python convert list to numpy array while preserving the number formats -


my goal convert data numpy array while preserving number formats in original list, clear , proper.


for example, data in list format:

[[24.589888563639835, 13.899891781550952, 4478597, -1], [26.822224204095697, 14.670531752529088, 4644503, -1], [51.450405486761866, 54.770422572665254, 5570870, 0], [44.979065080591504, 54.998835550128852, 6500333, 0], [44.866399274880663, 55.757240813761534, 6513301, 0], [45.535380533604247, 57.790074517001365, 6593281, 0], [44.850372630818214, 54.720574554485822, 6605483, 0], [51.32738085400576, 55.118344981379266, 6641841, 0]]

when convert numpy array, 

data = np.asarray(data)

i mathematical notation e, how can conserve same format in output array?

[[  2.45898886e+01   1.38998918e+01   4.47859700e+06  -1.00000000e+00]  [  2.68222242e+01   1.46705318e+01   4.64450300e+06  -1.00000000e+00]  [  5.14504055e+01   5.47704226e+01   5.57087000e+06   0.00000000e+00]  [  4.49790651e+01   5.49988356e+01   6.50033300e+06   0.00000000e+00]  [  4.48663993e+01   5.57572408e+01   6.51330100e+06   0.00000000e+00]  [  4.55353805e+01   5.77900745e+01   6.59328100e+06   0.00000000e+00]  [  4.48503726e+01   5.47205746e+01   6.60548300e+06   0.00000000e+00]  [  5.13273809e+01   5.51183450e+01   6.64184100e+06   0.00000000e+00]]

update:

i did :

np.set_printoptions(precision=6,suppress=true)

but still different numbers when pass part of data variable , inside it, , see decimals have changed! why internally changing decimals, why can't hold them is?

simple array creation nested list:

in [133]: data = np.array(alist) in [136]: data.shape out[136]: (8, 4) in [137]: data.dtype out[137]: dtype('float64')

this 2d array, 8 'rows', 4 'columns'; elements stored float.

the list can loaded structured array, defined have mix of float , integer fields. note have convert 'rows' tuples load.

in [139]: dt = np.dtype('f,f,i,i') in [140]: dt out[140]: dtype([('f0', '<f4'), ('f1', '<f4'), ('f2', '<i4'), ('f3', '<i4')]) in [141]: data = np.array([tuple(row) row in alist], dtype=dt) in [142]: data.shape out[142]: (8,) in [143]: data out[143]:  array([( 24.58988762,  13.89989185, 4478597, -1),        ( 26.82222366,  14.67053223, 4644503, -1),        ( 51.45040512,  54.77042389, 5570870,  0),        ( 44.97906494,  54.99883652, 6500333,  0),        ( 44.86639786,  55.7572403 , 6513301,  0),        ( 45.53538132,  57.79007339, 6593281,  0),        ( 44.85037231,  54.72057343, 6605483,  0),        ( 51.32738113,  55.11834335, 6641841,  0)],        dtype=[('f0', '<f4'), ('f1', '<f4'), ('f2', '<i4'), ('f3', '<i4')])

you access fields name, not column number:

in [144]: data['f0'] out[144]:  array([ 24.58988762,  26.82222366,  51.45040512,  44.97906494,         44.86639786,  45.53538132,  44.85037231,  51.32738113], dtype=float32) in [145]: data['f3'] out[145]: array([-1, -1,  0,  0,  0,  0,  0,  0], dtype=int32)

compare values display of single columns 2d float array:

in [146]: dataf = np.array(alist) in [147]: dataf[:,0] out[147]:  array([ 24.58988856,  26.8222242 ,  51.45040549,  44.97906508,         44.86639927,  45.53538053,  44.85037263,  51.32738085]) in [148]: dataf[:,3] out[148]: array([-1., -1.,  0.,  0.,  0.,  0.,  0.,  0.])

the use of structured array makes more sense when there's mix of floats, int, strings or other dtypes.

but bit - wrong pure float version? why important retain integer identity of 2 columns?


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