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Differences On Numpyp.Floor And Python Int method.md
numpy.floor Return the floor of the input.
The floor of the scalar x is the largest integer i, such that i <= x;
np.floor() will not change its data type;
Example:
import numpy as np
tmp_list = list([-3.3, -2.22, -1.56, -0.56, 0.56, 1.56, 2.22, 3.33])
tmp_list = np.array(tmp_list)
print("type(tmp_list[0]):", type(tmp_list[0]) )
print("type(np.floor(tmp_list)[0]):", type(np.floor(tmp_list)[0]) )
print("np.floor(tmp_list):", np.floor(tmp_list))
# Output:
# type(tmp_list[0]): <class 'numpy.float64'>
# type(np.floor(tmp_list)[0]): <class 'numpy.float64'>
# np.floor(tmp_list): [-4. -3. -2. -1. 0. 1. 2. 3.]
for x in tmp_list:
print("x:",x,",int:",int(x))
# output:
# x: -3.3 ,int: -3
# x: -2.22 ,int: -2
# x: -1.56 ,int: -1
# x: -0.56 ,int: 0
# x: 0.56 ,int: 0
# x: 1.56 ,int: 1
# x: 2.22 ,int: 2
# x: 3.33 ,int: 3
For floating point numbers, int() will truncates toward zero, so -0.1 will by truncate to 0, -1.8 to -1; And its data type will be changed to <class 'int'>.
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