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

Each Answer to this Q is separated by one/two green lines.

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.

Here is a one-off way to do this, which is useful if you don’t want to change your default settings:

``````def fullprint(*args, **kwargs):
from pprint import pprint
import numpy
opt = numpy.get_printoptions()
numpy.set_printoptions(threshold=numpy.inf)
pprint(*args, **kwargs)
numpy.set_printoptions(**opt)
``````

This sounds like you’re using numpy.

If that’s the case, you can add:

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

That will disable the corner printing. For more information, see this NumPy Tutorial.

Using a context manager as Paul Price sugggested

``````import numpy as np

class fullprint:
'context manager for printing full numpy arrays'

def __init__(self, **kwargs):
kwargs.setdefault('threshold', np.inf)
self.opt = kwargs

def __enter__(self):
self._opt = np.get_printoptions()
np.set_printoptions(**self.opt)

def __exit__(self, type, value, traceback):
np.set_printoptions(**self._opt)

if __name__ == '__main__':
a = np.arange(1001)

with fullprint():
print(a)

print(a)

with fullprint(threshold=None, edgeitems=10):
print(a)
``````

`numpy.savetxt`

``````numpy.savetxt(sys.stdout, numpy.arange(10000))
``````

or if you need a string:

``````import StringIO
sio = StringIO.StringIO()
numpy.savetxt(sio, numpy.arange(10000))
s = sio.getvalue()
print s
``````

The default output format is:

``````0.000000000000000000e+00
1.000000000000000000e+00
2.000000000000000000e+00
3.000000000000000000e+00
...
``````

and it can be configured with further arguments.

Note in particular how this also not shows the square brackets, and allows for a lot of customization, as mentioned at: How to print a Numpy array without brackets?

Tested on Python 2.7.12, numpy 1.11.1.

This is a slight modification (removed the option to pass additional arguments to `set_printoptions)`of neoks answer.

It shows how you can use `contextlib.contextmanager` to easily create such a contextmanager with fewer lines of code:

``````import numpy as np
from contextlib import contextmanager

@contextmanager
def show_complete_array():
oldoptions = np.get_printoptions()
np.set_printoptions(threshold=np.inf)
try:
yield
finally:
np.set_printoptions(**oldoptions)
``````

In your code it can be used like this:

``````a = np.arange(1001)

print(a)      # shows the truncated array

with show_complete_array():
print(a)  # shows the complete array

print(a)      # shows the truncated array (again)
``````

A slight modification: (since you are going to print a huge list)

``````import numpy as np
np.set_printoptions(threshold=np.inf, linewidth=200)

x = np.arange(1000)
print(x)
``````

This will increase the number of characters per line (default linewidth of 75). Use any value you like for the linewidth which suits your coding environment. This will save you from having to go through huge number of output lines by adding more characters per line.

``````with np.printoptions(edgeitems=50):
print(x)
``````

Change 50 to how many lines you wanna see

Source: here

Complementary to this answer from the maximum number of columns (fixed with `numpy.set_printoptions(threshold=numpy.nan)`), there is also a limit of characters to be displayed. In some environments like when calling python from bash (rather than the interactive session), this can be fixed by setting the parameter `linewidth` as following.

``````import numpy as np
np.set_printoptions(linewidth=2000)    # default = 75
Mat = np.arange(20000,20150).reshape(2,75)    # 150 elements (75 columns)
print(Mat)
``````

In this case, your window should limit the number of characters to wrap the line.

For those out there using sublime text and wanting to see results within the output window, you should add the build option `"word_wrap": false` to the sublime-build file [source] .

``````np.set_printoptions(threshold=False)
``````

Since NumPy version 1.16, for more details see GitHub ticket 12251.

``````from sys import maxsize
from numpy import set_printoptions

set_printoptions(threshold=maxsize)
``````

Suppose you have a numpy array

`````` arr = numpy.arange(10000).reshape(250,40)
``````

If you want to print the full array in a one-off way (without toggling np.set_printoptions), but want something simpler (less code) than the context manager, just do

``````for row in arr:
print row
``````

If you’re using a jupyter notebook, I found this to be the simplest solution for one off cases. Basically convert the numpy array to a list and then to a string and then print. This has the benefit of keeping the comma separators in the array, whereas using `numpyp.printoptions(threshold=np.inf)` does not:

``````import numpy as np
print(str(np.arange(10000).reshape(250,40).tolist()))
``````

You won’t always want all items printed, especially for large arrays.

``````In [349]: ar
Out[349]: array([1, 1, 1, ..., 0, 0, 0])

In [350]: ar[:100]
Out[350]:
array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,
1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1])
``````

It works fine when sliced array < 1000 by default.

If you are using Jupyter, try the variable inspector extension. You can click each variable to see the entire array.

You can use the `array2string` function – docs.

``````a = numpy.arange(10000).reshape(250,40)
print(numpy.array2string(a, threshold=numpy.nan, max_line_width=numpy.nan))
# [Big output]
``````

If you have pandas available,

``````    numpy.arange(10000).reshape(250,40)
``````

avoids the side effect of requiring a reset of `numpy.set_printoptions(threshold=sys.maxsize)` and you don’t get the numpy.array and brackets. I find this convenient for dumping a wide array into a log file

If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners:
To disable this behaviour and force NumPy to print the entire array, you can change the printing options using `set_printoptions`.

``````>>> np.set_printoptions(threshold='nan')
``````

or

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

You can also refer to the numpy documentation numpy documentation for “or part” for more help.

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