In python the result of a /= 2.0 and a = a / 2.0 are not the same
Question:
In [67]: import numpy as np
In [68]: a = np.arange(10)
In [69]: b = a.copy()
In [70]: a /= 2.0
In [71]: a
Out[71]: array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
In [72]: b = b / 2.0
In [73]:
In [73]: b
Out[73]: array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])
I don’t know why it the results are different when try to deal with numpy array.
Answers:
a = np.arange(10) has an integer dtype.
>>> np.arange(10).dtype
dtype('int64')
Modifying an array inplace — for example, with a /= 2.0 — does not change
the dtype. So the result contains ints.
In contrast, a/2.0 "upcasts" the resultant array to float since the divisor is
a float.
If you start with an array of floating-point dtype, then both operations yield the same result:
In [12]: a = np.arange(10, dtype='float')
In [13]: b = a.copy()
In [14]: a /= 2.0
In [15]: a
Out[15]: array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])
In [16]: b = b / 2.0
In [17]: b
Out[17]: array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])
In [67]: import numpy as np
In [68]: a = np.arange(10)
In [69]: b = a.copy()
In [70]: a /= 2.0
In [71]: a
Out[71]: array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
In [72]: b = b / 2.0
In [73]:
In [73]: b
Out[73]: array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])
I don’t know why it the results are different when try to deal with numpy array.
a = np.arange(10) has an integer dtype.
>>> np.arange(10).dtype
dtype('int64')
Modifying an array inplace — for example, with a /= 2.0 — does not change
the dtype. So the result contains ints.
In contrast, a/2.0 "upcasts" the resultant array to float since the divisor is
a float.
If you start with an array of floating-point dtype, then both operations yield the same result:
In [12]: a = np.arange(10, dtype='float')
In [13]: b = a.copy()
In [14]: a /= 2.0
In [15]: a
Out[15]: array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])
In [16]: b = b / 2.0
In [17]: b
Out[17]: array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])