Convert Structured Array With Various Numeric Data Types To Regular Array

Suppose I have a NumPy structured array with various numeric datatypes. As a basic example, my_data = np.array( [(17, 182.1), (19, 175.6)], dtype='i2,f4') How can I cast this i

Solution 1:

You can do it easily with Pandas:

>>>import pandas as pd>>>pd.DataFrame(my_data).values
array([[  17.       ,  182.1000061],
       [  19.       ,  175.6000061]], dtype=float32)

Solution 2:

Here's one way (assuming my_data is a one-dimensional structured array):

In [26]: my_data
Out[26]: 
array([(17, 182.10000610351562), (19, 175.60000610351562)], 
      dtype=[('f0', '<i2'), ('f1', '<f4')])

In [27]: np.column_stack(my_data[name] for name in my_data.dtype.names)
Out[27]: 
array([[  17.       ,  182.1000061],
       [  19.       ,  175.6000061]], dtype=float32)

Solution 3:

The obvious way works:

>>> my_data
array([(17, 182.10000610351562), (19, 175.60000610351562)],
      dtype=[('f0', '<i2'), ('f1', '<f4')])
>>> n = len(my_data.dtype.names)  # n ==2>>> my_data.astype(','.join(['f4']*n))
array([(17.0, 182.10000610351562), (19.0, 175.60000610351562)],
      dtype=[('f0', '<f4'), ('f1', '<f4')])
>>> my_data.astype(','.join(['f4']*n)).view('f4')
array([  17.       ,  182.1000061,   19.       ,  175.6000061], dtype=float32)
>>> my_data.astype(','.join(['f4']*n)).view('f4').reshape(-1, n)
array([[  17.       ,  182.1000061],
       [  19.       ,  175.6000061]], dtype=float32)

Solution 4:

A variation on Warren's answer (which copies data by field):

x = np.empty((my_data.shape[0],len(my_data.dtype)),dtype='f4')
for i,n inenumerate(my_data.dtype.names):
    x[:,i]=my_data[n]

Or you could iterate by row. r is a tuple. It has to be converted to a list in order to fill a row of x. With many rows and few fields this will be slower.

for i,r in enumerate(my_data):
    x[i,:]=list(r)

It may be instructive to try x.data=r.data, and get an error: AttributeError: not enough data for array. x data is a buffer with 4 floats. my_data is a buffer with 2 tuples, each of which contains an int and a float (or sequence of [int float int float]). my_data.itemsize==6. One way or other, the my_data has to be converted to all floats, and the tuple grouping removed.

But using astype as Jaime shows does work:

x.data=my_data.astype('f4,f4').data

In quick tests using a 1000 item array with 5 fields, copying field by field is just as fast as using astype.