Develop Reference

Algorithm not passing tests even when I get correct results on my end

The question is mostly about base conversion. Here's the question.
Start with a random minion ID n, which is a nonnegative integer of length k in base b
Define x and y as integers of length k. x has the digits of n in descending order, and y has the digits of n in ascending order
Define z = x - y. Add leading zeros to z to maintain length k if necessary
Assign n = z to get the next minion ID, and go back to step 2
For example, given minion ID n = 1211, k = 4, b = 10, then x = 2111, y = 1112 and z = 2111 - 1112 = 0999. Then the next minion ID will be n = 0999 and the algorithm iterates again: x = 9990, y = 0999 and z = 9990 - 0999 = 8991, and so on.
Depending on the values of n, k (derived from n), and b, at some point the algorithm reaches a cycle, such as by reaching a constant value. For example, starting with n = 210022, k = 6, b = 3, the algorithm will reach the cycle of values [210111, 122221, 102212] and it will stay in this cycle no matter how many times it continues iterating. Starting with n = 1211, the routine will reach the integer 6174, and since 7641 - 1467 is 6174, it will stay as that value no matter how many times it iterates.
Given a minion ID as a string n representing a nonnegative integer of length k in base b, where 2 <= k <= 9 and 2 <= b <= 10, write a function solution(n, b) which returns the length of the ending cycle of the algorithm above starting with n. For instance, in the example above, solution(210022, 3) would return 3, since iterating on 102212 would return to 210111 when done in base 3. If the algorithm reaches a constant, such as 0, then the length is 1.
Here's my code
def solution(n, b): #n(num): str, b(base): int
#Your code here
num = n
k = len(n)
resList = []
resIdx = 0
loopFlag = True
while loopFlag:
numX = "".join(x for x in sorted(num, reverse=True))
numY = "".join(y for y in sorted(num))
xBaseTen, yBaseTen = getBaseTen(numX, b), getBaseTen(numY, b)
xMinusY = xBaseTen - yBaseTen
num = getBaseB(xMinusY, b, k)
resListLen = len(resList)
for i in range(resListLen - 1, -1, -1):
if resList[i] == num:
loopFlag = False
resIdx = resListLen - i
break
if loopFlag:
resList.append(num)
if num == 0:
resIdx = 1
break
return resIdx
def getBaseTen(n, b): #n(number): str, b(base): int -> int
nBaseTenRes = 0
n = str(int(n)) # Shave prepending zeroes
length = len(n) - 1
for i in range(length + 1):
nBaseTenRes += int(n[i]) * pow(b, length - i)
return nBaseTenRes
def getBaseB(n, b, k): #(number): int, b(base): int, k:(len): int -> str
res = ""
r = 0 # Remainder
nCopy = n
while nCopy > 0:
r = nCopy % b
nCopy = floor(nCopy / b)
res += str(r)
res = res[::-1]
resPrependZeroesLen = k - len(res)
if resPrependZeroesLen > 0:
for i in range(resPrependZeroesLen):
res = "0" + res
return res
The two test that are available to me and are not passing, are ('1211', 10) and ('210022', 3). But I get the right answers for them (1, 3).
Why am I failing? Is the algo wrong? Hitting the time limit?

The problem arose between the differences of the execution environments.
When I executed on my machine on Python 3.7 this
r = nCopy % n
gave me an answer as an int.
While Foobar runs on 2.7, and the answer above is given as a float

Python / smallest positive integer

I took following codility demo task
Write a function:
def solution(A)
that, given an array A of N integers, returns the smallest positive integer (greater than 0) that does not occur in A.
For example, given A = [1, 3, 6, 4, 1, 2], the function should return 5.
Given A = [1, 2, 3], the function should return 4.
Given A = [−1, −3], the function should return 1.
Write an efficient algorithm for the following assumptions:
N is an integer within the range [1..100,000];
each element of array A is an integer within the range [−1,000,000..1,000,000].
My Solution
def solution(A):
# write your code in Python 3.6
l = len(A)
B = []
result = 0
n = 0
for i in range(l):
if A[i] >=1:
B.append(A[i])
if B ==[]:
return(1)
else:
B.sort()
B = list(dict.fromkeys(B))
n = len(B)
for j in range(n-1):
if B[j+1]>B[j]+1:
result = (B[j]+1)
if result != 0:
return(result)
else:
return(B[n-1]+1)
Although I get correct output for all inputs I tried but my score was just 22%. Could somebody please highlight where I am going wrong.

