I have a little question about python Numpy. What I want to do is the following:
having two numpy arrays arr1 = [1,2,3] and arr2 = [3,4,5] I would like to obtain a new array arr3 = [[1,2,3],[3,4,5]], but in an iterative way. For a single instance, this is just obtained by typing arr3 = np.array([arr1,arr2]).
What I have instead, are several arrays e.g. [4,3,1 ..], [4,3,5, ...],[1,2,1,...] and I would like to end up with [[4,3,1 ..], [4,3,5, ...],[1,2,1,...]], potentally using a for loop. How should I do this?
EDIT:
Ok I'm trying to add more details to the overall problem. First, I have a list of strings list_strings=['A', 'B','C', 'D', ...]. I'm using a specific method to obtain informative numbers out of a single string, so for example I have method(list_strings[0]) = [1,2,3,...], and I can do this for each single string I have in the initial list.
What I would like to come up with is an iterative for loop to end up having all the numbers extracted from each string in turn in the way I've described at the beginning, i.e.a single array with all the numeric sub-arrays with information extracted from each string. Hope this makes more sense now, and sorry If I haven't explained correctly, I'm really new in programming and trying to figure out stuff.
Well if your strings are in a list, we want to put the arrays that result from calling method in a list as well. Python's list comprehension is a great way to achieve that.
list_strings = ['A', ...]
list_of_converted_strings = [method(item) for item in list_strings]
arr = np.array(list_of_converted_strings)
Numpy arrays are of fixed dimension i.e. for example a 2D numpy array of shape n X m will have n rows and m columns. If you want to convert a list of lists into a numpy array all the the sublists in the main list should be of same length. You cannot convert it into a numpy array if sublist are of varying size.
For example, below code will give an error
np.array([[1], [3,4]]])
so if all the sublist are of same size then you can use
np.array([method(x) for x in strings]])
Related
Hi have an original vector, I would like to put the first 3 elements into new vector, do some math and then get new elements after the math. Put those new elements into a new vector, delete the original first 3 elements from original vector and repeat this exact procedure until the original vector is empty.
This is what I have done so far
OR=np.array([1,2,3,4,5,6])
new=OR[0:3]
while (True):
tran=-2*c_[new]
OR= delete(OR, [0,1,2])
new=OR[0:3]
if (OR==[]):
break
However it is not working out properly, do you have any suggestions?
Not sure what c_ is in your code, but regardless since numpy arrays are not dynamic, you can't remove or add elements to them. Deleting elements creates a new array without those elements, which is not optimal. I think you should either use a python deque which has fast pop methods for removing one element from the front/end, or just iterate over the original numpy array, for example like this:
def modify_array(arr):
# your code for modifying the array here
result = []
original_array = np.arange(1, 10)
for idx in range(0, len(original_array), 3):
result.append(modify_array(original_array[idx:idx+3]))
result = np.concatenate(result)
EDIT:
As my question was badly formulated, I decided to rewrite it.
Does numpy allow to create an array with a function, without using Python's standard list comprehension ?
With list comprehension I could have:
array = np.array([f(i) for i in range(100)])
with f a given function.
But if the constructed array is really big, using Python's list would be slow and would eat a lot of memory.
If such a way doesn't exist, I suppose I could first create an array of my wanted size
array = np.arange(100)
And then map a function over it.
array = f(array)
According to results from another post, it seems that it would be a reasonable solution.
Let's say I want to use the add function with a simple int value, it will be as follows:
array = np.array([i for i in range(5)])
array + 5
But now what if I want the value (here 5) as something that varies according to the index of the array element. For example the operation:
array + [i for i in range(5)]
What object can I use to define special rules for a variable value within a vectorized operation ?
You can add two arrays together like this:
Simple adding two arrays using numpy in python?
This assumes your "variable by index" is just another array.
