How to pretty-print a numpy.array without scientific notation and with given precision? How to pretty-print a numpy.array without scientific notation and with given precision? python python

# How to pretty-print a numpy.array without scientific notation and with given precision?

You can use `set_printoptions` to set the precision of the output:

``import numpy as npx=np.random.random(10)print(x)# [ 0.07837821  0.48002108  0.41274116  0.82993414  0.77610352  0.1023732#   0.51303098  0.4617183   0.33487207  0.71162095]np.set_printoptions(precision=3)print(x)# [ 0.078  0.48   0.413  0.83   0.776  0.102  0.513  0.462  0.335  0.712]``

And `suppress` suppresses the use of scientific notation for small numbers:

``y=np.array([1.5e-10,1.5,1500])print(y)# [  1.500e-10   1.500e+00   1.500e+03]np.set_printoptions(suppress=True)print(y)# [    0.      1.5  1500. ]``

See the docs for set_printoptions for other options.

To apply print options locally, using NumPy 1.15.0 or later, you could use the numpy.printoptions context manager. For example, inside the `with-suite` `precision=3` and `suppress=True` are set:

``x = np.random.random(10)with np.printoptions(precision=3, suppress=True):    print(x)    # [ 0.073  0.461  0.689  0.754  0.624  0.901  0.049  0.582  0.557  0.348]``

But outside the `with-suite` the print options are back to default settings:

``print(x)    # [ 0.07334334  0.46132615  0.68935231  0.75379645  0.62424021  0.90115836#   0.04879837  0.58207504  0.55694118  0.34768638]``

If you are using an earlier version of NumPy, you can create the context manageryourself. For example,

``import numpy as npimport contextlib@contextlib.contextmanagerdef printoptions(*args, **kwargs):    original = np.get_printoptions()    np.set_printoptions(*args, **kwargs)    try:        yield    finally:         np.set_printoptions(**original)x = np.random.random(10)with printoptions(precision=3, suppress=True):    print(x)    # [ 0.073  0.461  0.689  0.754  0.624  0.901  0.049  0.582  0.557  0.348]``

To prevent zeros from being stripped from the end of floats:

`np.set_printoptions` now has a `formatter` parameter which allows you to specify a format function for each type.

``np.set_printoptions(formatter={'float': '{: 0.3f}'.format})print(x)``

which prints

``[ 0.078  0.480  0.413  0.830  0.776  0.102  0.513  0.462  0.335  0.712]``

``[ 0.078  0.48   0.413  0.83   0.776  0.102  0.513  0.462  0.335  0.712]``

You can get a subset of the `np.set_printoptions` functionality from the `np.array_str` command, which applies only to a single print statement.

http://docs.scipy.org/doc/numpy/reference/generated/numpy.array_str.html

For example:

``In [27]: x = np.array([[1.1, 0.9, 1e-6]]*3)In [28]: print x[[  1.10000000e+00   9.00000000e-01   1.00000000e-06] [  1.10000000e+00   9.00000000e-01   1.00000000e-06] [  1.10000000e+00   9.00000000e-01   1.00000000e-06]]In [29]: print np.array_str(x, precision=2)[[  1.10e+00   9.00e-01   1.00e-06] [  1.10e+00   9.00e-01   1.00e-06] [  1.10e+00   9.00e-01   1.00e-06]]In [30]: print np.array_str(x, precision=2, suppress_small=True)[[ 1.1  0.9  0. ] [ 1.1  0.9  0. ] [ 1.1  0.9  0. ]]``

Unutbu gave a really complete answer (they got a +1 from me too), but here is a lo-tech alternative:

``>>> x=np.random.randn(5)>>> xarray([ 0.25276524,  2.28334499, -1.88221637,  0.69949927,  1.0285625 ])>>> ['{:.2f}'.format(i) for i in x]['0.25', '2.28', '-1.88', '0.70', '1.03']``

As a function (using the `format()` syntax for formatting):

``def ndprint(a, format_string ='{0:.2f}'):    print [format_string.format(v,i) for i,v in enumerate(a)]``

Usage:

``>>> ndprint(x)['0.25', '2.28', '-1.88', '0.70', '1.03']>>> ndprint(x, '{:10.4e}')['2.5277e-01', '2.2833e+00', '-1.8822e+00', '6.9950e-01', '1.0286e+00']>>> ndprint(x, '{:.8g}')['0.25276524', '2.283345', '-1.8822164', '0.69949927', '1.0285625']``

The index of the array is accessible in the format string:

``>>> ndprint(x, 'Element[{1:d}]={0:.2f}')['Element[0]=0.25', 'Element[1]=2.28', 'Element[2]=-1.88', 'Element[3]=0.70', 'Element[4]=1.03']``