Formatting floats in a numpy array [duplicate]

Question

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.

Answer 1

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.