NumPy Input and Output: set_printoptions() function

numpy.set_printoptions() function

The set_printoptions() function is used to set printing options.

These options determine the way floating point numbers, arrays and other NumPy objects are displayed.


numpy.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None,
suppress=None, nanstr=None, infstr=None, formatter=None, sign=None, floatmode=None, **kwarg)

Version: 1.15.0


Name Description Required /
precision Number of digits of precision for floating point output (default 8).
May be None if floatmode is not fixed, to print as many digits as necessary to uniquely specify the value.
int or None
threshold Total number of array elements which trigger summarization rather than full repr (default 1000).
edgeitems Number of array items in summary at beginning and end of each dimension (default 3).
linewidth The number of characters per line for the purpose of inserting line breaks (default 75).
suppress If True, always print floating point numbers using fixed point notation, in which case numbers equal to zero in the current precision will print as zero.
If False, then scientific notation is used when absolute value of the smallest number is < 1e-4 or the ratio of the maximum absolute value to the minimum is > 1e3.
The default is False.
nanstr String representation of floating point not-a-number (default nan).
infstr String representation of floating point infinity (default inf).
sign Controls printing of the sign of floating-point types. If '+', always print the sign of positive values.
If ' ', always prints a space (whitespace character) in the sign position of positive values.
If '-', omit the sign character of positive values. (default '-')
string, either '-', '+', or ' '
formatter If not None, the keys should indicate the type(s) that the respective formatting function applies to.
Callables should return a string. Types that are not specified (by their corresponding keys) are handled by the default formatters.
Individual types for which a formatter can be set are:
  • 'bool'
  • 'int'
  • 'timedelta' : a `numpy.timedelta64`
  • 'datetime' : a `numpy.datetime64`
  • 'float'
  • 'longfloat' : 128-bit floats
  • 'complexfloat'
  • 'longcomplexfloat' : composed of two 128-bit floats
  • 'numpystr' : types `numpy.string_` and `numpy.unicode_`
  • 'object' : `np.object_` arrays
  • 'str' : all other strings

Other keys that can be used to set a group of types at once are:
  • 'all' : sets all types
  • 'int_kind' : sets 'int'
  • 'float_kind' : sets 'float' and 'longfloat'
  • ‘complex_kind’ : sets ‘complexfloat’ and ‘longcomplexfloat'
  • ‘str_kind’ : sets ‘str’ and ‘numpystr’

dict of callables
floatmode Controls the interpretation of the precision option for floating-point types.
Can take the following values:
  • 'fixed': Always print exactly precision fractional digits,
    even if this would print more or fewer digits than necessary to specify the value uniquely.
  • 'unique': Print the minimum number of fractional digits necessary
    to represent each value uniquely. Different elements may have a different number of digits.
    The value of the precision option is ignored.
  • 'maxprec': Print at most precision fractional digits, but if
    an element can be uniquely represented with fewer digits only print it with that many.
  • 'maxprec_equal': Print at most precision fractional digits,
    but if every element in the array can be uniquely represented with an equal number of fewer digits,
    use that many digits for all elements.
legacy If set to the string '1.13' enables 1.13 legacy printing mode.
This approximates numpy 1.13 print output by including a space in the sign position of floats and different behavior for 0d arrays.
If set to False, disables legacy mode. Unrecognized strings will be ignored with a warning for forward compatibility.
string or False


formatter is always reset with a call to set_printoptions.

NumPy.set_printoptions() method Example-1:

Floating point precision can be set:

>>> import numpy as np
>>> np.set_printoptions(precision=5)
>>> print(np.array([1.0123456789]))



NumPy.set_printoptions() method Example-2:

Long arrays can be summarised:

>>> import numpy as np
>>> np.set_printoptions(threshold=5)
>>> print(np.arange(12))


[ 0  1  2 ...  9 10 11]

NumPy.set_printoptions() method Example-3:

Small results can be suppressed:

>>> import numpy as np
>>> eps = np.finfo(float).eps
>>> x = np.arange(5.)
>>> x**3 - (x + eps)**3


array([-1.0948e-47, -6.6613e-16,  0.0000e+00,  0.0000e+00,  0.0000e+00])

NumPy.set_printoptions() method Example-4:

Small results can be suppressed:

>>> import numpy as np
>>> np.set_printoptions(suppress=True)
>>> x**3 - (x + eps)**3


array([-0., -0.,  0.,  0.,  0.])

NumPy.set_printoptions() method Example-5:

A custom formatter can be used to display array elements as desired:

>>> import numpy as np
>>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
>>> x = np.arange(4)
>>> x


array([int: 0, int: -1, int: -2, int: -3])

NumPy.set_printoptions() method Example-6:

>>> import numpy as np
>>> np.set_printoptions()  # formatter gets reset
>>> x


array([0, 1, 2, 3])

NumPy.set_printoptions() method Example:

To put back the default options, you can use:

>>> import numpy as np
>>> np.set_printoptions(edgeitems=3,infstr='inf',
... linewidth=75, nanstr='nan', precision=8,
... suppress=False, threshold=1000, formatter=None)

Python - NumPy Code Editor:

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