Reputation: 4744
How can you slice a numpy array by column, and exclude a particular row?
Imagine you have a numpy array where the first column serves as an index as to a 'player', and the next columns are the players scores at different games. How can you return the scores for a game, whilst excluding one player.
For example:
[0 0 0 0
1 2 1 1
2 -6 0 2
3 4 1 3]
If you want to return the first score (column 1), you would do:
>>score[:,1]
[0,2,-6,4]
But how can you exclude a player/row? If that player/row 3, how do you get:
[0,2,-6]
Or, if that player/row 1, how do you get:
[0,-6, 4]
Upvotes: 3
Views: 7989
Reputation: 30483
You can just pass the players that you want to include as a list to the first index of score
like this:
>>> import numpy as np
>>> score = np.array([
... [0,0,0,0],
... [1,2,1,1],
... [2,-6,0,2],
... [3,4,1,3]
... ])
>>> players_to_include = [0,2,3]
>>> score[players_to_include, 1]
array([ 0, -6, 4])
This will get you only player [0,2,3]'s score.
To generalize, you can do:
>>> players = list(xrange(np.size(score, 0)))
>>> players
[0, 1, 2, 3]
>>> excludes = [2,3]
>>> players_to_include = [p for p in players if p not in excludes]
>>> players_to_include
[0, 1]
>>> score[players_to_include, 1]
array([0, 2])
Upvotes: 6
Reputation: 5504
You can enter the range of requested rows as a list, for example:
score[ range(2) + [4], 1]
For a more general predicate function p(x) = 1 if x is a good row, you can do:
score [ [x for x in range(score.shape[0]) if p(x)], 1]
Upvotes: 4