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I have a homework assignment to extract a 2-dimensional numpy array out of another 2-dimensional np array by choosing specific columns by condition (not by range).

So I have an array A with shape (3, 50000). I am trying to get a new array with shape (3, x) for some x < 50000 with the original columns ofAthat satisfy the third cell in the column is-0.4 < z < 0.1`.

For example if:

A = [[1,2,3],[2,0.5,0],[9,-2,-0.2],[0,0,0.5]]

I wish to have back:

B = [[2,0.5,0],[9,-2,-0.2]

I have tried to make a bool 1 rank array that holds true on the columns I want, and to some how combine between the two. The problem it's output is 1 rank array which is not what I am looking for. And I got some ValueErrors..

bool_idx = (-0.4 < x_y_z[2] < 0.1)

This code made some troubles: 

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I can do it with some loops but NumPy got so many beautiful function I am sure I am missing something here..

Mad Physicist
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1 Answers1

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In Python, the expression -0.4 < x_y_z[2] < 0.1 is roughly equivalent to -0.4 < x_y_z[2] and x_y_z[2] < 0.1. The and operator decides the truth value of each part of the expression by converting it into a bool. Unlike Python lists and tuples, numpy arrays do not support the conversion.

The correct way to specify the condition is with bitwise & (which is unambiguous and non-short-circuiting), rather than the implicit and (which short circuits and is ambiguous in this case):

condition = ((x_y_z[2, :] > - 0.4) & (x_y_z[2, :] < 0.1))

condition is a boolean mask that selects the columns you want. You can select the rows with a simple slice:

selection = x_y_z[:, condition]
Mad Physicist
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  • Yesit works! Thank you so much! I dont understand tho what is the meaning of [2, :]. The second row at any column? – Shaq Apr 04 '19 at 08:12