Related
I have created a class with attributes and sorted them based on their level of x, from 1-6. I then want to sort the list into pairs, where the objects with the highest level of "x" and the object with the lowest level of "x" are paired together, and the second most and second less and so on. If it was my way it would look like this, even though objects are not itereable.
for objects in sortedlist:
i = 0
row(i) = [[sortedlist[i], list[-(i)-1]]
i += 1
if i => len(sortedlist)
break
Using zip
I think the code you want is:
rows = list(zip(sortedList, reversed(sortedList)))
However, note that this would "duplicate" the elements:
>>> sortedList = [1, 2, 3, 4, 5]
>>> list(zip(sortedList, reversed(sortedList)))
[(1, 5), (2, 4), (3, 3), (4, 2), (5, 1)]
If you know that the list has an even number of elements and want to avoid duplicates, you can instead write:
rows = list(zip(sortedList[:len(sortedList)//2], reversed(sortedList[len(sortedList)//2:])))
With the following result:
>>> sortedList = [1,2,3,4,5,6]
>>> list(zip(sortedList[:len(sortedList)//2], reversed(sortedList[len(sortedList)//2:])))
[(1, 6), (2, 5), (3, 4)]
Using loops
Although I recommend using zip rather than a for-loop, here is how to fix the loop you wrote:
rows = []
for i in range(len(sortedList)):
rows.append((sortedList[i], sortedList[-i-1]))
With result:
>>> sortedList=[1,2,3,4,5]
>>> rows = []
>>> for i in range(len(sortedList)):
... rows.append((sortedList[i], sortedList[-i-1]))
...
>>> rows
[(1, 5), (2, 4), (3, 3), (4, 2), (5, 1)]
So the problem is essentially this: I have a list of tuples made up of n ints that have to be eliminated if they dont fit certain criteria. This criterion boils down to that each element of the tuple must be equal to or less than the corresponding int of another list (lets call this list f) in the exact position.
So, an example:
Assuming I have a list of tuples called wk, made up of tuples of ints of length 3, and a list f composed of 3 ints. Like so:
wk = [(1,3,8),(8,9,1),(1,1,1)]
f = [2,5,8]
=== After applying the function ===
wk_result = [(1,3,8),(1,1,1)]
The rationale would be that when looking at the first tuple of wk ((1,3,8)), the first element of it is smaller than the first element of f. The second element of wk also complies with the rule, and the same applies for the third. This does not apply for the second tuple tho given that the first and second element (8 and 9) are bigger than the first and second elements of f (2 and 5).
Here's the code I have:
for i,z in enumerate(wk):
for j,k in enumerate(z):
if k <= f[j]:
pass
else:
del wk[i]
When I run this it is not eliminating the tuples from wk. What could I be doing wrong?
EDIT
One of the answers provided by user #James actually made it a whole lot simpler to do what I need to do:
[t for t in wk if t<=tuple(f)]
#returns:
[(1, 3, 8), (1, 1, 1)]
The thing is in my particular case it is not getting the job done, so I assume it might have to do with the previous steps of the process which I will post below:
max_i = max(f)
siz = len(f)
flist = [i for i in range(1,max_i +1)]
def cartesian_power(seq, p):
if p == 0:
return [()]
else:
result = []
for x1 in seq:
for x2 in cartesian_power(seq, p - 1):
result.append((x1,) + x2)
return result
wk = cartesian_power(flist, siz)
wk = [i for i in wk if i <= tuple(f) and max(i) == max_i]
What is happening is the following: I cannot use the itertools library to do permutations, that is why I am using a function that gets the job done. Once I produce a list of tuples (wk) with all possible permutations, I filter this list using two parameters: the one that brought me here originally and another one not relevant for the discussion.
