Set printing options.
These options determine the way floating point numbers, arrays and other NumPy objects are displayed.
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.
Total number of array elements which trigger summarization rather than full repr (default 1000). To always use the full repr without summarization, pass sys.maxsize.
Number of array items in summary at beginning and end of each dimension (default 3).
The number of characters per line for the purpose of inserting line breaks (default 75).
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.
String representation of floating point not-a-number (default nan).
String representation of floating point infinity (default inf).
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 ‘-‘)
Changed in version 2.0: The sign parameter can now be an integer type, previously types were floating-point types.
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.bytes_ and numpy.str_
‘object’ : np.object_ arrays
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 ‘numpystr’
Controls the interpretation of the precision option for floating-point types. Can take the following values (default maxprec_equal):
even if this would print more or fewer digits than necessary to specify the value uniquely.
to represent each value uniquely. Different elements may have a different number of digits. The value of the precision option is ignored.
an element can be uniquely represented with fewer digits only print it with that many.
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.
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. This also enables 1.21 legacy printing mode (described below).
If set to the string '1.21' enables 1.21 legacy printing mode. This approximates numpy 1.21 print output of complex structured dtypes by not inserting spaces after commas that separate fields and after colons.
If set to '1.25' approximates printing of 1.25 which mainly means that numeric scalars are printed without their type information, e.g. as 3.0 rather than np.float64(3.0).
If set to '2.1', shape information is not given when arrays are summarized (i.e., multiple elements replaced with ...).
If set to False, disables legacy mode.
Unrecognized strings will be ignored with a warning for forward compatibility.
Changed in version 1.22.0.
Changed in version 2.2.
If set a passed function will be used for generating arrays’ repr. Other options will be ignored.
Notes
formatter is always reset with a call to set_printoptions.
Use printoptions as a context manager to set the values temporarily.
Examples
Floating point precision can be set:
>>> import numpy as np >>> np.set_printoptions(precision=4) >>> np.array([1.123456789]) [1.1235]
Long arrays can be summarised:
>>> np.set_printoptions(threshold=5) >>> np.arange(10) array([0, 1, 2, ..., 7, 8, 9], shape=(10,))
Small results can be suppressed:
>>> eps = np.finfo(float).eps >>> x = np.arange(4.) >>> x**2 - (x + eps)**2 array([-4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00]) >>> np.set_printoptions(suppress=True) >>> x**2 - (x + eps)**2 array([-0., -0., 0., 0.])
A custom formatter can be used to display array elements as desired:
>>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
>>> x = np.arange(3)
>>> x
array([int: 0, int: -1, int: -2])
>>> np.set_printoptions() # formatter gets reset
>>> x
array([0, 1, 2])
To put back the default options, you can use:
>>> np.set_printoptions(edgeitems=3, infstr='inf', ... linewidth=75, nanstr='nan', precision=8, ... suppress=False, threshold=1000, formatter=None)
Also to temporarily override options, use printoptions as a context manager:
>>> with np.printoptions(precision=2, suppress=True, threshold=5): ... np.linspace(0, 10, 10) array([ 0. , 1.11, 2.22, ..., 7.78, 8.89, 10. ], shape=(10,))
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