Consider 2D Numpy array A and in-place function x like
A = np.arange(9).reshape(3,3)
def x(M):
M[:,2] = 0
Now, I have a list (or 1D numpy array) L pointing the rows, I want to select and apply the function f on them like
L = [0, 1]
x(A[L, :])
where the output will be written to A. Since I used index access to A, the matrix A is not affected at all:
A = array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
What I actually need is to slice the matrix such as
x(A[:2, :])
giving me the desired output
A = array([[0, 1, 0],
[3, 4, 0],
[6, 7, 8]])
The question is now, how to provide Numpy array slicing by the list L (either any automatic conversion of list to slice or if there is any build in function for that), because I am not able to convert the list L easily to slice like :2 in this case.
Note that I have both large matrix A and list L in my problem - that is the reason, why I would need the in-place operations to control the available memory.
Can you modify the function so as you can pass slice L inside it:
def func(M,L):
M[L,2] = 0
func(A,L)
print(A)
Out:
array([[0, 1, 0],
[3, 4, 0],
[6, 7, 8]])
Related
I stumbled upon what I think is a weird (or at least unintuitive) behavior of numpy and I would like to understand why it behaves that way.
Let's generate a generic array of shape (4, 3, 3).
import numpy as np
arr = np.arange(4*3*3).reshape((4, 3, 3))
Thinking about arr as a list of four three-by-three matrices I want to now swap the first two columns of the first matrix in the list. I can just reorder the columns with an index list:
idx = np.array([1, 0, 2])
m = arr[0]
m[:, idx]
>>> array([[1, 0, 2],
[4, 3, 5],
[7, 6, 8]])
I see that i successfully swapped the two columns. However, if I do try to do same directly with arr, I get:
arr[0, :, idx]
>>> array([[1, 4, 7],
[0, 3, 6],
[2, 5, 8]])
I guess I'm doing something wrong but I don't understand this behavior.
This weird output is because when you are doing
m = arr[0]
m[:, idx]
then m becomes a whole different "array" with data of "arr"
but when you are doing
arr[0, :, arr] there arr is a list which has arrays
Sat I have the following numpy array:
arr = numpy.array([[0,0], [1, 0], [2, 0], [3, 0]])
How do I add a single sub-array on each of the six sub-arrays? (Say if want to add [2,1] to each of them then the output should be [[2,1], [3, 1], [4, 1], [5, 1]])
I know if it's a 1D array you can just write something like arr + 1 and it will add 1 to each elements in arr but what about in this case? I have yet to be able to find relative information in the documentations
arr = np.array([np.append(item, [2,1]) for item in arr])
This should give you the result
I don't understand array as index in Python Numpy.
For example, I have a 2d array A in Numpy
[[1,2,3]
[4,5,6]
[7,8,9]
[10,11,12]]
What does A[[1,3], [0,1]] mean?
Just test it for yourself!
A = np.arange(12).reshape(4,3)
print(A)
>>> array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
By slicing the array the way you did (docs to slicing), you'll get the first row, zero-th column element and the third row, first column element.
A[[1,3], [0,1]]
>>> array([ 3, 10])
I'd highly encourage you to play around with that a bit and have a look at the documentation and the examples.
Your are creating a new array:
import numpy as np
A = [[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12]]
A = np.array(A)
print(A[[1, 3], [0, 1]])
# [ 4 11]
See Indexing, Slicing and Iterating in the tutorial.
Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas
Quoting the doc:
def f(x,y):
return 10*x+y
b = np.fromfunction(f, (5, 4), dtype=int)
print(b[2, 3])
# -> 23
You can also use a NumPy array as index of an array. See Index arrays in the doc.
NumPy arrays may be indexed with other arrays (or any other sequence- like object that can be converted to an array, such as lists, with the exception of tuples; see the end of this document for why this is). The use of index arrays ranges from simple, straightforward cases to complex, hard-to-understand cases. For all cases of index arrays, what is returned is a copy of the original data, not a view as one gets for slices.
Suppose I have an array, I want to have a matrix from that array by a matrix of index.
import numpy as np
arr = np.array([1,5])
mtxidx = np.array([[0,1,0],[0,1,1],[0,0,0]])
How can I get a matrix [[1,5,1],[1,5,5],[1,1,1]] ?
An initial thought is simply say
arr(mtxidx)
however it doesn't work
Is there any function/method that do this elegantly?
"Fancy" indexing works for me (NB in your question you are trying to call the array object (round brackets) but NumPy "ndarray" objects are not callable):
In [61]: arr[mtxidx]
Out[61]:
array([[1, 5, 1],
[1, 5, 5],
[1, 1, 1]])
Your initial thought was pretty close, simply replacing the parenthesis with [] would make it work.
arr[mtxidx]
A list comprehension would work as well.
>>> np.array([arr[row] for row in mtxidx])
array([[1, 5, 1],
[1, 5, 5],
[1, 1, 1]])
I upvote the fancy indexing proposed by #xnx but if you would have done something in same range but involving an operation (or ..anything else) you can also try this :
arr = np.array([1,5])
mtxidx = np.array([[0,1,0],[0,1,1],[0,0,0]])
def func(v):
return arr[v]
vfunc = np.vectorize(func)
vfunc(mtxidx)
# array([[1, 5, 1],
# [1, 5, 5],
# [1, 1, 1]])
I have a long 1D array. I'd like to create an array that is the result of np.arange() applied to each value in the array plus some constant. E.g if the constant = 3 and my array looks like
[1,2,3,4,5]
I'd like to get
[[1,2,3]
[2,3,4]
[3,4,5]
[4,5,6]
[5,6,7]]
np.arange() only accepts scalars as arguments. I played around with np.vectorize() a bit to no success. Clearly I could do this with a loop, or with lists and then convert to an array, but I was wondering if there's a good numpy-only solution.
You could use addition and broadcasting:
>>> x = np.array([1,2,3,4,5])
>>> constant = 3
>>> x[:,None] + np.arange(constant)
array([[1, 2, 3],
[2, 3, 4],
[3, 4, 5],
[4, 5, 6],
[5, 6, 7]])
This could also be written as np.add.outer(x, np.arange(constant)).