Time for action – computing the modulo

Let's call the previously mentioned functions:

  1. The remainder function returns the remainder of the two arrays, element-wise. 0 is returned if the second number is 0:
    a = np.arange(-4, 4)
print "Remainder", np.remainder(a, 2)

    The result of the remainder function is shown as follows:

    Remainder [0 1 0 1 0 1 0 1]
  2. The mod function does exactly the same as the remainder function:
    print "Mod", np.mod(a, 2)

    The result of the mod function is shown as follows:

    Mod [0 1 0 1 0 1 0 1]
  3. The % operator is just shorthand for the remainder function:
    print "% operator", a % 2

    The result of the % operator is shown as follows:

    % operator [0 1 0 1 0 1 0 1]
  4. The fmod function handles negative numbers differently than mod, fmod, and % do. The sign of the remainder is the sign of the dividend, and the sign of the divisor has no influence on the results:
    print "Fmod", np.fmod(a, 2)

    The fmod result is printed as follows:

    Fmod [ 0 -1  0 -1  0  1  0  1]

What just happened?

We demonstrated the NumPy mod, remainder, and fmod functions, which compute the modulo, or remainder (see modulo.py):

import numpy as np

a = np.arange(-4, 4)

print "Remainder", np.remainder(a, 2)
print "Mod", np.mod(a, 2)
print "% operator", a % 2
print "Fmod", np.fmod(a, 2)
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