[FIXED] How do you remove a column from a structured numpy array?

Issue

Imagine you have a structured numpy array, generated from a csv with the first row as field names. The array has the form:

dtype([('A', '<f8'), ('B', '<f8'), ('C', '<f8'), ..., ('n','<f8'])

Now, lets say you want to remove from this array the 'ith' column. Is there a convenient way to do that?

I'd like a it to work like delete:

new_array = np.delete(old_array, 'i')

Any ideas?

Solution

It's not quite a single function call, but the following shows one way to drop the i-th field:

In [67]: a
Out[67]: 
array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)], 
      dtype=[('A', '<f8'), ('B', '<f8'), ('C', '<f8')])

In [68]: i = 1   # Drop the 'B' field

In [69]: names = list(a.dtype.names)

In [70]: names
Out[70]: ['A', 'B', 'C']

In [71]: new_names = names[:i] + names[i+1:]

In [72]: new_names
Out[72]: ['A', 'C']

In [73]: b = a[new_names]

In [74]: b
Out[74]: 
array([(1.0, 3.0), (4.0, 6.0)], 
      dtype=[('A', '<f8'), ('C', '<f8')])

Wrapped up as a function:

def remove_field_num(a, i):
    names = list(a.dtype.names)
    new_names = names[:i] + names[i+1:]
    b = a[new_names]
    return b

It might be more natural to remove a given field name:

def remove_field_name(a, name):
    names = list(a.dtype.names)
    if name in names:
        names.remove(name)
    b = a[names]
    return b

Also, check out the drop_rec_fields function that is part of the mlab module of matplotlib.

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Update: See my answer at How to remove a column from a structured numpy array *without copying it*? for a method to create a view of subsets of the fields of a structured array without making a copy of the array.

Answered By - Warren Weckesser