If I have a numpy array like this:
[2.15295647e+01, 8.12531501e+00, 3.97113829e+00, 1.00777250e+01]
how can I move the decimal point and format the numbers so I end up with a numpy array like this:
[21.53, 8.13, 3.97, 10.08]
np.around(a, decimals=2) only gives me [2.15300000e+01, 8.13000000e+00, 3.97000000e+00, 1.00800000e+01] Which I don't want and I haven't found another way to do it.
In order to make numpy display float arrays in an arbitrary format, you can define a custom function that takes a float value as its input and returns a formatted string:
In [1]: float_formatter = "{:.2f}".format
The f here means fixed-point format (not 'scientific'), and the .2 means two decimal places (you can read more about string formatting here).
Let's test it out with a float value:
In [2]: float_formatter(1.234567E3)
Out[2]: '1234.57'
To make numpy print all float arrays this way, you can pass the formatter= argument to np.set_printoptions:
In [3]: np.set_printoptions(formatter={'float_kind':float_formatter})
Now numpy will print all float arrays this way:
In [4]: np.random.randn(5) * 10
Out[4]: array([5.25, 3.91, 0.04, -1.53, 6.68]
Note that this only affects numpy arrays, not scalars:
In [5]: np.pi
Out[5]: 3.141592653589793
It also won't affect non-floats, complex floats etc - you will need to define separate formatters for other scalar types.
You should also be aware that this only affects how numpy displays float values - the actual values that will be used in computations will retain their original precision.
For example:
In [6]: a = np.array([1E-9])
In [7]: a
Out[7]: array([0.00])
In [8]: a == 0
Out[8]: array([False], dtype=bool)
numpy prints a as if it were equal to 0, but it is not - it still equals 1E-9.
If you actually want to round the values in your array in a way that affects how they will be used in calculations, you should use np.round, as others have already pointed out.
You're confusing actual precision and display precision. Decimal rounding cannot be represented exactly in binary. You should try:
> np.set_printoptions(precision=2)
> np.array([5.333333])
array([ 5.33])
[ round(x,2) for x in [2.15295647e+01, 8.12531501e+00, 3.97113829e+00, 1.00777250e+01]]
You can use round function. Here some example
numpy.round([2.15295647e+01, 8.12531501e+00, 3.97113829e+00, 1.00777250e+01],2)
array([ 21.53, 8.13, 3.97, 10.08])
IF you want change just display representation, I would not recommended to alter printing format globally, as it suggested above. I would format my output in place.
>>a=np.array([2.15295647e+01, 8.12531501e+00, 3.97113829e+00, 1.00777250e+01])
>>> print([ "{:0.2f}".format(x) for x in a ])
['21.53', '8.13', '3.97', '10.08']
Source: Stackoverflow.com