numpy number array to strings with trailing zeros removed

Question

question: is my method of converting a numpy array of numbers to a numpy array of strings with specific number of decimal places AND trailing zeros removed the 'best' way?

import numpy as np
x = np.array([1.12345, 1.2, 0.1, 0, 1.230000])
print np.core.defchararray.rstrip(np.char.mod('%.4f', x), '0')

outputs:

['1.1235' '1.2' '0.1' '0.' '1.23']

which is the desired result. (I am OK with the rounding issue)

Both of the functions 'rstrip' and 'mod' are numpy functions which means this is fast but is there a way to accomplish this with ONE built in numpy function? (ie. does 'mod' have an option that I couldn't find?) It would save the overhead of returning copies twice which for very large arrays is slow-ish.

thanks!

@Dalek because that would not remove trailing zeros. The reason I want to remove the zeros is it will make my files smaller. I am manually creating some ascii GIS rasters and would prefer to keep the large files as small as possible. Speed-wise, the additional operation to remove trailing zeros is not a bid deal so I consider it worth it for the gain of having smaller files. It would be fine for a few files to be larger than needed but I'm planning to do some quite large scale stuff...it'll add up. So, I am OK with the speed of what I use, but I am curious if anyone has a slicker way. — user1269942
– user1269942, Commented Aug 14, 2014 at 20:14
If you are OK with 5 "signifcant digits" instead of 4 decimal places, you could use np.char.mod("%.5g", x). — Warren Weckesser
– Warren Weckesser, Commented Aug 14, 2014 at 20:28
What version of numpy are you using? In the latest version of numpy, savetxt accepts a file handle: docs.scipy.org/doc/numpy/reference/generated/numpy.savetxt.html — Warren Weckesser
– Warren Weckesser, Commented Aug 14, 2014 at 20:45

user1269942 · Accepted Answer · 2014-08-19 16:46:57Z

2

Thanks to Warren Weckesser for providing valuable comments. Credit to him.

I converted my code to use:

formatter = '%d'
if num_type == 'float':
  formatter = '%%.%df' % decimals
np.savetxt(out, arr, fmt=formatter)

where out is a file handle to which I had already written my headers. Alternatively, I could also use the headers= argument in np.savetxt. I have no clue how I didn't see those options in the documentation.

For a numpy array 1300 by 1300, creating the line by line output as I did before (using np.core.defchararray.rstrip(np.char.mod('%.4f', x), '0')) took ~1.7 seconds and using np.savetxt takes 0.48 seconds.

So np.savetxt is a cleaner, more readable, and faster solution.

Note: I did try:

np.savetxt(out, arr, fmt='%.4g')

in an effort to not have a switch based on number type but it did not work as I had hoped.

edited Aug 19, 2014 at 16:46

answered Aug 14, 2014 at 22:29

user1269942

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