python将列表转换为numpy数组，同时保留数字格式 [英] python convert list to numpy array while preserving the number formats
问题描述
我的目标是将我的数据转换为numpy数组，同时将数字格式保留在原始列表中，清晰明了.
My goal is to convert my data into numpy array while preserving the number formats in the original list, clear and proper.
例如，
这是我的列表格式数据:
for example,
this is my 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]]
当我将其转换为numpy数组时，
when i do convert it to numpy array,
data = np.asarray(data)
我得到数学符号e，如何在输出数组中保留相同的格式?
i get mathematical notation e, how can I conserve the same format in my 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]]
更新:
我做到了:
update:
I did :
np.set_printoptions(precision=6,suppress=True)
，但是当我将部分数据传递给另一个变量然后在其中查找时，我仍然得到不同的数字，我发现小数点已经改变了！为什么在内部更改小数，为什么不能仅保留小数呢?
推荐答案
从嵌套列表创建简单数组:
Simple array creation from the nested list:
In [133]: data = np.array(alist)
In [136]: data.shape
Out[136]: (8, 4)
In [137]: data.dtype
Out[137]: dtype('float64')
这是一个二维数组，8个行"，4个列"；所有元素都存储为float.
This is a 2d array, 8 'rows', 4 'columns'; all elements are stored as float.
可以将列表加载到结构化数组中，该数组定义为混合使用float和integer字段.请注意，为此负载，我必须将行"转换为元组.
The list can be loaded into a structured array, that is defined to have a mix of float and integer fields. Note that I have to convert the 'rows' to tuples for this 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) for 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')])
您按名称而不是列号访问字段:
You access fields by 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)
将这些值与2d浮点数组中的单列显示进行比较:
Compare those values with the display of single columns from the 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.])
当混合使用浮点数，整数，字符串或其他dtypes时，使用结构化数组更有意义.
The use of a structured array makes more sense when there's a mix of floats, int, strings or other dtypes.
但是要备份一下-纯浮动版本有什么问题?为什么保留2列的整数身份很重要?
But to back up a bit - what is wrong with the pure float version? Why is important to retain the integer identity of 2 columns?
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