Iloc giving 'IndexError: single positional indexer out of bounds'

Question

I am trying to encode some reading information into a machine learning model using the following

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() 

however i get an error

 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 

I read the same error question here and tried

 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() 

However, I think that this could just confuse me more and now I am even more so.

Any suggestions?

Answer 1

This error is caused by:

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

Indexing here goes beyond the bounds, most likely because your dataset has less than 19 columns, so column 18 does not exist. The code below does not use Y at all, so you can just comment on this line.

Answer 2

 @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() 

Answer 3

Essa mensagem de erro estava aparecendo no meu programa. Resolvi o problemma mudando o separator. Coloquei sep = ';'

O iloc não tem values. Isso Tambem Wai Dar Erro.