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How to prevent NumPy array printing truncation?

Mary-Kate Olsen
Release: 2024-11-29 19:01:11
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How to prevent NumPy array printing truncation?

Overcoming Truncation in NumPy Array Printing

When printing NumPy arrays, it's common to encounter truncated representations, obscuring the full extent of the data. This truncation can be frustrating, particularly when dealing with large or complex arrays. Fortunately, NumPy provides a solution to this problem.

To print a full NumPy array, regardless of its size, utilize the numpy.set_printoptions function. This function allows you to adjust the printing settings, including the threshold for truncating arrays.

import sys
import numpy

# Set the printing threshold to infinity
numpy.set_printoptions(threshold=sys.maxsize)
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By setting the threshold to sys.maxsize, you effectively instruct NumPy to print the entire array without truncating it. This ensures that you can view the full data, avoiding any loss of information.

Suppose you have an array with 10,000 elements:

>> numpy.arange(10000)
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Truncated Output:

array([   0,    1,    2, ..., 9997, 9998, 9999])
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Full Output after setting the threshold:

array([ 0,  1,  2, ..., 9997, 9998, 9999])
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Similarly, for a multidimensional array, such as one reshaped to have 250 rows and 40 columns:

>> numpy.arange(10000).reshape(250, 40)
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Truncated Output:

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]])
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Full Output after setting the threshold:

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]])
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By adjusting the printing threshold, you can easily view the entire contents of your NumPy arrays, facilitating data exploration and analysis.

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