Printing NumPy Arrays#

This page explains how to control the formatting of printed NumPy arrays. Note that these printing options apply only to arrays, not to scalars.

Defining printing options#

Applying settings globally#

Use numpy.set_printoptions to change printing options for the entire runtime session. To inspect current print settings, use numpy.get_printoptions:

>>> np.set_printoptions(precision=2)
>>> np.get_printoptions()
{'edgeitems': 3, 'threshold': 1000, 'floatmode': 'maxprec', 'precision': 2, 'suppress': False, 'linewidth': 75, 'nanstr': 'nan', 'infstr': 'inf', 'sign': '-', 'formatter': None, 'legacy': False, 'override_repr': None}

To restore the default settings, use:

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

Applying settings temporarily#

Use numpy.printoptions as a context manager to temporarily override print settings within a specific scope:

>>> arr = np.array([0.155, 0.184, 0.173])
>>> with np.printoptions(precision=2):
...     print(arr)
[0.15 0.18 0.17]

All keywords that apply to numpy.set_printoptions also apply to numpy.printoptions.

Changing the number of digits of precision#

The default number of fractional digits displayed is 8. You can change this number using precision keyword.

>>> arr = np.array([0.1, 0.184, 0.17322])
>>> with np.printoptions(precision=2):
...     print(arr)
[0.1 0.18 0.17]

The floatmode option determines how the precision setting is interpreted. By default, floatmode=maxprec_equal displays values with the minimal number of digits needed to uniquely represent them, using the same number of digits across all elements. If you want to show exactly the same number of digits specified by precision, use floatmode=fixed:

>>> arr = np.array([0.1, 0.184, 0.173], dtype=np.float32)
>>> with np.printoptions(precision=2, floatmode="fixed"):
...     print(arr)
[0.10 0.18 0.17]

Changing how nan and inf are displayed#

By default, numpy.nan is displayed as nan and numpy.inf is displayed as inf. You can override these representations using the nanstr and infstr options:

>>> arr = np.array([np.inf, np.nan, 0])
>>> with np.printoptions(nanstr="NAN", infstr="INF"):
...     print(arr)
[INF NAN  0.]

Controlling scientific notations#

By default, NumPy uses scientific notation when:

  • The absolute value of the smallest number is less than 1e-4, or

  • The ratio of the largest to the smallest absolute value is greater than 1e3

    >>> arr = np.array([0.00002, 210000.0, 3.14])
>>> print(arr)
[2.00e-05 2.10e+05 3.14e+00]

To suppress scientific notation and always use fixed-point notation, set suppress=True:

>>> arr = np.array([0.00002, 210000.0, 3.14])
>>> with np.printoptions(suppress=True):
...     print(arr)
[     0.00002 210000.           3.14   ]

Applying custom formatting functions#

You can apply custom formatting functions to specific or all data types using formatter keyword. See numpy.set_printoptions for more details on supported format keys.

For example, to format datetime64 values with a custom function:

>>> arr = np.array([np.datetime64("2025-01-01"), np.datetime64("2024-01-01")])
>>> with np.printoptions(formatter={"datetime":lambda x: f"(Year: {x.item().year}, Month: {x.item().month})"}):
...     print(arr)
[(Year: 2025, Month: 1) (Year: 2024, Month: 1)]