I don't understand what it does, and only saw it used by a sklearn-object. On trying some testing like this

x = [(1, 2), (2, 3), (3, 4)]
y = [[1, 2], [2, 3], [3, 4]]

print(y[:, 0])

I got this error (with both x and y):

TypeError: list indices must be integers or slices, not tuple

My assumption was that the : before the comma tells Python to take all entries, while the 0 specifies which 'subset'.

How does one use this expression properly and what does it do?

CodePudding user response：

As explained here this is a numpy-specific notation to index arrays, not plain python. This is why it does not work on your code. In your (initial) case, the sklearn object probably wraps a numpy array that supports the numpy slicing syntax.

In your specific case, it would work as follows:

import numpy as np

y = np.array([[1, 2], [2, 3], [3, 4]])
print(y[:, 0])

# prints: [1 2 3]

This would yield all indexes along the first axis (i.e. use full column vectors), but use only index 0 in the second axis (i.e. use only the first column vector).

CodePudding user response：

What you express would work with more complex objects, like numpy (and its famous slicing). In the case of vanilla Python, this is not possible. To access a specific number (in your case) you would have to do x[2][1] (yielding 4 in your example).

To achieve what you want (take the first item of each tuple), you would do [item[0] for item in y]. This is list comprehension: iterate through y, take the object at the first index of each item, and make a list out of that.