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I'm not sure what the correct term is for the multiplication here but I need to multiply an element from List A for example by every element in List B and create a new list for the new elements, so that the total length of the new list is len(A)*len(B).
As an example
A = [1,3,5], B=[4,6,8]
I need to multiply the two together to get
C = [4,6,8,12,18,24,20,30,40]
I have researched this and I have found that itertools(product) have exactly what I needed, however it is for a specific number of lists and I need to generalise to any number of lists as requested by the user.
I don't have access to the full code right now but the code asks the user for some lists (can be any number of lists) and the lists can have any number of elements in the lists (but all lists contain the same number of elements). These lists are then stored in one big list.
For example (user input)
A = [2,5,8], B= [4,7,3]
The big list will be
C = [[2,5,8],[4,7,3]]
In this case there are two lists in the big list but in general it can be any number of lists.
Once the code has this I have
print([a*b for a,b in itertools.product(C[0],C[1])])
>> [8,14,6,20,35,15,32,56,24]
The output of this is exactly what I want, however in this case the code is written for exactly two lists and I need it generalised to n lists.
I've been thinking about creating a loop to somehow loop over it n times but so far I have not been successful in this. Since C could any of any length then the loop needs a way to know when it's reached the end of the list. I don't need it to compute the product with n lists at the same time
print([a0*a1*...*a(n-1) for a0,a1,...,a(n-1) in itertools.product(C[0],C[1],C[2],...C[n-1])])
The loop could multiply two lists at a time then use the result from that multiplication against the next list in C and so on until C[n-1].
I would appreciate any advice to see if I'm at least heading in the right direction.
p.s. I am using numpy and the lists are arrays.
You can pass variable number of arguments to itertools.product with *. * is the unpacking operator that unpacks the list and passes its values the values of list to the function as if they are separately passed.
import itertools
import math
A = [[1, 2], [3, 4], [5, 6]]
result = list(map(math.prod, itertools.product(*A)))
print(result)
Result:
[15, 18, 20, 24, 30, 36, 40, 48]
You can find many explanations on the internet about * operator. In short, if you call a function like f(*lst), it will be roughly equivalent to f(lst[0], lst[1], ..., lst[len(lst) - 1]). So, it will save you from the need to know the length of the list.
Edit: I just realized that math.prod is a 3.8+ feature. If you're running an older version of Python, you can replace it with its numpy equivalent, np.prod.
You could use a reduce function that is intended exactly for these types of operations, which is based on recursion and accumulation. I am providing you an example with a primitive function so you can better understand its functionality:
lists = [
[4, 6, 8],
[1, 3, 5]
]
def reduce(function, iterable, initializer=None):
it = iter(iterable)
if initializer is None:
value = next(it)
else:
value = initializer
for element in it:
value = function(value, element)
return value
def cmp(a, b):
for x in a:
for y in b:
yield x*y
summed = list(reduce(cmp, lists))
# OUTPUT
[4, 12, 20, 6, 18, 30, 8, 24, 40]
In case you need it sorted just make use of the sort() function.
I want to make a function in python which receives as arguments a matrix, a coordinate for the line and another coordinate for the column. For example: A matrix m=[[1,2,3], [4,5,6]] and the function will receive the arguments (m,0,0)
It should return 1 (Which is the number located in position 0,0 in the matrix).
Think of it as a list of lists rather than a "matrix", and the logic becomes more obvious. Matrix m has two elements: m[0] = [1, 2, 3] and m[1] = [4, 5, 6]. So accessing a single value from within those lists requires another index. For example, m[0][1] = 2.
def matrix(m, a, b):
return m[a][b] # element b from list a in list m
If you really want to use a matrix, consider numpy.
I have two numpy arrays a and b, with twenty million elements (float number). If the combination elements of those two arrays are the same, then we call it duplicate, which should be remove from the two arrays. For instance,
a = numpy.array([1,3,6,3,7,8,3,2,9,10,14,6])
b = numpy.array([2,4,15,4,7,9,2,2,0,11,4,15])
From those two arrays, we have a[2]&b[2] is the same as a[11]&b[11], then we call it duplicate element, which should be removed. The same as a[1]&b[1] vs a[3]&b[3]Although each array has duplicate elements itself, they are not treated as duplicate elements. So I want the returned arrays to be:
a = numpy.array([1,3,6,7,8,3,2,9,10,14])
b = numpy.array([2,4,15,7,9,2,2,0,11,4])
Anyone has the cleverest way to implement such reduction?
