Question

I’m running genfromtxt like below:

date_conv = lambda x: str(x).replace(":", "/")
time_conv = lambda x: str(x)

a = np.genfromtxt(input.txt, delimiter=',', skip_header=4,
      usecols=[0, 1] + radii_indices, converters={0: date_conv, 1: time_conv})

Where input.txt is from this gist.

When I look at the results, it is a 1D array not a 2D array:

>>> np.shape(a)
(918,)

It seems to be an array of tuples instead:

>>> a[0]
('06/03/2006', '08:27:23', 6.4e-05, 0.000336, 0.001168, 0.002716, 0.004274, 0.004658, 0.003756, 0.002697, 0.002257, 0.002566, 0.003522, 0.004471, 0.00492, 0.005602, 0.006956, 0.008442, 0.008784, 0.006976, 0.003917, 0.001494, 0.000379, 6.4e-05)

If I remove the converters specification from the genfromtxt call it works fine and produces a 2D array:

>>> np.shape(a)
(918, 24)

Answer 1

What is returned is called a structured ndarray, see e.g. here: http://docs.scipy.org/doc/numpy/user/basics.rec.html. This is because your data is not homogeneous, i.e. not all elements have the same type: the data contains both strings (the first two columns) and floats. Numpy arrays have to be homogeneous (see here for an explanation).

The structured array ‘solves’ this constraint of homogeneity by using tuples for each record or row, that’s the reason the returned array is 1D: one series of tuples, but each tuple (row) consists of several fields, so you can regard it as rows and columns. The different columns are accessible as a['nameofcolumn'] e.g. a['Julian_Day'].

The reason that it returns a 2D array when removing the converters for the first two columns is that in that case, genfromtxt regards all data of the same type, and a normal ndarray is returned (the default type is float, but you can specify this with the dtype argument).

EDIT: If you want to make use of the column names, you can use the names argument (and set the skip_header at only three):

a2 = np.genfromtxt("input.txt", delimiter=',', skip_header=3, names = True, dtype = None,
                  usecols=[0, 1] + radii_indices, converters={0: date_conv, 1: time_conv})

the you can do e.g.:

>>> a2['Dateddmmyyyy']
array(['06/03/2006', '06/03/2006', '18/03/2006', '19/03/2006',
       '19/03/2006', '19/03/2006', '19/03/2006', '19/03/2006',
       '19/03/2006', '19/03/2006'], 
      dtype='|S10')