Ill show an example of the results with numbers, given f = [2,5,8]:
[(1, 1, 8), (1, 2, 8), (1, 3, 8), (1, 4, 8), (1, 5, 8), (1, 6, 8), (1, 7, 8), (1, 8, 1), (1, 8, 2), (1, 8, 3), (1, 8, 4), (1, 8, 5), (1, 8, 6), (1, 8, 7), (1, 8, 8), (2, 1, 8), (2, 2, 8), (2, 3, 8), (2, 4, 8), (2, 5, 8)]
As you can see, there are instances where the ints in the tuple are bigger than the corresponding position in the f list, like (1,6,8) where the second position of the tuple (6) is bigger than the number in the second position of f (5).
You can use list comprehension with a (short-circuiting) predicate over each tuple zipped with the list f.
wk = [(1, 3, 8), (8, 9, 1), (1, 1, 1), (1, 9, 1)]
f = [2, 5, 8] # In this contrived example, f could preferably be a 3-tuple as well.
filtered = [t for t in wk if all(a <= b for (a, b) in zip(t, f))]
print(filtered) # [(1, 3, 8), (1, 1, 1)]
Here, all() has been used to specify a predicate that all tuple members must be less or equal to the corresponding element in the list f; all() will short-circuit its testing of a tuple as soon as one of its members does not pass the tuple member/list member <= sub-predicate.
Note that I added a (1, 9, 1) tuple for an example where the first tuple element passes the sub-predicate (<= corresponding element in f) whereas the 2nd tuple element does not (9 > 5).
You can do this with a list comprehension. It iterates over the list of tuples and checks that all of the elements of the tuple are less than or equal to the corresponding elements in f. You can compare tuples directly for element-wise inequality
[t for t in wk if all(x<=y for x,y in zip(t,f)]
# returns:
[(1, 3, 8), (1, 1, 1)]
Here is without loop solution which will compare each element in tuple :
wk_1 = [(1,3,8),(8,9,1),(1,1,1)]
f = [2,5,8]
final_input=[]
def comparison(wk, target):
if not wk:
return 0
else:
data=wk[0]
if data[0]<=target[0] and data[1]<=target[1] and data[2]<=target[2]:
final_input.append(data)
comparison(wk[1:],target)
comparison(wk_1,f)
print(final_input)
output:
[(1, 3, 8), (1, 1, 1)]
P.S : since i don't know you want less and equal or only less condition so modify it according to your need.
Say I have a list of valid X = [1, 2, 3, 4, 5] and a list of valid Y = [1, 2, 3, 4, 5].
I need to generate all combinations of every element in X and every element in Y (in this case, 25) and get those combinations in random order.
This in itself would be simple, but there is an additional requirement: In this random order, there cannot be a repetition of the same x in succession. For example, this is okay:
[1, 3]
[2, 5]
[1, 2]
...
[1, 4]
This is not:
[1, 3]
[1, 2] <== the "1" cannot repeat, because there was already one before
[2, 5]
...
[1, 4]
Now, the least efficient idea would be to simply randomize the full set as long as there are no more repetitions. My approach was a bit different, repeatedly creating a shuffled variant of X, and a list of all Y * X, then picking a random next one from that. So far, I've come up with this:
import random
output = []
num_x = 5
num_y = 5
all_ys = list(xrange(1, num_y + 1)) * num_x
while True:
# end if no more are available
if len(output) == num_x * num_y:
break
xs = list(xrange(1, num_x + 1))
while len(xs):
next_x = random.choice(xs)
next_y = random.choice(all_ys)
if [next_x, next_y] not in output:
xs.remove(next_x)
all_ys.remove(next_y)
output.append([next_x, next_y])
print(sorted(output))
But I'm sure this can be done even more efficiently or in a more succinct way?
Also, my solution first goes through all X values before continuing with the full set again, which is not perfectly random. I can live with that for my particular application case.
