IndexError: too many indices for array when creating a train test split

Question

I am creating a Neural Network and currently I am working on the; train, test split but I am getting the error IndexError: too many indices for array My code is:

import csv
import math
import numpy as np 
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
import datetime
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

X1 = Values[1:16801] #16,800 values
Y1 = P1[1:16801]#16,800 values

train_size = int(len(X1) * 0.67)
test_size = len(X1) - train_size

train, test = X1[0:train_size,], X1[train_size:len(X1),]
def Data(X1, look_back=1):
     dataX, dataY = [], []
     for i in range(len(X1)-look_back-1):
         a = X1[i:(i+look_back), 0]
         dataX.append(a)
         dataY.append(Y1[i + look_back, 0])
     return numpy.array(dataX), numpy.array(dataY)

look_back = 1
trainX, testX = Data(train, look_back)

testX, testY = Data(test, look_back)

look_back = 1
trainX, testX = Data(train, look_back)

testX, testY = Data(test, look_back)

I have 16,800 values for X1 which look like:

[0.03454225 0.02062136 0.00186715 ... 0.92857565 0.64930691 0.20325924]

And my Y1 data looks like: [ 2.25226244 1.44078451 0.99174488 ... 12.8397099 9.75722427 7.98525797]

My traceback error message is:

IndexError                                Traceback (most recent call last)
<ipython-input-11-afedcaa56e0b> in <module>()
     86 
     87 look_back = 1
---> 88 trainX, testX = Data_split(train, look_back)
     89 
     90 testX, testY = Data_split(test, look_back)

<ipython-input-11-afedcaa56e0b> in Data(X1, look_back)
     78     dataX, dataY = [], []
     79     for i in range(len(X1)-look_back-1):
---> 80         a = X1[i:(i+look_back), 0]
     81         dataX.append(a)
     82         dataY.append(Y1[i + look_back, 0])

IndexError: too many indices for array

I asked a very similar question previously and got a answer but unfortunately I cannot apply that solution to this error

Answer 1

The problem is with the dimension of an array. you are trying to access element with multiple dimensions indexes which don't exist. look at line number 80.

 a = X1[i:(i+look_back), 0]   in your case metrics is just single dimention.

sample 2d metrics representation (,)

"," is the reference to the two-dimensional array with row and column but unfortunately, you are having X1 as ndarray.

[0.03454225 0.02062136 0.00186715 ... 0.92857565 0.64930691 0.20325924]

Similar problem example:-

>>> np.ndarray(4)
array([2.0e-323, 1.5e-323, 2.0e-323, 1.5e-323])
>>> a[1:2,0]
Traceback (most recent call last):
  File "<pyshell#38>", line 1, in <module>
    a[1:2,0]
IndexError: too many indices for array
>>> a[1:2]
array([-2.68156159e+154])
>>>