I have a script which contains two classes. (I'm obviously deleting a lot of stuff that I don't believe is relevant to the error I'm dealing with.) The eventual task is to create a decision tree, as I mentioned in this question.

Unfortunately, I'm getting an infinite loop, and I'm having difficulty identifying why. I've identified the line of code that's going haywire, but I would have thought the iterator and the list I'm adding to would be different objects. Is there some side effect of list's .append functionality that I'm not aware of? Or am I making some other blindingly obvious mistake?

class Dataset:
    individuals = [] #Becomes a list of dictionaries, in which each dictionary is a row from the CSV with the headers as keys
    def field_set(self): #Returns a list of the fields in individuals[] that can be used to split the data (i.e. have more than one value amongst the individuals
    def classified(self, predicted_value): #Returns True if all the individuals have the same value for predicted_value
    def fields_exhausted(self, predicted_value): #Returns True if all the individuals are identical except for predicted_value
    def lowest_entropy_value(self, predicted_value): #Returns the field that will reduce <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">entropy</a> the most
    def __init__(self, individuals=[]):

and

class Node:
    ds = Dataset() #The data that is associated with this Node
    links = [] #List of Nodes, the offspring Nodes of this node
    level = 0 #Tree depth of this Node
    split_value = '' #Field used to split out this Node from the parent node        node_value = '' #Value used to split out this Node from the parent Node

    def split_dataset(self, split_value):
        fields = [] #List of options for split_value amongst the individuals
        datasets = {} #Dictionary of Datasets, each one with a value from fields[] as its key
        for field in self.ds.field_set()[split_value]: #Populates the keys of fields[]
            fields.append(field)
            datasets[field] = Dataset()
        for i in self.ds.individuals: #Adds individuals to the datasets.dataset that matches their result for split_value
            datasets[i[split_value]].individuals.append(i) #<---Causes an infinite loop on the second hit
        for field in fields: #Creates subnodes from each of the datasets.Dataset options
            self.add_subnode(datasets[field],split_value,field)

    def add_subnode(self, dataset, split_value='', node_value=''):
    def __init__(self, level, dataset=Dataset()):

My initialisation code is currently:

if __name__ == '__main__':
    filename = (sys.argv[1]) #Takes in a CSV file
    predicted_value = "# class" #Identifies the field from the CSV file that should be predicted
    base_dataset = parse_csv(filename) #Turns the CSV file into a list of lists
    parsed_dataset = individual_list(base_dataset) #Turns the list of lists into a list of dictionaries
    root = Node(0, Dataset(parsed_dataset)) #Creates a root node, passing it the full dataset
    root.split_dataset(root.ds.lowest_entropy_value(predicted_value)) #Performs the first split, creating multiple subnodes
    n = root.links[0] 
    n.split_dataset(n.ds.lowest_entropy_value(predicted_value)) #Attempts to split the first subnode.