A simple solution to ensure an average O(N*M) complexity:
def pseudorandom(M,N):
l=[(x+1,y+1) for x in range(N) for y in range(M)]
random.shuffle(l)
for i in range(M*N-1):
for j in range (i+1,M*N): # find a compatible ...
if l[i][0] != l[j][0]:
l[i+1],l[j] = l[j],l[i+1]
break
else: # or insert otherwise.
while True:
l[i],l[i-1] = l[i-1],l[i]
i-=1
if l[i][0] != l[i-1][0]: break
return l
Some tests:
In [354]: print(pseudorandom(5,5))
[(2, 2), (3, 1), (5, 1), (1, 1), (3, 2), (1, 2), (3, 5), (1, 5), (5, 4),\
(1, 3), (5, 2), (3, 4), (5, 3), (4, 5), (5, 5), (1, 4), (2, 5), (4, 4), (2, 4),\
(4, 2), (2, 1), (4, 3), (2, 3), (4, 1), (3, 3)]
In [355]: %timeit pseudorandom(100,100)
10 loops, best of 3: 41.3 ms per loop
Here is my solution. First the tuples are chosen among the ones who have a different x value from the previous selected tuple. But I ve noticed that you have to prepare the final trick for the case you have only bad value tuples to place at end.
import random
num_x = 5
num_y = 5
all_ys = range(1,num_y+1)*num_x
all_xs = sorted(range(1,num_x+1)*num_y)
output = []
last_x = -1
for i in range(0,num_x*num_y):
#get list of possible tuple to place
all_ind = range(0,len(all_xs))
all_ind_ok = [k for k in all_ind if all_xs[k]!=last_x]
ind = random.choice(all_ind_ok)
last_x = all_xs[ind]
output.append([all_xs.pop(ind),all_ys.pop(ind)])
if(all_xs.count(last_x)==len(all_xs)):#if only last_x tuples,
break
if len(all_xs)>0: # if there are still tuples they are randomly placed
nb_to_place = len(all_xs)
while(len(all_xs)>0):
place = random.randint(0,len(output)-1)
if output[place]==last_x:
continue
if place>0:
if output[place-1]==last_x:
continue
output.insert(place,[all_xs.pop(),all_ys.pop()])
print output
Here's a solution using NumPy
def generate_pairs(xs, ys):
n = len(xs)
m = len(ys)
indices = np.arange(n)
array = np.tile(ys, (n, 1))
[np.random.shuffle(array[i]) for i in range(n)]
counts = np.full_like(xs, m)
i = -1
for _ in range(n * m):
weights = np.array(counts, dtype=float)
if i != -1:
weights[i] = 0
weights /= np.sum(weights)
i = np.random.choice(indices, p=weights)
counts[i] -= 1
pair = xs[i], array[i, counts[i]]
yield pair
Here's a Jupyter notebook that explains how it works
Inside the loop, we have to copy the weights, add them up, and choose a random index using the weights. These are all linear in n. So the overall complexity to generate all pairs is O(n^2 m)
But the runtime is deterministic and overhead is low. And I'm fairly sure it generates all legal sequences with equal probability.
An interesting question! Here is my solution. It has the following properties:
If there is no valid solution it should detect this and let you know
The iteration is guaranteed to terminate so it should never get stuck in an infinite loop
Any possible solution is reachable with nonzero probability
I do not know the distribution of the output over all possible solutions, but I think it should be uniform because there is no obvious asymmetry inherent in the algorithm. I would be surprised and pleased to be shown otherwise, though!
import random
def random_without_repeats(xs, ys):
pairs = [[x,y] for x in xs for y in ys]
output = [[object()], [object()]]
seen = set()
while pairs:
# choose a random pair from the ones left
indices = list(set(xrange(len(pairs))) - seen)
try:
index = random.choice(indices)
except IndexError:
raise Exception('No valid solution exists!')
