Converting a 2D numpy array to a structured array

I'm trying to convert a two-dimensional array into a structured array with named fields. I want each row in the 2D array to be a new record in the structured array. Unfortunately, nothing I've tried is working the way I expect.

I'm starting with:

>>> myarray = numpy.array([("Hello",2.5,3),("World",3.6,2)])
>>> print myarray
[['Hello' '2.5' '3']
 ['World' '3.6' '2']]

I want to convert to something that looks like this:

>>> newarray = numpy.array([("Hello",2.5,3),("World",3.6,2)], dtype=[("Col1","S8"),("Col2","f8"),("Col3","i8")])
>>> print newarray
[('Hello', 2.5, 3L) ('World', 3.6000000000000001, 2L)]

What I've tried:

>>> newarray = myarray.astype([("Col1","S8"),("Col2","f8"),("Col3","i8")])
>>> print newarray
[[('Hello', 0.0, 0L) ('2.5', 0.0, 0L) ('3', 0.0, 0L)]
 [('World', 0.0, 0L) ('3.6', 0.0, 0L) ('2', 0.0, 0L)]]

>>> newarray = numpy.array(myarray, dtype=[("Col1","S8"),("Col2","f8"),("Col3","i8")])
>>> print newarray
[[('Hello', 0.0, 0L) ('2.5', 0.0, 0L) ('3', 0.0, 0L)]
 [('World', 0.0, 0L) ('3.6', 0.0, 0L) ('2', 0.0, 0L)]]

Both of these approaches attempt to convert each entry in myarray into a record with the given dtype, so the extra zeros are inserted. I can't figure out how to get it to convert each row into a record.

Another attempt:

>&g开发者_开发技巧t;> newarray = myarray.copy()
>>> newarray.dtype = [("Col1","S8"),("Col2","f8"),("Col3","i8")]
>>> print newarray
[[('Hello', 1.7219343871178711e-317, 51L)]
 [('World', 1.7543139673493688e-317, 50L)]]

This time no actual conversion is performed. The existing data in memory is just re-interpreted as the new data type.

The array that I'm starting with is being read in from a text file. The data types are not known ahead of time, so I can't set the dtype at the time of creation. I need a high-performance and elegant solution that will work well for general cases since I will be doing this type of conversion many, many times for a large variety of applications.

Thanks!

You can "create a record array from a (flat) list of arrays" using numpy.core.records.fromarrays as follows:

>>> import numpy as np
>>> myarray = np.array([("Hello",2.5,3),("World",3.6,2)])
>>> print myarray
[['Hello' '2.5' '3']
 ['World' '3.6' '2']]


>>> newrecarray = np.core.records.fromarrays(myarray.transpose(), 
                                             names='col1, col2, col3',
                                             formats = 'S8, f8, i8')

>>> print newrecarray
[('Hello', 2.5, 3) ('World', 3.5999999046325684, 2)]

I was trying to do something similar. I found that when numpy created a structured array from an existing 2D array (using np.core.records.fromarrays), it considered each column (instead of each row) in the 2-D array as a record. So you have to transpose it. This behavior of numpy does not seem very intuitive, but perhaps there is a good reason for it.

If the data starts as a list of tuples, then creating a structured array is straight forward:

In [228]: alist = [("Hello",2.5,3),("World",3.6,2)]
In [229]: dt = [("Col1","S8"),("Col2","f8"),("Col3","i8")]
In [230]: np.array(alist, dtype=dt)
Out[230]: 
array([(b'Hello',  2.5, 3), (b'World',  3.6, 2)], 
      dtype=[('Col1', 'S8'), ('Col2', '<f8'), ('Col3', '<i8')])

The complication here is that the list of tuples has been turned into a 2d string array:

In [231]: arr = np.array(alist)
In [232]: arr
Out[232]: 
array([['Hello', '2.5', '3'],
       ['World', '3.6', '2']], 
      dtype='<U5')

We could use the well known zip* approach to 'transposing' this array - actually we want a double transpose:

In [234]: list(zip(*arr.T))
Out[234]: [('Hello', '2.5', '3'), ('World', '3.6', '2')]

zip has conveniently given us a list of tuples. Now we can recreate the array with desired dtype:

In [235]: np.array(_, dtype=dt)
Out[235]: 
array([(b'Hello',  2.5, 3), (b'World',  3.6, 2)], 
      dtype=[('Col1', 'S8'), ('Col2', '<f8'), ('Col3', '<i8')])

The accepted answer uses fromarrays:

In [236]: np.rec.fromarrays(arr.T, dtype=dt)
Out[236]: 
rec.array([(b'Hello',  2.5, 3), (b'World',  3.6, 2)], 
          dtype=[('Col1', 'S8'), ('Col2', '<f8'), ('Col3', '<i8')])

