# How to print the full NumPy array, without truncation?

StackOverflow

When I print a numpy array, I get a truncated representation, but I want the full array.

Is there any way to do this?

Examples:

``````>>> numpy.arange(10000)
array([   0,    1,    2, ..., 9997, 9998, 9999])

>>> numpy.arange(10000).reshape(250,40)
array([[   0,    1,    2, ...,   37,   38,   39],
[  40,   41,   42, ...,   77,   78,   79],
[  80,   81,   82, ...,  117,  118,  119],
...,
[9880, 9881, 9882, ..., 9917, 9918, 9919],
[9920, 9921, 9922, ..., 9957, 9958, 9959],
[9960, 9961, 9962, ..., 9997, 9998, 9999]])
``````

``````import sys
import numpy
numpy.set_printoptions(threshold=sys.maxsize)
``````

``````import numpy as np
np.set_printoptions(threshold=np.inf)
``````

I suggest using `np.inf` instead of `np.nan` which is suggested by others. They both work for your purpose, but by setting the threshold to "infinity" it is obvious to everybody reading your code what you mean. Having a threshold of "not a number" seems a little vague to me.

The previous answers are the correct ones, but as a weaker alternative you can transform into a list:

``````>>> numpy.arange(100).reshape(25,4).tolist()

[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19], [20, 21,
22, 23], [24, 25, 26, 27], [28, 29, 30, 31], [32, 33, 34, 35], [36, 37, 38, 39], [40, 41,
42, 43], [44, 45, 46, 47], [48, 49, 50, 51], [52, 53, 54, 55], [56, 57, 58, 59], [60, 61,
62, 63], [64, 65, 66, 67], [68, 69, 70, 71], [72, 73, 74, 75], [76, 77, 78, 79], [80, 81,
82, 83], [84, 85, 86, 87], [88, 89, 90, 91], [92, 93, 94, 95], [96, 97, 98, 99]]
``````

## Temporary setting

If you use NumPy 1.15 (released 2018-07-23) or newer, you can use the `printoptions` context manager:

``````with numpy.printoptions(threshold=numpy.inf):
print(arr)
``````

(of course, replace `numpy` by `np` if that"s how you imported `numpy`)

The use of a context manager (the `with`-block) ensures that after the context manager is finished, the print options will revert to whatever they were before the block started. It ensures the setting is temporary, and only applied to code within the block.

See `numpy.printoptions` documentation for details on the context manager and what other arguments it supports.