# the first element of our randomly chosen pair
x = pairs[index][0]
# search for a valid place in output where we slot it in
for i in xrange(len(output) - 1):
left, right = output[i], output[i+1]
if x != left[0] and x != right[0]:
output.insert(i+1, pairs.pop(index))
seen = set()
break
else:
# make sure we don't randomly choose a bad pair like that again
seen |= {i for i in indices if pairs[i][0] == x}
# trim off the sentinels
output = output[1:-1]
assert len(output) == len(xs) * len(ys)
assert not any(L==R for L,R in zip(output[:-1], output[1:]))
return output
nx, ny = 5, 5 # OP example
# nx, ny = 2, 10 # output must alternate in 1st index
# nx, ny = 4, 13 # shuffle 'deck of cards' with no repeating suit
# nx, ny = 1, 5 # should raise 'No valid solution exists!' exception
xs = range(1, nx+1)
ys = range(1, ny+1)
for pair in random_without_repeats(xs, ys):
print pair
This should do what you want.
rando will never generate the same X twice in a row, but I realized that it is possible (though seems unlikely, in that I never noticed it happen in the 10 or so times I ran without the extra check) that due to the potential discard of duplicate pairs it could happen upon a previous X. Oh! But I think I figured it out... will update my answer in a moment.
import random
X = [1,2,3,4,5]
Y = [1,2,3,4,5]
def rando(choice_one, choice_two):
last_x = random.choice(choice_one)
while True:
yield last_x, random.choice(choice_two)
possible_x = choice_one[:]
possible_x.remove(last_x)
last_x = random.choice(possible_x)
all_pairs = set(itertools.product(X, Y))
result = []
r = rando(X, Y)
while set(result) != all_pairs:
pair = next(r)
if pair not in result:
if result and result[-1][0] == pair[0]:
continue
result.append(pair)
import pprint
pprint.pprint(result)
For completeness, I guess I will throw in the super-naive "just keep shuffling till you get one" solution. It's not guaranteed to even terminate, but if it does, it will have a good degree of randomness, and you did say one of the desired qualities was succinctness, and this sure is succinct:
import itertools
import random
x = range(5) # this is a list in Python 2
y = range(5)
all_pairs = list(itertools.product(x, y))
s = list(all_pairs) # make a working copy
while any(s[i][0] == s[i + 1][0] for i in range(len(s) - 1)):
random.shuffle(s)
print s
As was commented, for small values of x and y (especially y!), this is actually a reasonably quick solution. Your example of 5 for each completes in an average time of "right away". The deck of cards example (4 and 13) can take much longer, because it will usually require hundreds of thousands of shuffles. (And again, is not guaranteed to terminate at all.)
Distribute the x values (5 times each value) evenly across your output:
import random
def random_combo_without_x_repeats(xvals, yvals):
# produce all valid combinations, but group by `x` and shuffle the `y`s
grouped = [[x, random.sample(yvals, len(yvals))] for x in xvals]
last_x = object() # sentinel not equal to anything
while grouped[0][1]: # still `y`s left
for _ in range(len(xvals)):
# shuffle the `x`s, but skip any ordering that would
# produce consecutive `x`s.
random.shuffle(grouped)
if grouped[0][0] != last_x:
break
else:
# we tried to reshuffle N times, but ended up with the same `x` value
# in the first position each time. This is pretty unlikely, but
# if this happens we bail out and just reverse the order. That is
# more than good enough.
grouped = grouped[::-1]
# yield a set of (x, y) pairs for each unique x
# Pick one y (from the pre-shuffled groups per x
for x, ys in grouped:
yield x, ys.pop()
last_x = x
This shuffles the y values per x first, then gives you a x, y combination for each x. The order in which the xs are yielded is shuffled each iteration, where you test for the restriction.
This is random, but you'll get all numbers between 1 and 5 in the x position before you'll see the same number again:
>>> list(random_combo_without_x_repeats(range(1, 6), range(1, 6)))
[(2, 1), (3, 2), (1, 5), (5, 1), (4, 1),
(2, 4), (3, 1), (4, 3), (5, 5), (1, 4),
(5, 2), (1, 1), (3, 3), (4, 4), (2, 5),
(3, 5), (2, 3), (4, 2), (1, 2), (5, 4),
(2, 2), (3, 4), (1, 3), (4, 5), (5, 3)]
(I manually grouped that into sets of 5). Overall, this makes for a pretty good random shuffling of a fixed input set with your restriction.
