Iloc, дающий 'IndexError: единственный позиционный индексатор вне границ'

Я пытаюсь кодировать некоторую информацию для чтения в модель машинного обучения, используя следующую

import numpy as np
import pandas as pd
import matplotlib.pyplot as py

Dataset = pd.read_csv('filename.csv', sep = ',')

X = Dataset.iloc[:,:-1].values
Y = Dataset.iloc[:,18].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()

однако я получаю сообщение об ошибке

runfile('C:/Users/name/Desktop/Machine Learning/Data preprocessing      template.py', wdir='C:/Users/taylorr2/Desktop/Machine Learning')
Traceback (most recent call last):

  File "<ipython-input-141-a5d1cd02c2df>", line 1, in <module>
    runfile('C:/Users/name/Desktop/Machine Learning/Data preprocessing  template.py', wdir='C:/Users/taylorr2/Desktop/Machine Learning')

  File "C:\Users\name\AppData\Local\Continuum\Anaconda2\lib\site-packages\spyder\utils\site\sitecustomize.py", line 866, in runfile
    execfile(filename, namespace)

  File "C:\Users\name\AppData\Local\Continuum\Anaconda2\lib\site-packages\spyder\utils\site\sitecustomize.py", line 87, in execfile
exec(compile(scripttext, filename, 'exec'), glob, loc)

  File "C:/Users/name/Desktop/Machine Learning/Data preprocessing template.py", line 8, in <module>
Y = Dataset.iloc[:,18].values

   File "C:\Users\name\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexing.py", line 1310, in __getitem__
return self._getitem_tuple(key)

   File "C:\Users\name\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexing.py", line 1560, in _getitem_tuple
self._has_valid_tuple(tup)

   File "C:\Users\name\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexing.py", line 151, in _has_valid_tuple
if not self._has_valid_type(k, i):

   File "C:\Users\name\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexing.py", line 1528, in _has_valid_type
return self._is_valid_integer(key, axis)

   File "C:\Users\name\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexing.py", line 1542, in _is_valid_integer
raise IndexError("single positional indexer is out-of-bounds")

IndexError: single positional indexer is out-of-bounds

Я прочитал здесь вопрос о той же ошибке и попробовал

import numpy as np
import pandas as pd
import matplotlib.pyplot as py

Dataset = pd.read_csv('filename.csv', sep = ',')

table = Dataset.find(id='AlerId')
rows = table.find_all('tr')[1:]
data = [[cell.text for cell in row.find_all('td')] for row in rows]
Dataset1 = pd.DataFrame(data=data, columns=columns)

X = Dataset1.iloc[:,:-1].values
Y = Dataset1.iloc[:,18].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()

Однако я думаю, что это могло бы просто смутить меня больше и теперь я в еще большей степени.

Любые предложения?

Ответы

Y = Dataset.iloc[:,18].values

@slonopotam Even I'm getting the same error plz do help in my code. reply soon sir      


import pandas as pd
from matplotlib import pyplot
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.arima_model import ARIMAResults
from sklearn.metrics import mean_squared_error
from math import sqrt
import numpy
# load and prepare datasets
dataset = pd.Series.from_csv('dataset.csv',sep=' ')
X = dataset.values.astype('float32')
history = [x for x in X]
validation = pd.Series.from_csv('validation.csv',sep=' ')
y = validation.values.astype('float32')


#for column in dataset.columns:
    #if dataset[column].dtype == type(object):
        #le = LabelEncoder()
        #dataset[column] = le.fit_transform(dataset[column])

# load model
model_fit = ARIMAResults.load('model.pkl')
bias = numpy.load('model_bias.npy')


# make first prediction
predictions = list()
yhat = bias + float(model_fit.forecast()[0])
predictions.append(yhat)
history.append(y[0])
print('>Predicted=%.3f, Expected=%3.f' % (yhat, y[0]))
# rolling forecasts
for i in range(1, len(y)):
    # predict
    model = ARIMA(history, order=(2,1,0))
    model_fit = model.fit(trend='nc', disp=0)
    yhat = bias + float(model_fit.forecast()[0])
    predictions.append(yhat)
    # observation
    obs = y[i]
    history.append(obs)
    print('>Predicted=%.3f, Expected=%3.f' % (yhat, obs))
# report performance
mse = mean_squared_error(y, predictions)
rmse = sqrt(mse)
print('RMSE: %.3f' % rmse)
pyplot.plot(y)
pyplot.plot(predictions, color='red')
pyplot.show()