# ValueError: cannot reshape array of size 3 into shape (1,80)

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## ValueError: cannot reshape array of size 3 into shape (1,80)

1. How to solve ValueError: cannot reshape array of size 3 into shape (1,80)

try the following with the two different values for `n`:
`import numpy as np n = 10160 #n = 10083 X = np.arange(n).reshape(1,-1) np.shape(X) X = X.reshape([X.shape, X.shape,1]) X_train_1 = X[:,0:10080,:] X_train_2 = X[:,10080:10160,:].reshape(1,80) np.shape(X_train_2)`
If you cannot make sure that `X` is 10160 long I suggest one of the following solutions:
`X_train_1` with 10080 samples, `X_train_2` with the rest:
`X = X.reshape([X.shape, X.shape,1]) X_train_1 = X[:,0:10080,:] # X_train_1 with 10080 samples X_train_2 = X[:,10080:,:].reshape(1,-1) # X_train_2 with the remaining samples`
Or `X_train_2` with 80 samples, `X_train_1` with the rest:
`X = X.reshape([X.shape, X.shape,1]) X_train_1 = X[:,0:-80,:] # X_train_1 with the remaining samples X_train_2 = X[:,-80:,:].reshape(1,80) # X_train_2 with 80 samples`

2. ValueError: cannot reshape array of size 3 into shape (1,80)

try the following with the two different values for `n`:
`import numpy as np n = 10160 #n = 10083 X = np.arange(n).reshape(1,-1) np.shape(X) X = X.reshape([X.shape, X.shape,1]) X_train_1 = X[:,0:10080,:] X_train_2 = X[:,10080:10160,:].reshape(1,80) np.shape(X_train_2)`
If you cannot make sure that `X` is 10160 long I suggest one of the following solutions:
`X_train_1` with 10080 samples, `X_train_2` with the rest:
`X = X.reshape([X.shape, X.shape,1]) X_train_1 = X[:,0:10080,:] # X_train_1 with 10080 samples X_train_2 = X[:,10080:,:].reshape(1,-1) # X_train_2 with the remaining samples`
Or `X_train_2` with 80 samples, `X_train_1` with the rest:
`X = X.reshape([X.shape, X.shape,1]) X_train_1 = X[:,0:-80,:] # X_train_1 with the remaining samples X_train_2 = X[:,-80:,:].reshape(1,80) # X_train_2 with 80 samples`

## Solution 1

try the following with the two different values for `n`:

``````import numpy as np
n = 10160
#n = 10083
X = np.arange(n).reshape(1,-1)
np.shape(X)

X = X.reshape([X.shape, X.shape,1])
X_train_1 = X[:,0:10080,:]
X_train_2 = X[:,10080:10160,:].reshape(1,80)
np.shape(X_train_2)
``````

If you cannot make sure that `X` is 10160 long I suggest one of the following solutions:

`X_train_1` with 10080 samples, `X_train_2` with the rest:

``````X = X.reshape([X.shape, X.shape,1])
X_train_1 = X[:,0:10080,:] # X_train_1 with 10080 samples
X_train_2 = X[:,10080:,:].reshape(1,-1) # X_train_2 with the remaining samples
``````

Or `X_train_2` with 80 samples, `X_train_1` with the rest:

``````X = X.reshape([X.shape, X.shape,1])
X_train_1 = X[:,0:-80,:] # X_train_1 with the remaining samples
X_train_2 = X[:,-80:,:].reshape(1,80) # X_train_2 with 80 samples
``````

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## Conclusion 