It is efficient too; because there is only a 1-in-N chance that you have to re-shuffle the x order, you should only see one reshuffle on average take place during a full run of the algorithm. The whole algorithm stays within O(N*M) boundaries therefor, pretty much ideal for something that produces N times M elements of output. Because we limit the reshuffling to N times at most before falling back to a simple reverse we avoid the (extremely unlikely) posibility of endlessly reshuffling.
The only drawback then is that it has to create N copies of the M y values up front.
Here is an evolutionary algorithm approach. It first evolves a list in which the elements of X are each repeated len(Y) times and then it randomly fills in each element of Y len(X) times. The resulting orders seem fairly random:
import random
#the following fitness function measures
#the number of times in which
#consecutive elements in a list
#are equal
def numRepeats(x):
n = len(x)
if n < 2: return 0
repeats = 0
for i in range(n-1):
if x[i] == x[i+1]: repeats += 1
return repeats
def mutate(xs):
#swaps random pairs of elements
#returns a new list
#one of the two indices is chosen so that
#it is in a repeated pair
#and swapped element is different
n = len(xs)
repeats = [i for i in range(n) if (i > 0 and xs[i] == xs[i-1]) or (i < n-1 and xs[i] == xs[i+1])]
i = random.choice(repeats)
j = random.randint(0,n-1)
while xs[j] == xs[i]: j = random.randint(0,n-1)
ys = xs[:]
ys[i], ys[j] = ys[j], ys[i]
return ys
def evolveShuffle(xs, popSize = 100, numGens = 100):
#tries to evolve a shuffle of xs so that consecutive
#elements are different
#takes the best 10% of each generation and mutates each 9
#times. Stops when a perfect solution is found
#popsize assumed to be a multiple of 10
population = []
for i in range(popSize):
deck = xs[:]
random.shuffle(deck)
fitness = numRepeats(deck)
if fitness == 0: return deck
population.append((fitness,deck))
for i in range(numGens):
population.sort(key = (lambda p: p[0]))
newPop = []
for i in range(popSize//10):
fit,deck = population[i]
newPop.append((fit,deck))
for j in range(9):
newDeck = mutate(deck)
fitness = numRepeats(newDeck)
if fitness == 0: return newDeck
newPop.append((fitness,newDeck))
population = newPop
#if you get here :
return [] #no special shuffle found
#the following function takes a list x
#with n distinct elements (n>1) and an integer k
#and returns a random list of length nk
#where consecutive elements are not the same
def specialShuffle(x,k):
n = len(x)
if n == 2:
if random.random() < 0.5:
a,b = x
else:
b,a = x
return [a,b]*k
else:
deck = x*k
return evolveShuffle(deck)
def randOrder(x,y):
xs = specialShuffle(x,len(y))
d = {}
for i in x:
ys = y[:]
random.shuffle(ys)
d[i] = iter(ys)
pairs = []
for i in xs:
pairs.append((i,next(d[i])))
return pairs
for example:
>>> randOrder([1,2,3,4,5],[1,2,3,4,5])
[(1, 4), (3, 1), (4, 5), (2, 2), (4, 3), (5, 3), (2, 1), (3, 3), (1, 1), (5, 2), (1, 3), (2, 5), (1, 5), (3, 5), (5, 5), (4, 4), (2, 3), (3, 2), (5, 4), (2, 4), (4, 2), (1, 2), (5, 1), (4, 1), (3, 4)]
As len(X) and len(Y) gets larger this has more difficulty finding a solution (and is designed to return the empty list in that eventuality), in which case the parameters popSize and numGens could be increased. As is, it is able to find 20x20 solutions very rapidly. It takes about a minute when X and Y are of size 100 but even then is able to find a solution (in the times that I have run it).
Interesting restriction! I probably overthought this, solving a more general problem: shuffling an arbitrary list of sequences such that (if possible) no two adjacent sequences share a first item.
from itertools import product
from random import choice, randrange, shuffle
def combine(*sequences):
return playlist(product(*sequences))
def playlist(sequence):
r'''Shuffle a set of sequences, avoiding repeated first elements.
