Formatting floats in a numpy array

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.

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[ round(x,2) for x in [2.15295647e+01, 8.12531501e+00, 3.97113829e+00, 1.00777250e+01]]
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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']
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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])
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最佳答案

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.


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