Concatenating two string columns of numpy array into single column in python

Question

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.

featuredpeow · Accepted Answer · 2016-08-31 22:02:25Z

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)

edited Aug 31, 2016 at 22:02

answered Aug 28, 2016 at 10:43

featuredpeow

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1 Comment

Praveen Over a year ago

np.apply_along_axis isn't a vectorized solution. See this answer... It just internally uses a for loop, so this doesn't really speed things up.

meetaig · Accepted Answer · 2016-08-28 10:33:02Z

3

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

answered Aug 28, 2016 at 10:33

meetaig

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Comments

ahad alam · Accepted Answer · 2016-08-28 12:08:38Z

1

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

np.concatenate((a,b),axis=1)

answered Aug 28, 2016 at 12:08

ahad alam

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Comments

farqis · Accepted Answer · 2022-12-18 03:12:58Z

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] + ...
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

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