'''#"""#'''
result = list(sequence)
length = len(result)
if length < 2:
# No rearrangement is possible.
return result
def swap(a, b):
if a != b:
result[a], result[b] = result[b], result[a]
swap(0, randrange(length))
for n in range(1, length):
previous = result[n-1][0]
choices = [x for x in range(n, length) if result[x][0] != previous]
if not choices:
# Trapped in a corner: Too many of the same item are left.
# Backtrack as far as necessary to interleave other items.
minor = 0
major = length - n
while n > 0:
n -= 1
if result[n][0] == previous:
major += 1
else:
minor += 1
if minor == major - 1:
if n == 0 or result[n-1][0] != previous:
break
else:
# The requirement can't be fulfilled,
# because there are too many of a single item.
shuffle(result)
break
# Interleave the majority item with the other items.
major = [item for item in result[n:] if item[0] == previous]
minor = [item for item in result[n:] if item[0] != previous]
shuffle(major)
shuffle(minor)
result[n] = major.pop(0)
n += 1
while n < length:
result[n] = minor.pop(0)
n += 1
result[n] = major.pop(0)
n += 1
break
swap(n, choice(choices))
return result
This starts out simple, but when it discovers that it can't find an item with a different first element, it figures out how far back it needs to go to interleave that element with something else. Therefore, the main loop traverses the array at most three times (once backwards), but usually just once. Granted, each iteration of the first forward pass checks each remaining item in the array, and the array itself contains every pair, so the overall run time is O((NM)**2).
For your specific problem:
>>> X = Y = [1, 2, 3, 4, 5]
>>> combine(X, Y)
[(3, 5), (1, 1), (4, 4), (1, 2), (3, 4),
(2, 3), (5, 4), (1, 5), (2, 4), (5, 5),
(4, 1), (2, 2), (1, 4), (4, 2), (5, 2),
(2, 1), (3, 3), (2, 5), (3, 2), (1, 3),
(4, 3), (5, 3), (4, 5), (5, 1), (3, 1)]
By the way, this compares x values by equality, not by position in the X array, which may make a difference if the array can contain duplicates. In fact, duplicate values might trigger the fallback case of shuffling all pairs together if more than half of the X values are the same.
In the following code, I created a list of lists, like an array. I began getting a "list assignment out of range" error. As a workaround for this error, I added 2 extra instances of diceSumTable, is you can see in the code. It will print now, but it is preceded by "2, 3". In my studying, I can't recall any reason why this would be happening if every instance of diceSumTable is already defined.
EDIT: Here was the original code, without applying the workaround.
def dice():
diceSumTable = [2,3,4,5,6,7,8,9,10,11,12]
diceSumTable[2] = [(1,1)]
diceSumTable[3] = [(1,2),(2,1)]
diceSumTable[4] = [(1,3),(2,2),(3,1)]
diceSumTable[5] = [(1,4),(2,3),(3,2),(4,1)]
diceSumTable[6] = [(1,5),(2,4),(3,3),(4,2),(5,1)]
diceSumTable[7] = [(1,6),(2,5),(3,4),(4,3),(5,2),(6,1)]
diceSumTable[8] = [(2,6),(3,5),(4,4),(5,3),(6,2)]
diceSumTable[9] = [(3,6),(4,5),(5,4),(6,3)]
diceSumTable[10] = [(4,6),(5,5),(6,4)]
diceSumTable[11] = [(5,6),(6,5)]
diceSumTable[12] = [(6,6)]
#for illustrative purposes
for i in diceSumTable:
print i
dice()
As said, you start indexing the diceSumTable from index #2 onward, leaving entries 0 and 1 untouched. The error you got was because you were indexing past the end of the array.
