Lists have a very simple method to insert elements:
a = [1,2,3,4] a.insert(2,66) print a [1, 2, 66, 3, 4] For a numpy array I could do:
a = np.asarray([1,2,3,4]) a_l = a.tolist() a_l.insert(2,66) a = np.asarray(a_l) print a [1 2 66 3 4] but this is very convoluted.
Is there an insert equivalent for numpy arrays?
3 Answers
You can use numpy.insert, though unlike list.insert it returns a new array because arrays in NumPy have fixed size.
>>> import numpy as np >>> a = np.asarray([1,2,3,4]) >>> np.insert(a, 2, 66) array([ 1, 2, 66, 3, 4]) If you just want to insert items in consequent indices, as a more optimized way you can use np.concatenate() to concatenate slices of the array with your intended items:
For example in this case you can do:
In [21]: np.concatenate((a[:2], [66], a[2:])) Out[21]: array([ 1, 2, 66, 3, 4]) Benchmark (5 time faster than insert ):
In [19]: %timeit np.concatenate((a[:2], [66], a[2:])) 1000000 loops, best of 3: 1.43 us per loop In [20]: %timeit np.insert(a, 2, 66) 100000 loops, best of 3: 6.86 us per loop And here is a benchmark with larger arrays (still 5 time faster):
In [22]: a = np.arange(1000) In [23]: %timeit np.concatenate((a[:300], [66], a[300:])) 1000000 loops, best of 3: 1.73 us per loop In [24]: %timeit np.insert(a, 300, 66) 100000 loops, best of 3: 7.72 us per loop To add elements to a numpy array you can use the method 'append' passing it the array and the element that you want to add. For example:
import numpy as np dummy = [] dummy = np.append(dummy,12)
this will create an empty array and add the number '12' to it
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