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I have a numpy array as following :

2016-07-02  10:55:01
2016-07-02  10:55:01
2016-07-02  10:55:01
2016-07-02  17:01:34
2016-07-02  17:01:34
2016-07-02  16:59:52
2016-07-02  17:01:34
2016-07-02  16:59:52
2016-07-02  16:59:52
2016-07-02  10:40:00
2016-07-02  12:01:14

this are two columns of array. date and time. but i want both into a single column concatenated by '\t'. both the values are in string format.

I did it by a loop as follows, but that is a bad idea and taking much time. : 

for D in Data:
    Data2 = np.append(Data2,np.array(D[0]+"\t"+D[1]))

Please suggest an efficient solution.

KrunalParmar
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4 Answers4

4

Neat, but not more efficient than simple loop (as Praveen pointed out in comment):

import numpy as np


np.apply_along_axis(lambda d: d[0] + '\t' + d[1], 1, arr)
featuredpeow
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    `np.apply_along_axis` _isn't_ a vectorized solution. See [this](http://stackoverflow.com/a/28904614/525169) answer... It just internally uses a for loop, so this doesn't really speed things up. – Praveen Aug 29 '16 at 07:09
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Insert the tabs \t into your array using numpy.insert and then do a numpy.reshape from n by 3 to n*3 by 1

meetaig
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1
import numpy as np
a=[[1],[2],[3]]
b=[[4],[5],[6]]

np.concatenate((a,b),axis=1)
ahad alam
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  1. Below method works for any two or more columns. It is very convenient if you want to concatenate multiple columns at a time, or even the whole row, because you don't have to explicitly write d[0] + '\t' + d[1] + ...

  2. On my computer it performs 50~60% faster than apply_along_axis() given above.

To concatenate the whole row delimited by '\t'

result = list(['\t'.join(row) for row in data])

Or if the actual row is larger and you only want to concatenate the first two columns:

result = list(['\t'.join(row[0:2]) for row in data])

Performance Comparison of both methods for 10,000 iterations with a very tiny data-set (< 100 rows) :

Method Time (ms)
Above method 350 ms
apply_along_axis() 870 ms
farqis
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