Is there a way to write a python function that will create 'N' arrays? (see body)-CodePudding

I have an numpy array that is shape 20, 3. (So 20 3 by 1 arrays. Correct me if I'm wrong, I am still pretty new to python)

I need to separate it into 3 arrays of shape 20,1 where the first array is 20 elements that are the 0th element of each 3 by 1 array. Second array is also 20 elements that are the 1st element of each 3 by 1 array, etc.

I am not sure if I need to write a function for this. Here is what I have tried: Essentially I'm trying to create an array of 3 20 by 1 arrays that I can later index to get the separate 20 by 1 arrays.

a = np.load() #loads file
num=20 #the num is if I need to change array size
num_2=3
for j in range(0,num):
    for l in range(0,num_2):
        array_elements = np.zeros(3)
        array_elements[l] = a[j:][l]

This gives the following error: ''' ValueError: setting an array element with a sequence ''' I have also tried making it a dictionary and making the dictionary values lists that are appended, but it only gives the first or last value of the 20 that I need.

CodePudding user response：

Your array has shape (20, 3), this means it's a 2-dimensional array with 20 rows and 3 columns in each row.

You can access data in this array by indexing using numbers or ':' to indicate ranges. You want to split this in to 3 arrays of shape (20, 1), so one array per column. To do this you can pick the column with numbers and use ':' to mean 'all of the rows'. So, to access the three different columns: a[:, 0], a[:, 1] and a[:, 2].

You can then assign these to separate variables if you wish e.g. arr = a[:, 0] but this is just a reference to the original data in array a. This means any changes in arr will also be made to the corresponding data in a.

If you want to create a new array so this doesn't happen, you can easily use the .copy() function. Now if you set arr = a[:, 0].copy(), arr is completely separate to a and changes made to one will not affect the other.

CodePudding user response：

Essentially you want to group your arrays by their index. There are plenty of ways of doing this. Since numpy does not have a group by method, you have to horizontally split the arrays into a new array and reshape it.

old_length = 3
new_length = 20
a = np.array(np.hsplit(a, old_length)).reshape(old_length, new_length)

Edit: It appears you can achieve the same effect by rotating the array -90 degrees. You can do this by using rot90 and setting k=-1 or k=3 telling numpy to rotate by 90 k times.

a = np.rot90(a, k=-1)