First you have to pack a and b to identify duplicates.
If values are positive integers (see the edit in other cases), this can be achieved by :
base=a.max()+1
c=a+base*b
Then just find unique values in c:
val,ind=np.unique(c,return_index=True)
and retrieve the associated values in a and b.
ind.sort()
print(a[ind])
print(b[ind])
for the disparition of the duplicate. (two here):
[ 1 3 6 7 8 3 2 9 10 14]
[ 2 4 15 7 9 2 2 0 11 4]
EDIT
regardless of datatype, the c array can be made as follow, packing data to bytes :
ab=ascontiguousarray(vstack((a,b)).T)
dtype = 'S'+str(2*a.itemsize)
c=ab.view(dtype=dtype)
This is done in one pass and without requiring any extra memory for the resulting arrays.
Pair up the elements at each index and iterate over them. Keep a track of which pairs have been seen so far and a counter of the index of the arrays. When a new pair has not been seen before, the index will increase by 1, effectively writing them back to their original place. However, for a duplicate pair you don't increase the index, effectively shifting every new pair one position to the left. At the end, keep the first indexth number of elements to shorten the arrays.
import itertools as it
def delete_duplicate_pairs(*arrays):
unique = set()
arrays = list(arrays)
n = range(len(arrays))
index = 0
for pair in it.izip(*arrays):
if pair not in unique:
unique.add(pair)
for i in n:
arrays[i][index] = pair[i]
index += 1
return [a[:index] for a in arrays]
If you are on Python 2, zip() creates the list of pairs up front. If you have a lot of elements in your arrays, it'll be more efficient to use itertools.izip() which will create the pairs as you request them. However, zip() in Python 3 behaves like that by default.
For your case,
>>> import numpy as np
>>> a = np.array([1,3,6,3,7,8,3,2,9,10,14,6])
>>> b = np.array([2,4,15,4,7,9,2,2,0,11,4,15])
>>> a, b = delete_duplicate_pairs(a, b)
>>> a
array([ 1, 3, 6, 7, 8, 3, 2, 9, 10, 14])
>>> b
array([ 2, 4, 15, 7, 9, 2, 2, 0, 11, 4])
Now, it all comes down to what values your arrays hold. If you have only the values 0-9, there are only 100 unique pairs and most elements will be duplicates, which saves you time. For 20 million elements for both a and b and containing values only between 0-9, the process completes in 6 seconds. For values between 0-999, it takes 12 seconds.
I was going through exam practice
someNumbers = [10, 5, 7, 3, 2]
result = sum(someNumbers[1:4])
How come the answer is 15?
Shouldnt sum means adding 5+7+3+2
how exactly do they work?
You are correct, sum does mean sum of the specified elements. However, [1:4] means elements at indices 1 to 3 (inclusive). The element at the index of 4 is not included.
Therefore, someNumbers[1:4] is actually just [5, 7, 3] which does sum to 15.
To get the last four elements in the list above, you would do either someNumbers[1:] or someNumbers[1:5]
def my_sort(array):
length_of_array = range(1, len(array))
for i in length_of_array:
value = array[i]
last_value = array[i-1]
if value<last_value:
array[i]=last_value
array[i-1]=value
my_sort(array)
return array
I know what the function does in general. Its a sorting alogarithm.... But i dont know how what each individual part/section does.
Well, I have to say that the best way to understand this is to experiment with it, learn what it is using, and, basically, learn Python. :)
However, I'll go through the lines one-by-one to help:
Define a function named my_sort that accepts one argument named array. The rest of the lines are contained in this function.
Create a range of numbers using range that spans from 1 inclusive to the length of array non-inclusive. Then, assign this range to the variable length_of_array.
