numpy, named columns

Question

Simple question about numpy:

I load 100 values to a vector a. From this vector, I want to create an array A with 2 columns, where one column has name "C1" and second one "C2", one has type int32 and another int64. An example:

a = range(100) A = array(a).reshape( len(a)/2, 2) # A.dtype = ...? 

How to define the columns' types and names, when I create array from a?

Answer 1

NumPy structured arrays have named columns:

import numpy as np      a = range(100) A = np.array(list(zip(*[iter(a)] * 2)), dtype=[('C1', 'int32'),('C2', 'int64')]) print(A.dtype) 

[('C1', '<i4'), ('C2', '<i8')] 

You can access the columns by name like this:

print(A['C1']) # [ 0  2  4  6  8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 #  50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98] 

Note that using np.array with zip causes NumPy to build an array from a temporary list of tuples. Python lists of tuples use a lot more memory than equivalent NumPy arrays. So if your array is very large you may not want to use zip.

Instead, given a NumPy array A, you could use ravel() to make A a 1D array, and then use view to turn it into a structured array, and then use astype to convert the columns to the desired type:

a = range(100) A = np.array(a).reshape( len(a)//2, 2) A = A.ravel().view([('col1','i8'),('col2','i8'),]).astype([('col1','i4'),('col2','i8'),]) print(A[:5]) # array([(0, 1), (2, 3), (4, 5), (6, 7), (8, 9)],  #       dtype=[('col1', '<i4'), ('col2', '<i8')])  print(A.dtype) # dtype([('col1', '<i4'), ('col2', '<i8')]) 

Answer 2

I know this is an old question, but a more recently available option would be to try using pandas. The DataFrame type is designed for structured data like this, where columns are named and can be of different types.