I have a list as
mylist = [1, 2, 3, 4, 5, 6, 7]
I want to find the neighboring elements of any element in the list. E.g. if I select 5, I should get 4 and 6. If I select 7, I should get 6 and 1.
I found itertools.cycle as something that can help but not sure how to proceed.
Edit:
The list will be ordered or I can order it using nump sort. But there will never be repeating elements.
Related
I was not able to find a duplicate of my question, unfortunately, although I am sure that this is a problem which has been solved before
I have a numpy array with a certain set of indices, eg.
ind1 = np.array([1, 3, 5, 7])
With these indices, I can filter some values from another array. Lets call this other array rows. As an example, I can retrieve
rows[ind1] = [1, 10, 20, 15]
The order of rows[ind1] must not be changed in the following.
I have another index array, ind2
ind2 = np.array([4, 5, 6, 7])
I also have an array cols, where I can filter values from using ind2. I know that cols[ind2] results in an array which has the size of rows[ind1] and the entries are the same, but the order is different. An example:
cols[ind2] = [15, 20, 10, 1]
I would like to rearrange the order of cols[ind2], so that it corresponds to rows[ind1]. I am interested in the corresponding order of ind2.
In the example, the result should be
cols[ind2] = [1, 10, 20, 15]
ind2 = [7, 6, 5, 4]
Using numpy, I did not find a way to do this. Any ideas would be helpful. Thanks in advance.
There may be a better way, but you can do this using argsorts.
Let's call your "reordered ind2" ind3.
If you are sure that rows[ind1] and cols[ind2] will have the same length and all of the same elements, then the sorted versions of both will be the same i.e np.sort(rows[ind1]) = np.sort(cols[ind2]).
If this is the case, and you don't run into any problems with repeated elements (unsure of your exact use case), then what you can do is find the indices to put cols[ind2] in order, and then from there, find the indices to put np.sort(cols[ind2]) into the order of rows[ind1].
So, if
p1 = np.argsort(rows[ind1])
and
p2 = np.argsort(cols[ind2])
and
p3 = np.argsort(p1)
Then
ind3 = ind2[p2][p3]. The reason this works is because if you do an argsort of an argsort, it gives you the indices you need to reverse the first sort. p2 sorts cols[ind2] (that's the definition of argsort), and p3 unsorts the result of that back into the order of rows[ind1].
Using numpy, given a sorted 1D array, how to efficiently obtain a 1D array with equal size where the value at each position is the number of preceding equal elements? I have very large arrays and processing each element in Python code one way or another is not acceptable.
Example:
input = [0, 0, 4, 4, 4, 5, 5, 5, 5, 6]
output = [0, 1, 0, 1, 2, 0, 1, 2, 3, 0]
import numpy as np
A=np.array([0, 0, 4, 4, 4, 5, 5, 5, 5, 6])
uni,counts=np.unique(A, return_counts=True)
out=np.concatenate([np.arange(n) for n in counts])
print(out)
Not certain about the efficiency (probably better way to form the out array rather than concatenating), but a very straightforward way to get the result you are looking for. Counts the unique elements, then does np.arange on each count to get the ascending sequence, then concatenates these arrays together.
In my table, each row can hold an integer let's call this integer column height.
for example, the values in this column could be: [5, 4, 0, 1, 9]
I need for this sequence in the example to be rearranged to become: [0, 1, 4, 5, 9] and then I want these values to become like this: [1, 2, 3, 4, 5].
The idea I am trying to write in SQL is to get the minimum at the first time and count how many values are there less than it.
How can I write/translate this idea into an SQL Query?
You seem to want a ranking:
select t.*, row_number() over (order by height) as height_seqnum
from t
order by height_seqnum;
a = [1,2,3,4,5]
for i in a:
list1.append(i)
list1.append(i-2) `i-2` is not functioning why?
Like for example, I am now in the index of element 4
i is not an index. It is the element present in the list itself. When you say you are now in the index of element 4, you are actually having element 4 not the index. So, you cannot treat like an index.
Python For loop is an interator based loop. It is used to step through items in lists, strings etc
The code:
a = [1,2,3,4,5]
list1 = []
for i in a:
print(i)
list1.append(i)
list1.append(i-2)
print list1
will produce the below output:
1
2
3
4
5
[1, -1, 2, 0, 3, 1, 4, 2, 5, 3]
I have two 1D-arrays containing the same set of values, but in a different (random) order. I want to find the list of indices, which reorders one array according to the other one. For example, my 2 arrays are:
ref = numpy.array([5,3,1,2,3,4])
new = numpy.array([3,2,4,5,3,1])
and I want the list order for which new[order] == ref.
My current idea is:
def find(val):
return numpy.argmin(numpy.absolute(ref-val))
order = sorted(range(new.size), key=lambda x:find(new[x]))
However, this only works as long as no values are repeated. In my example 3 appears twice, and I get new[order] = [5 3 3 1 2 4]. The second 3 is placed directly after the first one, because my function val() does not track which 3 I am currently looking for.
So I could add something to deal with this, but I have a feeling there might be a better solution out there. Maybe in some library (NumPy or SciPy)?
Edit about the duplicate: This linked solution assumes that the arrays are ordered, or for the "unordered" solution, returns duplicate indices. I need each index to appear only once in order. Which one comes first however, is not important (neither possible based on the data provided).
What I get with sort_idx = A.argsort(); order = sort_idx[np.searchsorted(A,B,sorter = sort_idx)] is: [3, 0, 5, 1, 0, 2]. But what I am looking for is [3, 0, 5, 1, 4, 2].
Given ref, new which are shuffled versions of each other, we can get the unique indices that map ref to new using the sorted version of both arrays and the invertibility of np.argsort.
Start with:
i = np.argsort(ref)
j = np.argsort(new)
Now ref[i] and new[j] both give the sorted version of the arrays, which is the same for both. You can invert the first sort by doing:
k = np.argsort(i)
Now ref is just new[j][k], or new[j[k]]. Since all the operations are shuffles using unique indices, the final index j[k] is unique as well. j[k] can be computed in one step with
order = np.argsort(new)[np.argsort(np.argsort(ref))]
From your original example:
>>> ref = np.array([5, 3, 1, 2, 3, 4])
>>> new = np.array([3, 2, 4, 5, 3, 1])
>>> np.argsort(new)[np.argsort(np.argsort(ref))]
>>> order
array([3, 0, 5, 1, 4, 2])
>>> new[order] # Should give ref
array([5, 3, 1, 2, 3, 4])
This is probably not any faster than the more general solutions to the similar question on SO, but it does guarantee unique indices as you requested. A further optimization would be to to replace np.argsort(i) with something like the argsort_unique function in this answer. I would go one step further and just compute the inverse of the sort:
def inverse_argsort(a):
fwd = np.argsort(a)
inv = np.empty_like(fwd)
inv[fwd] = np.arange(fwd.size)
return inv
order = np.argsort(new)[inverse_argsort(ref)]