import numpy as np
row_a = ['0.01722497', '', '0.09496404', '0.03654174', '0.03624997', '0.01583785', '0.02002064', '0.13934049', '0.0405615', '0.05686177', '', '0.08495372', '0.00619173', '0.00515492', '0.01053369', '0.06576333']
row_b = [0.04871661, 0.1122536, 0.20836956, 0.05473605, 0.02344445, 0.01739371, 0.00524003, 0.0640286, 0.02766152, 0.02442267, 0.04183814, 0.04853815, 0.01682549, 0.00263045, 0.00819199, 0.1631007]
dt = np.dtype([('col_1', 'U32'), ('col_2', float)])
arr = np.empty((2, len(row_a)), dtype=dt)
arr['col_1'] = row_a
arr['col_2'] = row_b
np.savetxt('table.csv', arr, delimiter=',', header='col_1,col_2', fmt='%s %f')
Code above (which is supposed to create a structured array out of an str and int array and output it to a csv) gives me the following error, even though I have 2 arrays of same length, 2 columns and 2 formats:
ValueError: fmt has wrong number of % formats: %s %f
Making a 1d structured array (as per my comment):
In [423]: row_a = ['0.01722497', '', '0.09496404', '0.03654174', '0.03624997', '0.01583785', '0
...: .02002064', '0.13934049', '0.0405615', '0.05686177', '', '0.08495372', '0.00619173',
...: '0.00515492', '0.01053369', '0.06576333']
...: row_b = [0.04871661, 0.1122536, 0.20836956, 0.05473605, 0.02344445, 0.01739371, 0.005
...: 24003, 0.0640286, 0.02766152, 0.02442267, 0.04183814, 0.04853815, 0.01682549, 0.00263
...: 045, 0.00819199, 0.1631007]
...: dt = np.dtype([('col_1', 'U32'), ('col_2', float)])
...: arr = np.empty(len(row_a), dtype=dt)
...: arr['col_1'] = row_a
...: arr['col_2'] = row_b
In [424]:
In [424]: arr
Out[424]:
array([('0.01722497', 0.04871661), ('', 0.1122536 ),
('0.09496404', 0.20836956), ('0.03654174', 0.05473605),
('0.03624997', 0.02344445), ('0.01583785', 0.01739371),
('0.02002064', 0.00524003), ('0.13934049', 0.0640286 ),
('0.0405615', 0.02766152), ('0.05686177', 0.02442267),
('', 0.04183814), ('0.08495372', 0.04853815),
('0.00619173', 0.01682549), ('0.00515492', 0.00263045),
('0.01053369', 0.00819199), ('0.06576333', 0.1631007 )],
dtype=[('col_1', '<U32'), ('col_2', '<f8')])
In [425]: arr.shape
Out[425]: (16,)
And the save:
In [426]: np.savetxt('table.csv', arr, delimiter=',', header='col_1,col_2', fmt='%s %f')
In [427]: cat table.csv
# col_1,col_2
0.01722497 0.048717
0.112254
0.09496404 0.208370
0.03654174 0.054736
...
The linked SO that I answered before had a more complex dtype. This is a simple 2 field case, so doesn't need special handling.
The """ values might give problems when file loading. I'd suggest at least using delimiter like ,, so the loader can treat it as missing value.
In [428]: np.savetxt('table.csv', arr, delimiter=',', header='col_1,col_2', fmt='%s, %f')
In [429]: cat table.csv
# col_1,col_2
0.01722497, 0.048717
, 0.112254
0.09496404, 0.208370
0.03654174, 0.054736
...
In [430]: np.genfromtxt('table.csv', dtype=None, names=True, delimiter=',')
Out[430]:
array([(0.01722497, 0.048717), ( nan, 0.112254),
(0.09496404, 0.20837 ), (0.03654174, 0.054736),
(0.03624997, 0.023444), (0.01583785, 0.017394),
In [431]: np.genfromtxt('table.csv', dtype=arr.dtype, names=True, delimiter=',')
Out[431]:
array([('0.01722497', 0.048717), ('', 0.112254), ('0.09496404', 0.20837 ),
('0.03654174', 0.054736), ('0.03624997', 0.023444),
('0.01583785', 0.017394), ('0.02002064', 0.00524 ),