Python - strings and integers in Numpy array

Question

I want to create an array with 3 columns. The first one a string, the other two integers used for calculations. Then more rows will be added through the append function (below).

Answer 1

When collecting values iteratively, it is usually best to collect them in a list, and make the array afterwards:

For example, making a list with your data:

In [371]: alist = [("String", 1, 2)]
In [372]: alist.append(("another string", 3, 4))
In [373]: alist
Out[373]: [('String', 1, 2), ('another string', 3, 4)]

For many purposes that list is quite useful, alist[0], or [i[0] for i in alist].

To make a list, one option is a structured array. Because I collected values as a list of tuples I can do:

In [374]: np.array(alist, dtype='U20,int,int')
Out[374]: 
array([('String', 1, 2), ('another string', 3, 4)], 
      dtype=[('f0', '<U20'), ('f1', '<i4'), ('f2', '<i4')])
In [375]: _['f1']
Out[375]: array([1, 3])

We access fields of such an array by field name. The array itself is 1d, (2,).

If instead we make an object dtype array:

In [376]: np.array(alist, dtype=object)
Out[376]: 
array([['String', 1, 2],
       ['another string', 3, 4]], dtype=object)
In [377]: _.shape
Out[377]: (2, 3)
In [378]: __[:,1]
Out[378]: array([1, 3], dtype=object)

With this we can access rows and columns. But beware that we don't get the fast numpy calculation benefits with a object array, especially one with mixed types.

Answer 2

To have such a mixed datatype data, we could use object as dtype before appending or stacking -

a = np.array([["String",1,2]], dtype=object)
b = [["another string", 3, 4]]
a = np.vstack((a,np.asarray(b,object)))

Sample run -

In [40]: a = np.array([["String",1,2]], dtype=object)

In [41]: b = [["another string", 3, 4]]

In [42]: np.vstack((a,np.asarray(b,object)))
Out[42]: 
array([['String', 1, 2],
       ['another string', 3, 4]], dtype=object)