Sometimes we have elements that are in scientific notations, and we have to suppress the scientific notation for simplicity. For this purpose, we call a function named as numpy.set_printoptions(). This function will help to suppress the scientific notation and display number to a certain precision. In this article we will see How to suppress the use of scientific notations for small numbers using NumPy in Python.
Syntax: numpy.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, formatter=None, sign=None, floatmode=None, *, legacy=None)
Parameters:
- precision: Number of digits of precision for floating point output (default 8)
- suppress: If True, always print floating point numbers using fixed point notation, if False, then scientific notation is used when absolute value of the smallest number is < 1e-4
Rest of the parameters are optional.
Suppress the use of scientific notations using Numpy in Python
Example 1:
Here we suppress the scientific notations for the elements of 1-D NumPy array with precision 2.
# Importing Numpy library import numpy as np
# Creating a 1-D Numpy array num = np.array([ 1.8e - 10 , 1.586 , 150.45 , 0.2855 ])
# Suppressing 1-D numpy array with precision 2 # using numpy.set_printoptions() print ( "Numpy array values with precision 2:\n" )
np.set_printoptions(precision = 2 , suppress = True )
print (num)
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Output:
Numpy array values with precision 2: [ 0. 1.59 150.45 0.29]
Example 2:
Here we suppress the scientific notations for the elements of 2-D NumPy array with precision 3.
# Importing Numpy library import numpy as np
# Creating a 2-D Numpy array num = np.array([[ 3.1415 , 2.7182 ],
[ 6.6260e - 34 , 6.6743e - 11 ]])
# Suppressing 2-D numpy array with precision 3 # using numpy.set_printoptions() print ( "Numpy array values with precision 3:\n" )
np.set_printoptions(precision = 3 , suppress = True )
print (num)
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Output:
Numpy array values with precision 3: [[3.142 2.718] [0. 0. ]]
Example 3:
Here we suppress the scientific notations for the elements of 3-D NumPy array with precision 4.
# Importing Numpy library import numpy as np
# Creating a 3-D Numpy array num = np.array([[[ 3.141527 , 2.718283 ],
[ 6.6268574 , 6.6743e - 11 ]],
[[ 34.8454 , 8.6260e - 34 ],
[ 7 , 8 ]]])
# Suppressing 3-D numpy array with precision 4 # using numpy.set_printoptions() print ( "Numpy array values with precision 4:\n" )
np.set_printoptions(precision = 4 , suppress = True )
print (num)
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Output:
Numpy array values with precision 4: [[[ 3.1415 2.7183] [ 6.6269 0. ]] [[34.8454 0. ] [ 7. 8. ]]]
Example 4:
Here we have used the formatter parameter for the set_printoptions() function.
import numpy as np
np.set_printoptions(suppress = True ,
formatter = { 'float_kind' : '{:0.2f}' . format })
a = np.array([ 1.8e - 10 , 1.586 , 150.45 , 0.2855 ])
print (a)
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Output:
[0.00 1.59 150.45 0.29]