For your problem a "dict" might be a better solution:
diceSumTable = {}
diceSumTable[ 2 ] = [(1,1)]
diceSumTable[ 3 ] = [(1,2), (2,1)]
Try this:
def dice():
diceSumTable = [] # define diceSumTable as a list
diceSumTable.append((1,1)) # append the tuple to diceSumTable
diceSumTable.append(((1,2),(2,1))) # append a tuple of tuples to diceSumTable
diceSumTable.append(((1,3),(2,2),(3,1)))
diceSumTable.append(((1,4),(2,3),(3,2),(4,1)))
diceSumTable.append(((1,5),(2,4),(3,3),(4,2),(5,1)))
diceSumTable.append(((1,6),(2,5),(3,4),(4,3),(5,2),(6,1)))
diceSumTable.append(((2,6),(3,5),(4,4),(5,3),(6,2)))
diceSumTable.append(((3,6),(4,5),(5,4),(6,3)))
diceSumTable.append(((4,6),(5,5),(6,4)))
diceSumTable.append(((5,6),(6,5)))
diceSumTable.append(((6,6)))
#for illustrative purposes
for i in diceSumTable:
print i
dice()
You got 2, 3 because that's what you entered into the list in the first to places, the other numbers you put in got replaced with statements like diceSumTable[x] =.
You are entering data from 2 index (means third element in array).
diceSumTable = [2,3,4,5,6,7,8,9,10,11,12,13,14]
when you do
# Replacing third element
diceSumTable[2] = [(1,1)]
diceSumTable will be like
diceSumTable = [2,3,[(1,1)],5,6,7,8,9,10,11,12,13,14]
Slly it will replace all values.
You are confusing the value of an entry in a list with the index of a list.
diceSumTable[3] corresponds to the fourth entry in diceSumTable (since they are numbered from 0).
Your first line creates a list diceSumTable with 13 entries, numbered 0...12.
Your next set of lines fills in the 3d through 13th entries (and throws away what was there before!).
To do what you want, you have a few choices.
1/ You can do what you're doing, but ignore the first two entries. This is not very pythonic...
2/ You can create a length 11 list, holding the actual entries. In this case, the most efficient way to do it is
diceSumTable = [] ### empty list
diceSumTable.append([(1,1)])
diceSumTable.append([(1,2),(2,1)])
#### etc.
3/ You can use a dict. This is probably closest to what you want, although it's slightly inefficient in space and time, since you just want consecutive integer keys (but premature optimisation is the root of all evil):
diceSumTable = {} ### empty dict
diceSumTable[2] = [(1,1)]
diceSumTable[3] = [(1,2),(2,1)]
#### etc.
(Markdown question: is there any way to intersperse code within a bulleted or enumerated list?)
When you set diceSumTable[2] you are replacing the third value in the list (lists are zero indexed - first value is name[0]) not the value that currently holds 2.
So after the first call you have diceSumTable equal to [2,3,[(1,1)],5,6,...].
I think what you could do is, as mentioned elsewhere, use diceSumTable = [] then diceSumTable.append((1,1)) for each dice combination.
You could also use a dictionary.
diceSumTable = {}
diceSumTable[2] = [(1,1)]
diceSumTable[3] = [(1,2),(2,1)]
#etc
You could then access by value rather than position.
>>>diceSumValue[3]
[(1,2),(2,1)]
>>>
You should index the list by it's indices, not the values:
In [123]: lst = [3, 4, 5]
In [124]: lst[0] == 3 #item "3" is at index 0
Out[124]: True
In [125]: lst[3] #out of range
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-125-48d1ca706e8c> in <module>()
----> 1 lst[3] #out of range
IndexError: list index out of range
In [126]: lst[1] = 10 #if I want to change "4" in the list to "10"
In [127]: lst
Out[127]: [3, 10, 5]
I am not shure of what result do you except from this code, what i understood is that you want to code to print :
[(1, 1)]
[(1, 2), (2, 1)]
[(1, 3), (2, 2), (3, 1)]
[(1, 4), (2, 3), (3, 2), (4, 1)]
[(1, 5), (2, 4), (3, 3), (4, 2), (5, 1)]
[(1, 6), (2, 5), (3, 4), (4, 3), (5, 2), (6, 1)]
[(2, 6), (3, 5), (4, 4), (5, 3), (6, 2)]
[(3, 6), (4, 5), (5, 4), (6, 3)]
[(4, 6), (5, 5), (6, 4)]
[(5, 6), (6, 5)]
[(6, 6)]
In this case, i think the error you are making is believing that the following line :
diceSumTable = [2,3,4,5,6,7,8,9,10,11,12,13,14]
Give you a list where index start from 2 and finish at 14, which is wrong because in Python every list index start at 0, and there is no way you can change that. The line you give me, actually create a list where the first index is 0 and the last index is 12 (size of the list - 1). And you list is such that diceSumTable[0] is 2 diceSumTable[1] is 3, etc.