Internally, fromarrays takes a common recfunctions approach: create target array, and copy values by field name. Effectively it does:

In [237]: newarr = np.empty(arr.shape[0], dtype=dt)
In [238]: for n, v in zip(newarr.dtype.names, arr.T):
     ...:     newarr[n] = v
     ...:     
In [239]: newarr
Out[239]: 
array([(b'Hello',  2.5, 3), (b'World',  3.6, 2)], 
      dtype=[('Col1', 'S8'), ('Col2', '<f8'), ('Col3', '<i8')])

I guess

new_array = np.core.records.fromrecords([("Hello",2.5,3),("World",3.6,2)],
                                        names='Col1,Col2,Col3',
                                        formats='S8,f8,i8')

is what you want.

Okay, I have been struggling with this for a while now but I have found a way to do this that doesn't take too much effort. I apologise if this code is "dirty"....

Let's start with a 2D array:

mydata = numpy.array([['text1', 1, 'longertext1', 0.1111],
                     ['text2', 2, 'longertext2', 0.2222],
                     ['text3', 3, 'longertext3', 0.3333],
                     ['text4', 4, 'longertext4', 0.4444],
                     ['text5', 5, 'longertext5', 0.5555]])

So we end up with a 2D array with 4 columns and 5 rows:

mydata.shape
Out[30]: (5L, 4L)

To use numpy.core.records.arrays - we need to supply the input argument as a list of arrays so:

tuple(mydata)
Out[31]: 
(array(['text1', '1', 'longertext1', '0.1111'], 
      dtype='|S11'),
 array(['text2', '2', 'longertext2', '0.2222'], 
      dtype='|S11'),
 array(['text3', '3', 'longertext3', '0.3333'], 
      dtype='|S11'),
 array(['text4', '4', 'longertext4', '0.4444'], 
      dtype='|S11'),
 array(['text5', '5', 'longertext5', '0.5555'], 
      dtype='|S11'))

This produces a separate array per row of data BUT, we need the input arrays to be by column so what we will need is:

tuple(mydata.transpose())
Out[32]: 
(array(['text1', 'text2', 'text3', 'text4', 'text5'], 
      dtype='|S11'),
 array(['1', '2', '3', '4', '5'], 
      dtype='|S11'),
 array(['longertext1', 'longertext2', 'longertext3', 'longertext4',
       'longertext5'], 
      dtype='|S11'),
 array(['0.1111', '0.2222', '0.3333', '0.4444', '0.5555'], 
      dtype='|S11'))

Finally it needs to be a list of arrays, not a tuple, so we wrap the above in list() as below:

list(tuple(mydata.transpose()))

That is our data input argument sorted.... next is the dtype:

mydtype = numpy.dtype([('My short text Column', 'S5'),
                       ('My integer Column', numpy.int16),
                       ('My long text Column', 'S11'),
                       ('My float Column', numpy.float32)])
mydtype
Out[37]: dtype([('My short text Column', '|S5'), ('My integer Column', '<i2'), ('My long text Column', '|S11'), ('My float Column', '<f4')])

Okay, so now we can pass that to the numpy.core.records.array():

myRecord = numpy.core.records.array(list(tuple(mydata.transpose())), dtype=mydtype)

... and fingers crossed:

myRecord
Out[36]: 
rec.array([('text1', 1, 'longertext1', 0.11110000312328339),
       ('text2', 2, 'longertext2', 0.22220000624656677),
       ('text3', 3, 'longertext3', 0.33329999446868896),
       ('text4', 4, 'longertext4', 0.44440001249313354),
       ('text5', 5, 'longertext5', 0.5554999709129333)], 
      dtype=[('My short text Column', '|S5'), ('My integer Column', '<i2'), ('My long text Column', '|S11'), ('My float Column', '<f4')])

Voila! You can index by column name as in:

myRecord['My float Column']
Out[39]: array([ 0.1111    ,  0.22220001,  0.33329999,  0.44440001,  0.55549997], dtype=float32)

I hope this helps as I wasted so much time with numpy.asarray and mydata.astype etc trying to get this to work before finally working out this method.

There's a lot of confusion here between "record array" and "structured array". Here's my short solution for a structured array.

dtype = np.dtype([("Col1","S8"),("Col2","f8"),("Col3","i8")])
myarray = np.array([("Hello",2.5,3),("World",3.6,2)], dtype=dtype)
np.array(np.rec.fromarrays(myarray.transpose(), names=dtype.names).astype(dtype=dtype).tolist(), dtype=dtype)

So, with the assumption that dtype is defined, this is a one-liner.