You can use set_printoptions to set the precision of the output:
import numpy as np
x=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 manager
yourself. For example,
import numpy as np
import contextlib
@contextlib.contextmanager
def 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]
instead of
[ 0.078 0.48 0.413 0.83 0.776 0.102 0.513 0.462 0.335 0.712]