Adding records to a numpy record array

You can use numpy.append(), but as you need to convert the new data into a record array also:

import numpy as npy = np.zeros(4,dtype=('a4,int32,float64'))y = np.append(y, np.array([("0",7,24.5)], dtype=y.dtype))

Since ndarray can't dynamic change it's size, you need to copy all the data when you want to append some new data. You can create a class that reduce the resize frequency:

import numpy as npclass DynamicRecArray(object):    def __init__(self, dtype):        self.dtype = np.dtype(dtype)        self.length = 0        self.size = 10        self._data = np.empty(self.size, dtype=self.dtype)    def __len__(self):        return self.length    def append(self, rec):        if self.length == self.size:            self.size = int(1.5*self.size)            self._data = np.resize(self._data, self.size)        self._data[self.length] = rec        self.length += 1    def extend(self, recs):        for rec in recs:            self.append(rec)    @property    def data(self):        return self._data[:self.length]y = DynamicRecArray(('a4,int32,float64'))y.extend([("xyz", 12, 3.2), ("abc", 100, 0.2)])y.append(("123", 1000, 0))print y.datafor i in xrange(100):    y.append((str(i), i, i+0.1))

This is because concatenating numpy arrays is typically avoided as it requires reallocation of contiguous memory space. Size your array with room to spare, and then concatenate in large chunks if needed. This post may be of some help.