我有一个csv文件：

Index,X1,X2,X3,X4,X5,Y
1,-1.608052,-0.377992,1.204209,1.313808,1.218265,1
2,0.393766,0.630685,-1.222062,0.090558,0.015893,0
3,-0.466243,0.276972,2.519047,0.673745,0.16729,1
4,1.47121,-0.046791,-0.303291,-0.365437,1.989287,0
5,-1.672906,1.25588,-0.355706,0.123143,-2.241941,1

我想创建一个分类系统程序，数据在第二行。我想从第二排得到数据。我试着用下一个（列表）这样：

def load_DataTrain(filename):
    try:
        with open(filename, newline='') as iFile:
            return list(reader(iFile, delimiter=','))
            next(list)
    except FileNotFoundError as e:
        raise e

但它不工作，我得到一个错误，因为程序从第一行读取。我没有使用pandas或csv.reader来读取我的csv。这是我从Divyesh GitHub得到的代码：

from csv import reader
from sys import exit
from math import sqrt
from operator import itemgetter

def load_DataTrain(filename):
    try:
        with open(filename) as iFile:
            return list(reader(iFile, delimiter=','))
            next(list)
    except FileNotFoundError as e:
        raise e

def convert_to_float(DataTrain, mode):
    new_set = []
    try:
        if mode == 'training':
            for data in DataTrain:
                new_set.append([float(x) for x in data[:len(data)-1]] + [data[len(data)-1]])

        elif mode == 'test':
            for data in DataTrain:
                new_set.append([float(x) for x in data])

        else:
            print('Invalid mode, program will exit.')
            exit()

        return new_set

    except ValueError as v:
        print(v)
        print('Invalid data set format, program will exit.')
        exit()

def get_classes(training_set):
    return list(set([c[-1] for c in training_set]))

def find_neighbors(distances, k):
    return distances[0:k]

def find_response(neighbors, classes):
    votes = [0] * len(classes)

    for instance in neighbors:
        for ctr, c in enumerate(classes):
            if instance[-2] == c:
                votes[ctr] += 1

    return max(enumerate(votes), key=itemgetter(1))

def knn(training_set, test_set, k):
    distances = []
    dist = 0
    limit = len(training_set[0]) - 1

    # generate response classes from training data
    classes = get_classes(training_set)

    try:
        for test_instance in test_set:
            for row in training_set:
                for x, y in zip(row[:limit], test_instance):
                    dist += (x-y) * (x-y)
                distances.append(row + [sqrt(dist)])
                dist = 0

            distances.sort(key=itemgetter(len(distances[0])-1))

            # find k nearest neighbors
            neighbors = find_neighbors(distances, k)

            # get the class with maximum votes
            index, value = find_response(neighbors, classes)

            # Display prediction
            print('The predicted class for sample ' + str(test_instance) + ' is : ' + classes[index])
            print('Number of votes : ' + str(value) + ' out of ' + str(k))

            # empty the distance list
            distances.clear()

    except Exception as e:
        print(e)

def main():
    try:
        # get value of k
        k = int(input('Enter the value of k : '))

        # load the training and test data set
        training_file = input('Enter name of training data file : ')
        test_file = input('Enter name of test data file : ')
        training_set = convert_to_float(load_DataTrain(training_file), 'training')
        test_set = convert_to_float(load_DataTrain(test_file), 'test')

        if not training_set:
            print('Empty training set')

        elif not test_set:
            print('Empty test set')

        elif k > len(training_set):
            print('Expected number of neighbors is higher than number of training data instances')

        else:
            knn(training_set, test_set, k)

    except ValueError as v:
        print(v)

    except FileNotFoundError:
        print('File not found')

if __name__ == '__main__':
    main()

结果是：

could not convert string to float: 'Index'

我应该怎么做才能从csv文件的第二行读取？