Hi I want to use the values in another array using a list of tuples as the index's
Code :
import numpy as np
elevation_array = np.random.rand(5,5) #creates a random array 5 by 5
sort_idx = np.argsort(elevation_array, axis=None)
new_idx = zip(*np.unravel_index(sort_idx[::-1], elevation_array.shape))
for r, c in new_idx:
r, c = [r, c]
for [x, y], elevation in np.ndenumerate(elevation_array):
print elevation[r, c] # I will want to for other stuff here later on
I have also tried it this way:
for r, c in new_idx:
r, c = [r, c]
for elevation in np.ndenumerate(elevation_array):
print elevation[r, c]
I get the error in the first of:
IndexError: 0-d arrays can only use a single () or a list of newaxes (and a single ...) as an index
Any help would be great and explaination would be really useful as I am new to python
In the 2nd I get the error:
tuple indices must be integers, not tuple
ANSWER:
for r, c in new_idx:
print elevation_array[r, c]
I got it so simple I can't believe I did not know how to do that! :)
You can get the same result doing:
print np.sort(elevation_array.flat)[::-1]
Related
I am trying to generate an array that is the sum of two previous arrays. e.g
c = [A + B for A in a and B in b]
Here, get the error message
NameError: name 'B' is not defined
where
len(a) = len(b) = len(c)
Please can you let me know what I am doing wrong. Thanks.
The boolean and operator does not wire iterables together, it evaluates the truthiness (or falsiness) of its two operands.
What you're looking for is zip:
c = [A + B for A, B in zip(a, b)]
Items from the two iterables are successively assigned to A to B until one of the two is exhausted. B is now defined!
It should be
c = [A + B for A in a for B in b]
for instead of and. You might want to consider using numpy, where you can add 2 matrices directly, and more efficient.
'for' does not work the way you want it to work.
You could use zip().
A = [1,2,3]
B = [4,5,6]
c = [ a + b for a,b in zip(A,B)]
zip iterates through A & B and produces tuples.
To see what this looks like try:
[ x for x in zip(A,B)]
Suppose I have three lists where one contains NaN's (I think they're 'NaNs', they get printed as '--' from a previous masked array operation):
a = [1,2,3,4,5]
b = [6,7,--,9,--]
c = [6,7,8,9,10]
I'd like to perform an operation that iterates through b, and deletes the indexes from all lists where b[i]=NaN. I'm thinking something like this:
for i in range(0,len(b):
if b[i] = NaN:
del.a[i] etc
b is generated from from masking c under some condition earlier on in my code, something like this:
b = np.ma.MaskedArray(c, condition)
Thanks!
This is easy to do using numpy:
import numpy as np
a = np.array([1,2,3,4,5])
b = np.array([6,7,np.NaN,9,np.NaN])
c = np.array([6,7,8,9,10])
where_are_nans = np.isnan(b)
filtered_array = a[~where_are_nans] #note the ~ negation
print(filtered_array)
And as you can easily see it returns:
[1 2 4]
I have two arrays
A=np.array([[2,0],
[3,4],
[5,6]])
and
B=np.array([[4,3],
[6,7],
[3,4],
[2,0]])
I want to essentially subtract B from A and obtain indices of elements in A which are present in B. How do I accomplish this? In this example, I need answers like:
C=[0,1] //index in A of elements repeated in B
D=[[2,0], [3,4]] //their value
E=[3,2] //index location of these in B
Several usual commands like nonzero, remove, filter, etc seem unusable for ND arrays. Can someone please help me out?
You can define a data type that will be the concatenation of your columns, allowing you to use the 1d set operations:
a = np.ascontiguousarray(A).view(np.dtype((np.void, A.shape[1]*min(A.strides))))
b = np.ascontiguousarray(B).view(np.dtype((np.void, B.shape[1]*min(B.strides))))
check = np.in1d(a, b)
C = np.where(check)[0]
D = A[check]
check = np.in1d(b, a)
E = np.where(check)[0]
If you wanted only D, for example, you could have done:
D = np.intersect1d(a, b).view(A.dtype).reshape(-1, A.shape[1])
note in the last example how the original dtype can be recovered.
If I have two numpy arrays and want to find the the non-intersecting values, how do I do it?
Here's a short example of what I can't figure out.
a = ['Brian', 'Steve', 'Andrew', 'Craig']
b = ['Andrew','Steve']
I want to find the non-intersecting values. In this case I want my output to be:
['Brian','Craig']
The opposite of what I want is done with this:
c=np.intersect1d(a,b)
which returns
['Andrew' 'Steve']
You can use setxor1d. According to the documentation:
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the input arrays.
Usage is as follows:
import numpy
a = ['Brian', 'Steve', 'Andrew', 'Craig']
b = ['Andrew','Steve']
c = numpy.setxor1d(a, b)
Executing this will result in c having a value of array(['Brian', 'Craig']).
Given that none of the objects shown in your question are Numpy arrays, you don't need Numpy to achieve this:
c = list(set(a).symmetric_difference(b))
If you have to have a Numpy array as the output, it's trivial to create one:
c = np.array(set(a).symmetric_difference(b))
(This assumes that the order in which elements appear in c does not matter. If it does, you need to state what the expected order is.)
P.S. There is also a pure Numpy solution, but personally I find it hard to read:
c = np.setdiff1d(np.union1d(a, b), np.intersect1d(a, b))
np.setdiff1d(a,b)
This will return non intersecting value of first argument with second argument
Example:
a = [1,2,3]
b = [1,3]
np.setdiff1d(a,b) -> returns [2]
np.setdiff1d(b,a) -> returns []
This should do it for python arrays
c=[x for x in a if x not in b]+[x for x in b if x not in a]
It first collects all the elements from a that are not in b and then adds all those elements from b that are not in a. This way you get all elements that are in a or b, but not in both.
import numpy as np
a = np.array(['Brian', 'Steve', 'Andrew', 'Craig'])
b = np.array(['Andrew','Steve'])
you can use
set(a) - set(b)
Output:
set(['Brian', 'Craig'])
Note: set operation returns unique values
i have array of arrays this way.
I wanna sum a specific fild (like 3rd in the list )
data = [[d, 408.56087701, 87.26907024],
[b, 277.95015117, 75.19386881],
[b, 385.41416264, 84.73488504],
[b, 380.31630662, 71.23504808],
[b, 392.10729207, 83.80720357],
[b, 399.70877373, 76.59640833],
[b, 350.93124656, 79.34979059],
[b, 330.09702335, 79.37166555]]
I am trying this, but it produces problem as I have to select only 3rd in the list (first field is string)
data = [sum(x) for x in zip(*data)]
I have to add condition to show that x is third in sub list.
the_sum = sum(x[2] for x in data)
Or if you're wondering why you thought zip(*...) seemed like a good idea in the first place:
the_sum = sum(zip(*data)[2])
Although that's more wasteful of memory