Remove Single Quotes From Numpy Array

Question

I have a Numpy Array with elements that are in single quotes and I want to convert the dtype to a float.

array = 
 [['20150101' '0.12']
 ['20150102' '0.42']
 ['20150103' '0.12']
 ['20150104' '0.46']
 ['20150105' '0.14']
 ['20150106' '0.1']
 ['20150107' '0.27']
 ['20150108' '0.03']
 ['20150109' '0.16']
 ['20150110' '0.29']
 ['20150111' '0.32']
 ['20150112' '0.16']]

I tried:

values = array.item().split(' ')
new_array = np.asarray(values, dtype='float')

but I get the ValueError: can only convert an array of size 1 to a Python scalar. I want the output to look like this: (No single quotes)

new_array = 
     [[20150101 0.12]
     [20150102 0.42]
     [20150103 0.12]
     [20150104 0.46]
     [20150105 0.14]
     [20150106 0.10]
     [20150107 0.27]
     [20150108 0.03]
     [20150109 0.16]
     [20150110 0.29]
     [20150111 0.32]
     [20150112 0.16]]

Is there a numpy function that can allow me to remove the single quotes?

The quotes signify that the array dtype is a string, eg 'U10'. — hpaulj
– hpaulj, Commented Feb 24, 2019 at 1:53
Your desired version has a mix of integers and floats. Is that intentional? What single string were you trying to split? Why? — hpaulj
– hpaulj, Commented Feb 24, 2019 at 2:02

hpaulj · Accepted Answer · 2019-02-24 03:53:26Z

3

What you show is a 2d array with a string dtype, which I can recreate with:

In [420]: arr = np.array([['20150101', '0.12'], 
     ...:  ['20150102', '0.42'], 
     ...:  ['20150103', '0.12'], 
     ...:  ['20150104', '0.46']])                                               
In [421]:                                                                       
In [421]: arr                                                                   
Out[421]: 
array([['20150101', '0.12'],     # the repr display
       ['20150102', '0.42'],
       ['20150103', '0.12'],
       ['20150104', '0.46']], dtype='<U8')
In [422]: print(arr)                        # the str display                                                
[['20150101' '0.12']
 ['20150102' '0.42']
 ['20150103' '0.12']
 ['20150104' '0.46']]

The quotes reflect the underlying nature of the array; they aren't just an incidental part of array.

Conversion to a float dtype array:

In [423]: arr.astype(float)                                                     
Out[423]: 
array([[2.0150101e+07, 1.2000000e-01],
       [2.0150102e+07, 4.2000000e-01],
       [2.0150103e+07, 1.2000000e-01],
       [2.0150104e+07, 4.6000000e-01]])

The scientific notation is required by the wide range of values. The first column by itself displays as:

In [424]: _[:,0]                                                                
Out[424]: array([20150101., 20150102., 20150103., 20150104.])

I can get a mix of integer and float with:

In [426]: arr1 = np.zeros((4,), dtype='i,f')                                    
In [427]: arr1                                                                  
Out[427]: 
array([(0, 0.), (0, 0.), (0, 0.), (0, 0.)],
      dtype=[('f0', '<i4'), ('f1', '<f4')])
In [428]: arr1['f0'] = arr[:,0]                                                 
In [429]: arr1['f1'] = arr[:,1]                                                 
In [430]: arr1                                                                  
Out[430]: 
array([(20150101, 0.12), (20150102, 0.42), (20150103, 0.12),
       (20150104, 0.46)], dtype=[('f0', '<i4'), ('f1', '<f4')])

This a 1d structured array. Notice the difference in notation, including the use of ().

edited Feb 24, 2019 at 3:53

answered Feb 24, 2019 at 3:47

hpaulj

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2 Comments

Eric Over a year ago

It looks like the asker might be looking for the datetime64[D] type for his first column

hpaulj Over a year ago

@Eric, yes the numbers do look date like. But the strings won't work as is. They'll need editing, e.g. '2015-01-01'.

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