I am very new to python. I want to clearly understand the below code, if there's anyone who can help me.
Code:
import numpy as np
arr = np.array([[1, 2, 3, 4,99,11,22], [5, 6, 7, 8,43,54,22]])
for x in np.nditer(arr[0:,::4]):
print(x)
My understanding:
This 2D array has two 1D arrays.
np.nditer(arr[0:,::4]) will give all value from 0 indexed array to upto last array, ::4 means the gap between printed arrays will be 4.
Question:
Is my understanding for no 2 above correct?
How can I get the index for the print(x)? Because of the step difference of 4 e.g [0:,::4] or any gap [0:,::x] I want to find out the exact index that it is printing. But how?
Addressing your questions below
Yes, I think your understanding is correct. It might help to first print what arr[0:,::4] returns though:
iter_array = arr[0:,::4]
print(iter_array)
>>> [[ 1 99]
>>> [ 5 43]]
The slicing takes out each 4th index of the original array. All nditer does is iterate through these values in order. (Quick FYI: arr[0:] and arr[:] are equivalent, since the starting point is 0 by default).
As you pointed out, to get the index for these you need to keep track of the slicing that you did, i.e. arr[0:, ::x]. Remember, nditer has nothing to do with how you sliced your array. I'm not sure how to best get the indices of your slicing, but this is what I came up with:
import numpy as np
ls = [
[1, 2, 3, 4,99,11,22],
[5, 6, 7, 8,43,54,22]
]
arr = np.array(ls)
inds = np.array([
[(ctr1, ctr2) for ctr2, _ in enumerate(l)] for ctr1, l in enumerate(ls)
]) # create duplicate of arr filled with zeros
step = 4
iter_array = arr[0:,::step]
iter_inds = inds[0:,::step]
print(iter_array)
>>> [[ 1 99]
>>> [ 5 43]]
print(iter_inds)
>>> [[[0 0]
>>> [0 4]]
>>>
>>> [[1 0]
>>> [1 4]]]
All that I added here was an inds array. This array has elements equal to their own index. Then, when you slice both arrays in the same way, you get your indices. Hopefully this helps!
Related
I have an array of numbers corresponding to indices of another array.
index_array = np.array([2, 3, 5])
What I want to do is to create another array with the numbers 0, 1, 4, 6, 7, 8, 9. What I have thought is:
index_list = []
for i in range(10):
if i not in index_array:
index_list.append(i)
This works but I don't know if there is a more efficient way to do it or even a built-in function for it.
Probably the simplest solution is just to remove unwanted indices from the set:
n = 10
index_array = [2, 3, 5]
complement = np.delete(np.arange(n), index_array)
You can use numpy.setdiff1d to efficiently collect the unique value from a "universal array" that aren't in your index array. Passing assume_unique=True provides a small speed up.
When assume_unique is True, the result will be sorted so long as the input is sorted.
import numpy as np
# "Universal set" to take complement with respect to.
universe = np.arange(10)
a = np.array([2,3,5])
complement = np.setdiff1d(universe, a, assume_unique=True)
print(complement)
Results in
[0 1 4 6 7 8 9]
In a given numpy array X:
X = array([1,2,3,4,5,6,7,8,9,10])
I would like to replace indices (2, 3) and (7, 8) with a single element -1 respectively, like:
X = array([1,2,-1,5,6,7,-1,10])
In other words, I replaced values at indices (2, 3) and (7,8) of the original array with a singular value.
Question is: Is there a numpy-ish way (i.e. without for loops and usage of python lists) around it? Thanks.
Note: This is NOT equivalent of replacing a single element in-place with another. Its about replacing multiple values with a "singular" value. Thanks.
A solution using numpy.delete, similar to #pault, but more efficient as it uses pure numpy indexing. However, because of this efficient indexing, it means that you cannot pass jagged arrays as indices
Setup
a = np.array([1,2,3,4,5,6,7,8,9,10])
idx = np.stack([[2, 3], [7, 8]])
a[idx] = -1
np.delete(a, idx[:, 1:])
array([ 1, 2, -1, 5, 6, 7, -1, 10])
I'm not sure if this can be done in one step, but here's a way using np.delete:
import numpy as np
from operator import itemgetter
X = np.array(range(1,11))
to_replace = [[2,3], [7,8]]
X[list(map(itemgetter(0), to_replace))] = -1
X = np.delete(X, list(map(lambda x: x[1:], to_replace)))
print(X)
#[ 1 2 -1 5 6 7 -1 10]
First we replace the first element of each pair with -1. Then we delete the remaining elements.
Try np.put:
np.put(X, [2,3,7,8], [-1,0]) # `0` can be changed to anything that's not in the array
print(X[X!=0]) # whatever You put as an number in `put`
So basically use put to do the values for the indexes, then drop the zero-values.
Or as #khan says, can do something that's out of range:
np.put(X, [2,3,7,8], [-1,np.max(X)+1])
print(X[X!=X.max()])
All Output:
[ 1 2 -1 5 6 7 -1 10]
I am working through some code trying to understand some Python mechanics, which I just do not get. I guess it is pretty simple and I also now, what it does, but i do not know how it works. I understand the normal use of for-loops but this here... I do not know.
Remark: I know some Python, but I am not an expert.
np.array([[[S[i,j]] for i in range(order+1)] for j in range(order+1)])
The second piece of code, I have problems with is this one:
for i in range(len(u)):
for j in range(len(v)):
tmp+=[rm[i,j][k]*someFuction(name,u[i],v[j])[k] for k in range(len(rm[i,j])) if rm[i,j][k]]
How does the innermost for-loop work? And also what does the if do here?
Thank you for your help.
