplying by a ratio to see the difference of the data 
    test_indices = shuffled_indices[:test_set_size]
    train_indices = shuffled_indices[test_set_size:]
    #taking the test of the beggining because of the entry 
    #taking rest for training 
    return data.iloc[train_indices],data.iloc[test_indices]
#redo the variable since outside the cells 
housing = load_housing_data()

#creating a category of income prices that is stratified 
housing["income_cat"] = np.ceil(housing["median_income"]/1.5)
housing["income_cat"].where(housing["income_cat"]<5,5.0,inplace = True)
#since now the income has been set into categories 
#stratified because not even split reprisentative of the population 
split = StratifiedShuffleSplit(n_splits=1,test_size = 0.2,random_state=42)

这是代码末尾的循环

for train_index,test_index in split.split(housing,housing["income_cat"]):
    strat_train_set = housing.loc[train_index]
    strat_test_set = housing.loc[test_index]

有人可以告诉我最后一个for循环在做什么吗？基本上，它应该将数据集分层进行训练和测试，但是我尤其对循环头感到困惑，因为为什么整个数据框对象都在第一个参数中，然后在其后是收入类别部分。参照创建的每个收入类别进行分层，从而操纵整个数据框对象中的所有后续类别吗？