Python numpy array assign values to each column according to row

Question

I want to assign row-specific values to each row in a 2D numpy array. In particular, each row should contain the value of the reciprocal of its row number. In other words, all columns in row 1 should have values 1, all columns in row 2 should be 1/2, all in row 3 should be 1/3, and so on. I tried this:

m = 3
n = 10

train = np.empty([n,m], float)

for curr_n in range(n):
    train[curr_n,:] = 1/(curr_n+1)

print train

But the output is:

[[ 1.  1.  1.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]]

What am I doing wrong? I used "float" at the beginning, but I'm still getting solid 0 integers for all but the first row.

1/(curr_n+1) always return 1 or 0 because curr_n is a type of integer. You might want to do like 1.0/(curr_n+1) or 1/float(curr_n+1). — Kei Minagawa
– Kei Minagawa, Commented Apr 21, 2014 at 13:51

John Powell · Accepted Answer · 2014-04-21 12:42:42Z

5

Implicit type conversion has been added in Python 3, so your original code will then work, as is:

from __future__ import division

m = 3
n = 10

train = np.empty([n,m], float)

for curr_n in range(n):
   train[curr_n,:] = 1/(curr_n+1)

print train

Some information on the __future__ module can be seen in the official docs future module

Or, as sshashank124 put in his answer and I put in the comment, you can use 1. or 1.0 to force float behavior.

edited Apr 21, 2014 at 12:42

answered Apr 21, 2014 at 12:22

John Powell

12.6k6 gold badges64 silver badges69 bronze badges

Sign up to request clarification or add additional context in comments.

Comments

sshashank124 · Accepted Answer · 2014-04-21 12:27:30Z

You have a simple type problem. Use 1.0 instead of 1 in your reciprocation. The following works:

m = 3
n = 10

train = np.empty([n,m])

for curr_n in range(n):
    train[curr_n,:] = 1.0/(curr_n+1)   #converted 1 to 1.0

print train

Explanation

Although you might think that numpy deals with floats by default, in this case, the numpy array is getting assigned the value after python has had the time to calculate the inverses. It is then that python truncates your floats to an int and numpy innocently converts that sneaky int to a float just as it is supposed to and ends up taking all the blame.

Collectives™ on Stack Overflow

Python numpy array assign values to each column according to row

2 Answers 2

Comments

Explanation

Comments

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

Comments

Explanation

Comments

Related