Python solution with O(N) time complexity and O(N) space complexity:
def solution(A):
arr = [0] * 1000001
for a in A:
if a>0:
arr[a] = 1
for i in range(1, 1000000+1):
if arr[i] == 0:
return i
My main idea was to:
creat a zero-initialized "buckets" for all the positive possibilities.
Iterate over A. Whenever you meet a positive number, mark it's bucket as visited (1).
Iterate over the "buckets" and return the first zero "bucket".

def solution(A):
s = set(A)
for x in range(1,100002):
if x not in s:
return x
pass
And GOT 100%

# you can write to stdout for debugging purposes, e.g.
# print("this is a debug message")
def solution(A):
# write your code in Python 3.6
i = 1;
B = set(A);
while True:
if i not in B:
return i;
i+=1;

My Javascript solution. The solution is to sort the array and compare the adjacent elements of the array. Complexity is O(N)
function solution(A) {
// write your code in JavaScript (Node.js 8.9.4)
A.sort((a, b) => a - b);
if (A[0] > 1 || A[A.length - 1] < 0 || A.length <= 2) return 1;
for (let i = 1; i < A.length - 1; ++i) {
if (A[i] > 0 && (A[i + 1] - A[i]) > 1) {
return A[i] + 1;
}
}
return A[A.length - 1] + 1;
}

in Codility you must predict correctly others inputs, not only the sample ones and also get a nice performance. I've done this way:
from collections import Counter
def maior_menos_zero(A):
if A < 0:
return 1
else:
return 1 if A != 1 else 2
def solution(A):
if len(A) > 1:
copia = set(A.copy())
b = max(A)
c = Counter(A)
if len(c) == 1:
return maior_menos_zero(A[0])
elif 1 not in copia:
return 1
else:
for x in range(1,b+2):
if x not in copia:
return x
else:
return maior_menos_zero(A[0])
Got it 100%. If is an array A of len(A) == 1, function maior_menos_zero will be called. Moreover, if it's an len(A) > 1 but its elements are the same (Counter), then function maior_menos_zero will be called again. Finally, if 1 is not in the array, so 1 is the smallest positive integer in it, otherwise 1 is in it and we shall make a for X in range(1,max(A)+2) and check if its elements are in A, futhermore, to save time, the first ocurrence of X not in A is the smallest positive integer.

My solution (100% acceptance):
def solution(nums):
nums_set = set()
for el in nums:
if el > 0 and el not in nums_set:
nums_set.add(el)
sorted_set = sorted(nums_set)
if len(sorted_set) == 0:
return 1
if sorted_set[0] != 1:
return 1
for i in range(0, len(sorted_set) - 1, 1):
diff = sorted_set[i + 1] - sorted_set[i]
if diff >= 2:
return sorted_set[i] + 1
return sorted_set[-1] + 1

I tried the following, and got 100% score
def solution(A):
A_set = set(A)
for x in range(10**5 + 1, 1):
if x not in A_set:
return x
else:
return 10**5 + 1

This solution is an easy approach!
def solution(A):
... A.sort()
... maxval = A[-1]
... nextmaxval = A[-2]
... if maxval < 0:
... while maxval<= 0:
... maxval += 1
... return maxval
... else:
... if nextmaxval + 1 in A:
... return maxval +1
... else:
... return nextmaxval + 1

This is my solution
def solution(A):
# write your code in Python 3.8.10
new = set(A)
max_ = abs(max(A)) #use the absolute here for negative maximum value
for num in range(1,max_+2):
if num not in new:
return num

Try this, I am assuming the list is not sorted but if it is sorted you can remove the number_list = sorted(number_list) to make it a little bit faster.
def get_smallest_positive_integer(number_list):
if all(number < 0 for number in number_list) or 1 not in number_list:
#checks if numbers in list are all negative integers or if 1 is not in list
return 1
else:
try:
#get the smallest number in missing integers
number_list = sorted(number_list) # remove if list is already sorted by default
return min(x for x in range(number_list[0], number_list[-1] + 1) if x not in number_list and x != 0)
except:
#if there is no missing number in list get largest number + 1
return max(number_list) + 1
print(get_smallest_positive_integer(number_list))
input:
number_list = [1,2,3]
output:
>>4
input:
number_list = [-1,-2,-3]
output:
>>1
input:
number_list = [2]
output:
>>1
input:
number_list = [12,1,23,3,4,5,61,7,8,9,11]
output:
>>2
input:
number_list = [-1,3,2,1]
output:
>>4

