NumPy
keyboard_arrow_down 319 guides
chevron_leftCookbooks
Accessing a value in a 2D arrayAccessing columns of a 2D arrayAccessing rows of a 2D arrayCalculating the determinant of a matrixChecking allowed values for a NumPy data typeChecking if a NumPy array is a view or copyChecking the version of NumPyChecking whether a NumPy array contains a given rowComputing Euclidean distance using NumpyConcatenating 1D arraysConverting array to lowercaseConverting type of NumPy array to stringCreating a copy of an arrayDifference between Python List and Numpy arrayDifference between the methods array_equal and array_equivDifference between the methods mod and fmodDifference between the methods power and float_powerFinding the closest value in an arrayFinding the Index of Largest Value in a Numpy ArrayFinding the Index of Smallest Value in a Numpy ArrayFinding the most frequent value in a NumPy arrayFlattening Numpy arraysGetting constant PiGetting elements from a two dimensional array using two dimensional array of indicesGetting indices of N maximum valuesGetting indices of N minimum valuesGetting the number of columns of a 2D arrayGetting the number of non-zero elements in a NumPy arrayGetting the number of rows of a 2D arrayInitializing an array of onesInitializing an array of zerosInitializing an identity matrixLimiting array values to a certain rangePerforming linear regressionPrinting full or truncated NumPy arrayPrinting large Numpy arrays without truncationRemoving rows containing NaN in a NumPy arrayReversing a NumPy arraySaving NumPy array to a fileShape of Numpy ArraysSorting value of one array according to anotherSuppressing scientific notation
check_circle
Mark as learned thumb_up
0
thumb_down
0
chat_bubble_outline
0
Comment auto_stories Bi-column layout
settings
Printing large NumPy arrays without truncation
schedule Aug 11, 2023
Last updated local_offer
Tags Python●NumPy
tocTable of Contents
expand_more Master the mathematics behind data science with 100+ top-tier guides
Start your free 7-days trial now!
Start your free 7-days trial now!
Suppose we have a large Numpy array like follows:
np.arange(5000)
array([ 0, 1, 2, ..., 4997, 4998, 4999])
As we can see, Numpy truncates large arrays when printing by default.
Using the set_printoptions method
In order to print the array in its entirety, we must configure Numpy like follows:
import sysnp.set_printoptions(threshold=sys.maxsize)
Now, when we print our large array again, we will see all its numbers:
np.arange(5000)
** You'll see all the numbers printed here **
Using the printoptions method
For those who are using NumPy 1.15 or above, you could also use the printoptions(~) method like follows:
with np.printoptions(threshold=np.inf): print(np.arange(5000))
** You'll see all the numbers printed here **
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
thumb_up
thumb_down
Comment
Citation
Ask a question or leave a feedback...
thumb_up
0
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!