EDIT: Sorry that the code is so unreadable, I just try to understand it myself. S, rm are numpy matrices, someFunction returns an array with scalar entries, andtmp is just a help variable
There are quite a few different concepts inside your code. Let's start with the most basic ones. Python lists and numpy arrays have different methodologies for indexation. Also you can build a numpy array by providing it a list:
S_list = [[1,2,3], [4,5,6], [7,8,9]]
S_array = np.array(S_list)
print(S_list)
print(S_array)
print(S_list[0][2]) # indexing element 2 from list 0
print(S_array[0,2]) # indexing element at position 0,2 of 2-dimensional array
This results in:
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
[[1 2 3]
[4 5 6]
[7 8 9]]
3
3
So for your first line of code:
np.array([[[S[i,j]] for i in range(order+1)] for j in range(order+1)])
You are building a numpy array by providing it a list. This list is being built with the concept of list comprehension. So the code inside the np.array(...) method:
[[[S[i,j]] for i in range(order+1)] for j in range(order+1)]
... is equivalent to:
order = 2
full_list = []
for j in range(order+1):
local_list = []
for i in range(order+1):
local_list.append(S_array[i, j])
full_list.append(local_list)
print(full_list)
This results in:
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]
As for your second snippet its important to notice that although typically numpy arrays have very specific and constant (for all the array) cell types you can actually give the data type object to a numpy array. So creating a 2-dimensional array of lists is possible. It is also possible to create a 3-dimensional array. Both are compatible with the indexation rm[i,j][k]. You can check this in the following example:
rm = np.array(["A", 3, [1,2,3]], dtype="object")
print(rm, rm[2][0]) # Acessing element 0 of list at position 2 of the array
rm2 = np.zeros((3, 3, 3))
print(rm2[0, 1][2]) # This is also valid
The following code:
[rm[i,j][k]*someFuction(name,u[i],v[j])[k] for k in range(len(rm[i,j])) if rm[i,j][k]]
... could be written as such:
some_list = []
for k in range(len(rm[i,j])):
if rm[i, j][k]: # Expecting a boolean value (or comparable)
a_list = rm[i,j][k]*someFuction(name,u[i],v[j])
some_list.append(a_list[k])
The final detail is the tmp+=some_list. When you sum two list they'll be concatenated as can been seen in this simple example:
tmp = []
tmp += [1, 2, 3]
print(tmp)
tmp += [4, 5, 6]
print(tmp)
Which results in this:
[1, 2, 3]
[1, 2, 3, 4, 5, 6]
Also notice that multiplying a list by a number will effectively be the same as summing the list several times. So 2*[1,2] will result in [1,2,1,2].
Its a list comprehension, albeit a pretty unreadable one. That was someome doing something very 'pythonic' in spite of readablity. Just look up list comprehensions and try to rewrite it yourself as a traditional for loop. list comprehensions are very useful, not sure I would have gone that route here.
The syntax for a list comprehension is
[var for var in iterable if optional condition]
So this bottom line can be rewritten like so:
for k in range(len(rm[i,j]):
if rm[i,j][k]:
tmp+= rm[i,j][k]*someFunction(name,u[i],v[j])[k]
I have a rather basic question about the NumPy module in Python 2, particularly the version on trinket.io. I do not see how to replace values in a multidimensional array several layers in, regardless of the method. Here is an example:
a = numpy.array([1,2,3])
a[0] = 0
print a
a = numpy.array([[1,2,3],[1,2,3]])
a[0][0] = a[1][0] = 0
print a
Result:
array([0, 2, 3], '<class 'int'>')
array([[1, 2, 3], [1, 2, 3]], '<class 'int'>')
I need the ability to change individual values, my specific code being:
a = numpy.empty(shape = (8,8,2),dtype = str)
for row in range(a.shape[0]):
for column in range(a.shape[1]):
a[row][column][1] = 'a'
Thank you for your time and any help provided.
To change individual values you can simply do something like:
a[1,2] = 'b'
If you want to change all the array, you can do:
a[:,:] = 'c'
Use commas (array[a,b]) instead of (array[a][b])
With numpy version 1.11.0, I get
[[0 2 3]
[0 2 3]]
When I run your code. I guess your numpy version is newer and better.
As user3408085 said, the correct thing is to go a[0,0] = 0 to change one element or a[:,0]=0 if your actually want to zero the entire first column.
The reason a[0][0]=0 does not modify a (at least in your version of numpy) is that a[0] is a new array. If break down your command a[0][0]=0 into 2 lines:
b=a[0]
b[0]=0
Then the fact that this modifies a is counterintuitive.
I am trying to use array slicing to reverse part of a NumPy array. If my array is, for example,
a = np.array([1,2,3,4,5,6])
then I can get a slice b
b = a[::-1]
Which is a view on the original array. What I would like is a view that is partially reversed, for example
1,4,3,2,5,6
I have encountered performance problems with NumPy if you don't play along exactly with how it is designed, so I would like to avoid "fancy" indexing if it is possible.
If you don't like the off by one indices
>>> a = np.array([1,2,3,4,5,6])
>>> a[1:4] = a[1:4][::-1]
>>> a
array([1, 4, 3, 2, 5, 6])
>>> a = np.array([1,2,3,4,5,6])
>>> a[1:4] = a[3:0:-1]
>>> a
array([1, 4, 3, 2, 5, 6])
You can use the permutation matrices (that's the numpiest way to partially reverse an array).
a = np.array([1,2,3,4,5,6])
new_order_for_index = [1,4,3,2,5,6] # Careful: index from 1 to n !
# Permutation matrix
m = np.zeros( (len(a),len(a)) )
for index , new_index in enumerate(new_order_for_index ):
m[index ,new_index -1] = 1
print np.dot(m,a)
# np.array([1,4,3,2,5,6])