Programmatically add column names to numpy ndarray

Question

I\'m trying to add column names to a numpy ndarray, then select columns by their names. But it doesn\'t work. I can\'t tell if the problem occurs when I add the names, or late

Answer 1

Unfortunately, I don't know what is going on when you try to add the field names, but I do know that you can build the array you want directly from the file via

data = np.genfromtxt(csv_file, delimiter=',', names=True)

EDIT:

It seems like adding field names only works when the input is a list of tuples:

data = np.array(map(tuple,data), [(n, 'float64') for n in csv_names])

Answer 2

The problem is that you are thinking in terms of spreadsheet-like arrays, whereas NumPy does use different concepts.

Here is what you must know about NumPy:

1. NumPy arrays only contain elements of a single type.
2. If you need spreadsheet-like "columns", this type must be some tuple-like type. Such arrays are called Structured Arrays, because their elements are structures (i.e. tuples).

In your case, NumPy would thus take your 2-dimensional regular array and produce a one-dimensional array whose type is a 108-element tuple (the spreadsheet array that you are thinking of is 2-dimensional).

These choices were probably made for efficiency reasons: all the elements of an array have the same type and therefore have the same size: they can be accessed, at a low-level, very simply and quickly.

Now, as user545424 showed, there is a simple NumPy answer to what you want to do (genfromtxt() accepts a names argument with column names).

If you want to convert your array from a regular NumPy ndarray to a structured array, you can do:

data.view(dtype=[(n, 'float64') for n in csv_names]).reshape(len(data))

(you were close: you used astype() instead of view()).

You can also check the answers to quite a few Stackoverflow questions, including Converting a 2D numpy array to a structured array and how to convert regular numpy array to record array?.