Linked Questions

I am not sure about the difference between (N,) and (N,1) in numpy. Assuming both are some features, they have same N dimension, and both have one sample. What's the difference? a = np.ones((10,)) ...

I noticed that for a rank 1 array with 3 elements numpy returns (3,) for the shape. I know that this tuple represents the size of the array along each dimension, but why isn't it (3,1)? import numpy ...

I've recently encountered a couple of bugs due to numpy arrays being in the shape (x,) - these can easily be fixed by the snippet below a = np.array([1,2,3,4]) #this form produced a bug a.shape >&...

I find that I often have to reshape (5,) into (5,1) in order to use dot product. What can't I just use a dot product with a vector of shape (5,)?

I have an array converted from list, when I try to get its shape, I got only one number. like this: list1=[1,2,3,4,5] a1=numpy.array(list1) print a1.shape and I got (5,) and then I ...

I have been curious about this for some time. I can live with that, but it always bites me when enough care is not taken, so I decide to post it here. Suppose the following example (Numpy version = 1....

What is the difference between numpy.zeros(n) and numpy.zeros(n,1)? The output for the first statement is [0 0 ..... n times] whereas the second one is ([0] [0] .... n rows)

I have this NumPy array: X = numpy.linspace(1, 10, 10) I believe the output will become (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) Why is the shape of this array (10,)? I don't understand why it isn't (1,10).

I'm using train_test_split with my dataframe, my code looks something like this: df = pd.read_csv('data.csv', header=None) y = df[0] X_train, X_test, y_train, y_test = train_test_split(df, y,...

What is the difference between train_x = np.asarray([[0.0], [1.0], [2.0], [3.0], [4.0]]) train_y = np.asarray([1.0, 1.1, 3.0, 2.0, 5.0]) in Python numpy.asarray assignment? What is the meaning of [] ...

These two seem both array to me. # suppose that test_data has [1000,728] shape print (test_data[0:1,:].shape) # output [1,728] print (test_data[0,:].shape) # output [728,] I tried these two arrays ...

What does this mean in numpy coding, (4,)? You have an array and you run the shape of it and it gives you this answer. What does it mean? x = np.array([1, 2, 3, 4]) x.shape (4,)

What is the difference between numpy arrays with shapes of form (n,) and (n,1), where n is some number (like 150)? For example, x_dat = np.random.normal(size=150) returns an array of shape (150,), ...

What does (13, ) mean here? without column?

In Numpy, what is the difference between an array with shape (4,) and an array with shape (4,1)? Is it that the latter is a vector?