我有一個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

我想創建一個分類系統程序，數據在第二行。我想從第二排得到數據。我試過下一個（列表）如下：

^{pr2}$

但它不起作用，我得到一個錯誤，因為程序從第一行讀取。我沒有用熊貓或者csv.reader讀我的csv。這是我從Divyesh GitHub得到的代碼：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()

結果是：could not convert string to float: 'Index'

我應該怎么做才能從csv文件的第二行讀取？在

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