Start a for-loop that iterates through the range defined in the preceding line. Furthermore, assign each number returned to the variable i. This for-loop encloses lines 4 through 9.
Create a variable value that is equal to the item returned by indexing array at position i.
Create a variable last_value that is equal to the item returned by indexing array at position i-1.
Test if value is less than last_value. If so, run lines 7 through 9.
Make the i index of array equal last_value.
Make the i-1 index of array equal value.
Rerun my_sort recursively, passing in the argument array.
Return array for this iteration of the recursive function.
When array is finally sorted, the recursion will end and you will be left with array all nice and sorted.
I hope this shed some light on the subject!
I'll see what I can do for you. The code, for reference:
def my_sort(array):
length_of_array = range(1, len(array))
for i in length_of_array:
value = array[i]
last_value = array[i-1]
if value<last_value:
array[i]=last_value
array[i-1]=value
my_sort(array)
return array
def my_sort(array):
A function that takes an array as an argument.
length_of_array = range(1, len(array))
We set the variable length_of_array to a range of numbers that we can iterate over, based on the number of items in array. I assume you know what range does, but if you don't, in short you can iterate over it in the same way you'd iterate over a list. (You could also use xrange() here.)
for i in length_of_array:
value = array[i]
last_value = array[-1]
What we're doing is using the range to indirectly traverse the array because there's the same total of items in each. If we look closely, though, value uses the i as its index, which starts off at 1, so value is actually array[1], and last_value is array[1-1] or array[0].
if value<last_value:
array[i]=last_value
array[i-1]=value
So now we're comparing the values. Let's say we passed in [3, 1, 3, 2, 6, 4]. We're at the first iteration of the loop, so we're essentially saying, if array[1], which is 1, is less than array[0], which is 3, swap them. Of course 1 is less than 3, so swap them we do. But since the code can only compare each item to the previous item, there's no guarantee that array will be properly sorted from lowest to highest. Each iteration could unswap a properly swapped item if the item following it is larger (e.g. [2,5,6,4] will remain the same on the first two iterations -- they will be skipped over by the if test -- but when it hits the third, 6 will swap with 4, which is still wrong). In fact, if we were to finish this out without the call to my_sort(array) directly below it, our original array would evaluate to [1, 3, 2, 3, 4, 6]. Not quite right.
my_sort(array)
So we call my_sort() recursively. What we're basically saying is, if on the first iteration something is wrong, correct it, then pass the new array back to my_sort(). This sounds weird at first, but it works. If the if test was never satisfied at all, that would mean each item in our original list was smaller than the next, which is another way (the computer's way, really) of saying it was sorted in ascending order to begin with. That's the key. So if any list item is smaller than the preceding item, we jerk it one index left. But we don't really know if that's correct -- maybe it needs to go further still. So we have to go back to the beginning and (i.e., call my_sort() again on our newly-minted list), and recheck to see if we should pull it left again. If we can't, the if test fails (each item is smaller than the next) until it hits the next error. On each iteration, this teases the same smaller number leftward by one index until it's in its correct position. This sounds more confusing than it is, so let's just look at the output for each iteration:
[3, 1, 3, 2, 6, 4]
[1, 3, 3, 2, 6, 4]
[1, 3, 2, 3, 6, 4]
[1, 2, 3, 3, 6, 4]
[1, 2, 3, 3, 4, 6]
Are you seeing what's going on? How about if we only look at what's changing on each iteration:
[3, 1, ... # Wrong; swap. Further work ceases; recur (return to beginning with a fresh call to my_sort()).
[1, 3, 3, 2, ... # Wrong; swap. Further work ceases; recur
[1, 3, 2, ... # Wrong; swap. Further work ceases; recur
[1, 2, 3, 3, 6, 4 # Wrong; swap. Further work ceases; recur
[1, 2, 3, 3, 4, 6] # All numbers all smaller than following number; correct.
This allows the function to call itself as many times as it needs to pull a number from the back to the front. Again, each time it's called, it focuses on the first wrong instance, pulling it one left until it puts it in its proper position. Hope that helps! Let me know if you're still having trouble.