Numpy ndarray - how to select a row based on values for multiple columns

Question

I have a ndarray of x and y component of 2D speed values of an object, such as:

(Pdb) p highspeedentries
array([[  0.  ,  52.57],
   [-40.58,  70.89],
   [-57.32,  76.47],
   [-57.92,  65.1 ],
   [-52.47,  96.68],
   [-45.58,  77.12],
   [-37.83,  69.52]])

(Pdb) p type(highspeedentries)
<class 'numpy.ndarray'>

I have to check if there is any row that contains a value higher than 55 for the first and second components in the row. I also need to take the absolute value for any negative speed component, but first I tried the following and all of them give errors.

(Pdb) p highspeedentries[highspeedentries[:,1] > 55 and highspeedentries[0,:] > 55]
*** ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

(Pdb) p highspeedentries[highspeedentries[:,1] > 55 & highspeedentries[0,:] > 55]
*** TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be 
safely coerced to any supported types according to the casting rule ''safe''

(Pdb) p highspeedentries[np.logical_and(highspeedentries[:,1] > 55, highspeedentries[0,:] > 55)]
*** ValueError: operands could not be broadcast together with shapes (7,) (2,)

highspeedentries[0,:] > 55 replace with highspeedentries[:,0] > 55 — bubble
– bubble, Commented Nov 14, 2019 at 0:04

10762409 · Accepted Answer · 2019-11-14 00:15:16Z

1

One dumb way of approaching it is to just loop over the entire array looking for your state:

for i in highspeedentries:
    if (abs(i[0]) > 55 and abs(i[1]) > 55):
        print('True')
        break
else:
    print('False')

Alternatively, you were on the right track for your third attempt:

logical_and(abs(highspeedentries[:, 0]) > 55 , abs(highspeedentries[:, 1]) > 55).any()

The order of indices was incorrect, and if you add .any() to the end you get a single value (True if at least one element is True, False otherwise) instead of a boolean array. To apply the absolute value, you can just apply abs() to the arrays before comparing with 55.

edited Nov 14, 2019 at 0:15

answered Nov 14, 2019 at 0:04

10762409

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