Reputation: 47124
What's the right way to round each item in the par1 and par2 columns of this array to 6 decimal places. Below is what I tried so far but I'm getting a strange error.
(I guess it also wouldn't work because it would round the first column?)
a = numpy.array([('54641', 5.2283950300822005e-19, 0.99986935998398196),
('19463068', 1.9641846381816301e-11, 3.9584362981756201e-24),
('19500889', 3.0296847410896202e-11, 1.05569703377661e-11),
('19528632', 3.5188395912917703e-11, 1.4213535554705201e-09)],
dtype=[('pos', 'S100'), ('par1', '<f8'), ('par2', '<f8')])
a = numpy.around(a, decimals=6)
Strange Error (any idea why it's saying this?)
Traceback (most recent call last):
File "msg/combine.py", line 244, in <module>
a = numpy.around(a, decimals=6)
File "/usr/local/msg/lib/python2.6/site-packages/numpy/core/fromnumeric.py", line 2611, in around
return round(decimals, out)
TypeError: return arrays must be of ArrayType
Upvotes: 3
Views: 3100
Reputation: 19547
Not sure if you can do this without loops:
>>> for col in ['par1','par2']:
... a[col] = numpy.around(a[col],2)
...
>>> a
array([('54641', 0.0, 1.0), ('19463068', 0.0, 0.0), ('19500889', 0.0, 0.0),
('19528632', 0.0, 0.0)],
dtype=[('pos', 'S100'), ('par1', '<f8'), ('par2', '<f8')])
Of course you can use pandas for structured arrays:
>>> import pandas as pd
>>> data = pd.DataFrame(a)
>>> data[['par1','par2']] = numpy.around(data[['par1','par2']], 2)
>>> data
pos par1 par2
0 54641 0 1
1 19463068 0 0
2 19500889 0 0
Upvotes: 3