# Is there an efficient method for converting numpy.ndarray to a list?

I have a Y array of shape (this is just an example, I have huge data in this shape). The array is formed with numpy vstack and hstack (I don't want to change the way I get this array as I got it with some tricky operations):

`Y=array([[1, 1,2], [1, 2,0], [-1, 3,1], [-1, 2,2]]) y=[1,1,-1,-1] Y1=list(Y)`

Now I am inputting data into the libsvm function, this library expects the input parameters to be in the form of a dictionary, list or tuple. Hence the code for it:

```
prob=svm_problem(y, Y1)
```

The above function throws an error that "xi" must be a dictionary, list or tuple. "Another way I know is to convert Y to a list iteratively. The way to do this is:

```
Y1=[]
for i in range(0, Y.shape[0]):
Y1.append(list(Y[i])
```

The above method works well, but is slow given the huge data I have. Is there any faster method to achieve the same?

+3

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