structured-array

I am using numpy 1.16.2. In brief, I am wondering how to add an object-type field to a structured array. The standard way via the recfunctions module throws an error and I suppose there is a reason for this. Therefore, I wonder whether there is anything wrong with my workaround. Furthermore, I would like to understand why this workaround is necessary and whether I need to use extra caution when accessing the newly created array. Now here come the details: I have a numpy structured array: import numpy as np a = np.zeros(3, dtype={'names':['A','B','C'], 'formats':['int','int','float']}) for i in

Here's what happens if I initialize a struct array with the same field names and types in different ways: >>> a = np.zeros(2, dtype=[('x','int64'),('y','a')]) >>> a array([(0L, ''), (0L, '')], dtype=[('x', '<i8'), ('y', 'S')]) So initializing with list of tuples works fine. >>> mdtype = dict(names=['x','y'],formats=['int64','a']) >>> mdtype {'names': ['x', 'y'], 'formats': ['int64', 'a']} >>> a = np.zeros(2,dtype=mdtype) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: data type not understood So initializing with a dict doesn't, and the problem is the string

I understand how to create a masked array, and I would like to use masking in a record array so that I can access this data using named attributes. The masking seems to be "lost" when I create a record array from a masked array: >>> data = np.ma.array(np.ma.zeros(30, dtype=[('date', '|O4'), ('price', '<f8')]),mask=[i<10 for i in range(30)]) >>> data masked_array(data = [(--, --) (--, --) (--, --) (--, --) (--, --) (--, --) (--, --) (--, --) (--, --) (--, --) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0, 0.0) (0

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 into a regular NumPy array of floats? From this answer , I know I could use np.array(my_data.tolist()) but apparently it is slow since you "convert an efficiently packed NumPy array to a regular Python list". 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) Here's one way (assuming my_data is a one-dimensional

Okay, so after going through the tutorials on numpy's structured arrays I am able to create some simple examples: from numpy import array, ones names=['scalar', '1d-array', '2d-array'] formats=['float64', '(3,)float64', '(2,2)float64'] my_dtype = dict(names=names, formats=formats) struct_array1 = ones(1, dtype=my_dtype) struct_array2 = array([(42., [0., 1., 2.], [[5., 6.],[4., 3.]])], dtype=my_dtype) (My intended use case would have more than three entries and would use very long 1d-arrays.) So, all goes well until we try to perform some basic math. I get errors for all of the following: