How to generate 2d numpy array?

Question

I'm trying to generate a 2d numpy array with the help of generators:

x = [[f(a) for a in g(b)] for b in c]

And if I try to do something like this:

x = np.array([np.array([f(a) for a in g(b)]) for b in c])

I, as expected, get a np.array of np.array. But I want not this, but ndarray, so I can get, for example, column in a way like this:

y = x[:, 1]

So, I'm curious whether there is a way to generate it in such a way.

Of course it is possible with creating npdarray of required size and filling it with required values, but I want a way to do so in a line of code.

Answer 1

To create a new array, it seems numpy.zeros is the way to go

import numpy as np
a = np.zeros(shape=(x, y))

You can also set a datatype to allocate it sensibly

>>> np.zeros(shape=(5,2), dtype=np.uint8)
array([[0, 0],
       [0, 0],
       [0, 0],
       [0, 0],
       [0, 0]], dtype=uint8)
>>> np.zeros(shape=(5,2), dtype="datetime64[ns]")
array([['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000'],
       ['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000'],
       ['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000'],
       ['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000'],
       ['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000']],
      dtype='datetime64[ns]')

See also

• How do I create an empty array/matrix in NumPy?
• np.full(size, 0) vs. np.zeros(size) vs. np.empty()