We Are Going To Discuss About **ValueError: cannot reshape array of size 3 into shape (1,80)**. So lets Start this Python Article.

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

**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[0], X.shape[1],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[0], X.shape[1],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[0], X.shape[1],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`

**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[0], X.shape[1],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[0], X.shape[1],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[0], X.shape[1],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[0], X.shape[1],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[0], X.shape[1],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[0], X.shape[1],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

So This is all About **This Tutorial.** Hope This Tutorial Helped You. Thank You.