numpy array with n prefilled columns-CodePudding

I need to specialized numpy arrays. Assume I have a function:

    def gen_array(start, end, n_cols):

It should behave like this, generating three columns where each column goes from start (inclusive) to end (exclusive):

>>> gen_array(20, 25, 3)
array([[20, 20, 20],
       [21, 21, 21],
       [22, 22, 22],
       [23, 23, 23],
       [24, 24, 24]])

My rather naïve implementation looks like this:

def gen_array(start, end, n_columns):
    a = np.arange(start, end).reshape(end-start, 1) # create a column vector from start to end
    return np.dot(a, [np.ones(n_columns)])          # replicate across n_columns

(It's okay, though not required, that the np.dot converts values to floats.)

I'm sure there's a better, more efficient and more numpy-ish way to accomplish the same thing. Suggestions?

Update

Buildin on a suggestion by @msi_gerva to use np.tile, my latest best thought is:

def gen_array(start, end, n_cols):
    return np.tile(np.arange(start, end).reshape(-1, 1), (1, n_cols))

... which seems pretty good to me.

CodePudding user response：

In addition to numpy.arange and numpy.reshape, use numpy.repeat to extend your data.

import numpy as np

def gen_array(start, end, n_cols):
    return np.arange(start, end).repeat(n_cols).reshape(-1, n_cols)

print(gen_array(20, 25, 3))
# [[20 20 20]
#  [21 21 21]
#  [22 22 22]
#  [23 23 23]
#  [24 24 24]]

CodePudding user response：

The simplest I found:

The [:,None] adds a dimension to the array.

np.arange(start, end)[:,None]*np.ones(number)