When slicing a Numpy array, it looks inconsistent to me.
In[87]: y
Out[87]:
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
In[88]: y[0,0]
Out[88]: 1
y[0,0] is 1. That's OK, but when I type
In[89]: y[0,0:1]
Out[89]: array([1])
Why is the slice [0,0:1] the single value array [1]? I was rather expecting array[1,4] which occurs when I type
In[90]: y[0,0:2]
Out[90]: array([1,4])
where I'd rather expect array[1,4,7] since y[0,2] is 7.
By the way, if of importance, I'm using Anaconda 2019 distro.
In[91]: import sys
...: print(sys.version)
3.7.3 (default, Mar 27 2019, 17:13:21) [MSC v.1915 64 bit (AMD64)]
Has anyone a clue why the slicing indexes look so confusing ? Thanks
Numpy uses the same slicing notation as Python does, i.e. [start:stop:step].
As a convention, the value at index stop is excluded from the resulting sequence.
You can find more information at paragraph 3 of this tutorial.
The slice extends from the ‘from’ index and ends one item before the ‘to’ index.