For your specific example, a jury-rigged solution would be to use numpy.arange() as in:
In [4]: array + np.arange(5)
Out[4]: array([0, 2, 4, 6, 8])
In general, you can find some numpy ufunc that does the job of your custom function or you can compose then in a python function to do so, which then returns an ndarray, something like:
def custom_func():
# code for your tasks
return arr
You can then simply add the returned result to your already defined array as in:
array + cusom_func()
I'm new to python, but I'm solid in coding in vb.net. I'm trying to hold numerical values in a jagged array; to do this in vb.net I would do the following:
Dim jag(3)() as double
For I = 0 to 3
Redim jag(i)(length of this row)
End
Now, I know python doesn't use explicit declarations like this (maybe it can, but I don't know how!). I have tried something like this;
a(0) = someOtherArray
But that doesn't work - I get the error Can't assign to function call. Any advice on a smoother way to do this? I'd prefer to stay away from using a 2D matrix as the different elements of a (ie. a(0), a(1),...) are different lengths.
arr = [[]]
I'm not sure what you're trying to do, python lists is dynamically assigned, but if you want a predefined length and dimension use list comprehensions.
arr = [[0 for x in range(3)] for y in range(3)]
From Microsoft documentation:
A jagged array is an array whose elements are arrays. The elements of
a jagged array can be of different dimensions and sizes
Python documentation about Data Structures.
You could store a list inside another list or a dictionary that stores a list. Depending on how deep your arrays go, this might not be the best option.
numbersList = []
listofNumbers = [1,2,3]
secondListofNumbers = [4,5,6]
numbersList.append(listofNumbers)
numbersList.append(secondListofNumbers)
for number in numbersList:
print(number)
I have an array of 2d indices.
indices = [[2,4], [6,77], [102,554]]
Now, I have a different 4-dimensional array, arr, and I want to only extract an array (it is an array, since it is 4-dimensional) with corresponding index in the indices array. It is equivalent to the following code.
for i in range(len(indices)):
output[i] = arr[indices[i][0], indices[i][1]]
However, I realized that using explicit for-loop yields a slow result. Is there any built-in numpy API that I can utilized? At this point, I tried using np.choose, np.put, np.take, but did not succeed to yield what I wanted. Thank you!
We need to index into the first two axes with the two columns from indices (thinking of it as an array).
Thus, simply convert to array and index, like so -
indices_arr = np.array(indices)
out = arr[indices_arr[:,0], indices_arr[:,1]]
Or we could extract those directly without converting to array and then index -
d0,d1 = [i[0] for i in indices], [i[1] for i in indices]
out = arr[d0,d1]
Another way to extract the elements would be with conversion to tuple, like so -
out = arr[tuple(indices_arr.T)]
If indices is already an array, skip the conversion process and use indices in places where we had indices_arr.
Try using the take function of numpy arrays. Your code should be something like:
outputarray= np.take(arr,indices)
I am trying to work with lists of numpy matrices and am encountering an annoying problem.
Let's say I start with a list of ten 2x2 zero matrices
para=[numpy.matrix(numpy.zeros((2,2)))]*(10)
I access individual matrices like this
para[0]
para[1]
and so on. So far so good.
Now, I want to modify the first row of the second matrix only, leaving all the others unchanged. So I do this
para[1][0]=numpy.matrix([[1,1]])
The first index points to the second matrix in the list and the second index points to the first row in that matrix, replacing it with [1,1].
But strangely enough, this command changes the first row of ALL ten matrices in the list to [1,1] instead of just the second one like I wanted. What gives?
When you multiply the initial list by 10, you end up with a list of 10 numpy arrays which are in fact references to the the same underlying structure. Modifying one will modify all of them because in fact there's only one numpy array, not 10.
If you need proof, check out this example in the REPL:
>>> a = numpy.zeros(10)
>>> a = [numpy.zeros(10)]*10
>>> a[0] is a[1]
True
>>>
The is operator checks if both objects are in fact the same(not if they are equal in value).
What you should do is use a list comprehension to generate your initial arrays instead of a multiplication, like so:
para=[numpy.matrix(numpy.zeros((2,2))) for i in range(10)]
That will call numpy.matrix() ten times instead of just once and generate 10 distinct matrixes.