You can use:
>>> lst = [None] * 5
>>> lst
[None, None, None, None, None]
python - Create an array of size n with initialized value - Stack Overflow
python - initialize a numpy array - Stack Overflow
Creating an array who size and values depend on another array's values being put through a function
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You can use:
>>> lst = [None] * 5
>>> lst
[None, None, None, None, None]
Why don't these questions get answered with the obvious answer?
a = numpy.empty(n, dtype=object)
This creates an array of length n that can store objects. It can't be resized or appended to. In particular, it doesn't waste space by padding its length. This is the Python equivalent of Java's
Object[] a = new Object[n];
If you're really interested in performance and space and know that your array will only store certain numeric types then you can change the dtype argument to some other value like int. Then numpy will pack these elements directly into the array rather than making the array reference int objects.
numpy.zerosReturn a new array of given shape and type, filled with zeros.
or
numpy.onesReturn a new array of given shape and type, filled with ones.
or
numpy.emptyReturn a new array of given shape and type, without initializing entries.
However, the mentality in which we construct an array by appending elements to a list is not much used in numpy, because it's less efficient (numpy datatypes are much closer to the underlying C arrays). Instead, you should preallocate the array to the size that you need it to be, and then fill in the rows. You can use numpy.append if you must, though.
The way I usually do that is by creating a regular list, then append my stuff into it, and finally transform the list to a numpy array as follows :
Copyimport numpy as np
big_array = [] # empty regular list
for i in range(5):
arr = i*np.ones((2,4)) # for instance
big_array.append(arr)
big_np_array = np.array(big_array) # transformed to a numpy array
of course your final object takes twice the space in the memory at the creation step, but appending on python list is very fast, and creation using np.array() also.
Hi all,
I'm trying to create an 2 arrays. The 2nd array is the one I'm having trouble with. It needs to have a size based on a user inputted length.
These are my steps:
-
I ask the user to input a start number and an end number.
-
I create the first array using arange(first, last, 1)
-
Each of the values in between first and last go through as the single input to another function.
-
For the second array, each value in the array is the return values. So this gives 2 arrays of the same length that line up.
I don't know how to write step 3. I imagine it may even include a for loop. Something like
1st_array = (first, last, 1) difference = last - first 2nd_array = zeros(int(difference) for i in range(first, last) add value to 2nd array(function(1st array value 1), function(1st array value 2, .... function(1st array value n)
How could I do this?