How to use numpy.genfromtxt when first column is string and the remaining columns are numbers?

Question

Basically, I have a bunch of data where the first column is a string (label) and the remaining columns are numeric values. I run the following:

data = numpy.genfromtxt('data.txt', delimiter = ',') 

This reads most of the data well, but the label column just gets 'nan'. How can I deal with this?

Answer 1

By default, np.genfromtxt uses dtype=float: that's why you string columns are converted to NaNs because, after all, they're Not A Number...

You can ask np.genfromtxt to try to guess the actual type of your columns by using dtype=None:

>>> from StringIO import StringIO >>> test = "a,1,2\nb,3,4" >>> a = np.genfromtxt(StringIO(test), delimiter=",", dtype=None) >>> print a array([('a',1,2),('b',3,4)], dtype=[('f0', '|S1'),('f1', '<i8'),('f2', '<i8')]) 

You can access the columns by using their name, like a['f0']...

Using dtype=None is a good trick if you don't know what your columns should be. If you already know what type they should have, you can give an explicit dtype. For example, in our test, we know that the first column is a string, the second an int, and we want the third to be a float. We would then use

>>> np.genfromtxt(StringIO(test), delimiter=",", dtype=("|S10", int, float)) array([('a', 1, 2.0), ('b', 3, 4.0)],        dtype=[('f0', '|S10'), ('f1', '<i8'), ('f2', '<f8')]) 

Using an explicit dtype is much more efficient than using dtype=None and is the recommended way.

In both cases (dtype=None or explicit, non-homogeneous dtype), you end up with a structured array.

[Note: With dtype=None, the input is parsed a second time and the type of each column is updated to match the larger type possible: first we try a bool, then an int, then a float, then a complex, then we keep a string if all else fails. The implementation is rather clunky, actually. There had been some attempts to make the type guessing more efficient (using regexp), but nothing that stuck so far]