This lead you to two options, either accept that list start at index 0, and if you want to keep using the mapping you want (i guess there is a reason for that, you surely want to associate 2 with (1,1), 3 with (1,2), (2,1)) just use diceSumTable[theNumberOfYourMapping -2]. Or as say haavee, you can use dict for that. But in this case, when you will iterate over your dict, you won't have you value is the write order. If there is no gap between you're wanted index i will go with the map.
I think it will be great if you could explain use a bit more what you want to do, why do you want 2 and not 0 to be associated to [(1,1)]. Do you want to do something else with this lis t than printing it. To sum up here is the code, i would have written, if i understood what you wanted to do :
def dice():
diceSumTable = [[(1,1)],
[(1,2),(2,1)],
[(1,3),(2,2),(3,1)],
[(1,4),(2,3),(3,2),(4,1)],
[(1,5),(2,4),(3,3),(4,2),(5,1)],
[(1,6),(2,5),(3,4),(4,3),(5,2),(6,1)],
[(2,6),(3,5),(4,4),(5,3),(6,2)],
[(3,6),(4,5),(5,4),(6,3)],
[(4,6),(5,5),(6,4)],
[(5,6),(6,5)],
[(6,6)]]
#this is like for value in diceSumTable but i will iterate to 0,1..10 in more
for (i,value) in enumerate(diceSumTable):
print str(i+2) + " is associated to " + str(value)
dice()
Moreover if you want to know more about Python list, i could read :
http://effbot.org/zone/python-list.htm
And for Python dictionary :
http://www.pythonforbeginners.com/dictionary/dictionary-manipulation-in-pythonc
I have the following list of tuples:
a = [(1, 2), (2, 4), (3, 1), (4, 4), (5, 2), (6, 8), (7, -1)]
I would like to select the elements which second value in the tuple is increasing compared to the previous one. For example I would select (2, 4) because 4 is superior to 2, in the same manner I would select (4, 4) and (6, 8).
Can this be done in a more elegant way than a loop starting explicitly on the second element ?
To clarify, I want to select the tuples which second elements are superior to the second element of the prior tuple.
>>> [right for left, right in pairwise(a) if right[1] > left[1]]
[(2, 4), (4, 4), (6, 8)]
Where pairwise is an itertools recipe you can find here.
You can use a list comprehension to do this fairly easily:
a = [a[i] for i in range(1, len(a)) if a[i][1] > a[i-1][1]]
This uses range(1, len(a)) to start from the second element in the list, then compares the second value in each tuple with the second value from the preceding tuple to determine whether it should be in the new list.
Alternatively, you could use zip to generate pairs of neighbouring tuples:
a = [two for one, two in zip(a, a[1:]) if two[1] > one[1]]
You can use enumerate to derive indices and then make list comprehension:
a = [t[1] for t in enumerate(a[1:]) if t[1][1] > a[t[0]-1][1]]
You can use list comprehension
[i for i in a if (i[0] < i[1])]
Returns
[(1, 2), (2, 4), (6, 8)]
Edit: I was incorrect in my understanding of the question. The above code will return all tuples in which the second element is greater than the first. This is not the answer to the question OP asked.