I think this should be as easy as starting at 1 and checking which number first fails to appear.
def solution(A):
i = 1
while i in A:
i += 1
return i
You can also consider putting A's elements into a set (for better performance on the search), but I'm not sure that it's worth for this case.
Update:
I've been doing some tests with the numbers OP gave (numbers from negative million to positive million and 100000 elements).
100000 elements:
Linear Search: 0.003s
Set Search: 0.017s
1000000 elements (extra test):
Linear Search: 0.8s
Set Search: 2.58s

Fibonacci series in bit string

I am working on Fibonacci series but in bit string which can be represented as:
f(0)=0;
f(1)=1;
f(2)=10;
f(3)=101;
f(4)=10110;
f(5)=10110101;
Secondly, I have a pattern for example '10' and want to count how many times this occurs in particular series, for example, the Fibonacci series for 5 is '101101101' so '10' occur 3 times.
my code is running correctly without error but the problem is that it cannot run for more than the value of n=45 I want to run n=100
can anyone help? I only want to calculate the count of occurrence
n=5
fibonacci_numbers = ['0', '1']
for i in range(1,n):
fibonacci_numbers.append(fibonacci_numbers[i]+fibonacci_numbers[i-1])
#print(fibonacci_numbers[-1])
print(fibonacci_numbers[-1])
nStr = str (fibonacci_numbers[-1])
pattern = '10'
count = 0
flag = True
start = 0
while flag:
a = nStr.find(pattern, start)
if a == -1:
flag = False
else:
count += 1
start = a + 1
print(count)

This is a fun one! The trick is that you don't actually need that giant bit string, just the number of 10s it contains and the edges. This solution runs in O(n) time and O(1) space.
from typing import NamedTuple
class FibString(NamedTuple):
"""First digit, last digit, and the number of 10s in between."""
first: int
tens: int
last: int
def count_fib_string_tens(n: int) -> int:
"""Count the number of 10s in a n-'Fibonacci bitstring'."""
def combine(b: FibString, a: FibString) -> FibString:
"""Combine two FibStrings."""
tens = b.tens + a.tens
# mind the edges!
if b.last == 1 and a.first == 0:
tens += 1
return FibString(b.first, tens, a.last)
# First two values are 0 and 1 (tens=0 for both)
a, b = FibString(0, 0, 0), FibString(1, 0, 1)
for _ in range(1, n):
a, b = b, combine(b, a)
return b.tens # tada!
I tested this against your original implementation and sure enough it produces the same answers for all values that the original function is able to calculate (but it's about eight orders of magnitude faster by the time you get up to n=40). The answer for n=100 is 218922995834555169026 and it took 0.1ms to calculate using this method.

The nice thing about the Fibonacci sequence that will solve your issue is that you only need the last two values of the sequence. 10110 is made by combining 101 and 10. After that 10 is no longer needed. So instead of appending, you can just keep the two values. Here is what I've done:
n=45
fibonacci_numbers = ['0', '1']
for i in range(1,n):
temp = fibonacci_numbers[1]
fibonacci_numbers[1] = fibonacci_numbers[1] + fibonacci_numbers[0]
fibonacci_numbers[0] = temp
Note that it still uses a decent amount of memory, but it didn't give me a memory error (it does take a bit of time to run though).
I also wasn't able to print the full string as I got an OSError [Errno 5] Input/Output error but it can still count and print that output.
For larger numbers, storing as a string is going to quickly cause a memory issue. In that case, I'd suggest doing the fibonacci sequence with plain integers and then converting to bits. See here for tips on binary conversion.
While the regular fibonacci sequence doesn't work in a direct sense, consider that 10 is 2 and 101 is 5. 5+2 doesn't work - you want 10110 or an or operation 10100 | 10 yielding 22; so if you shift one by the length of the other, you can get the result. See for example
x = 5
y = 2
(x << 2) | y
>> 22
Shifting x by the number of bits representing y and then doing a bitwise or with | solves the issue. Python summarizes these bitwise operations well here. All that's left for you to do is determine how many bits to shift and implement this into your for loop!
For really large n you will still have a memory issue shown in the plot:
'

Finally i got the answer but can someone explain it briefly why it is working
def count(p, n):
count = 0
i = n.find(p)
while i != -1:
n = n[i + 1:]
i = n.find(p)
count += 1
return count
def occurence(p, n):
a1 = "1"
a0 = "0"
lp = len(p)
i = 1
if n <= 5:
return count(p, atring(n))
while lp > len(a1):
temp = a1
a1 += a0
a0 = temp
i += 1
if i >= n:
return count(p, a1)
fn = a1[:lp - 1]
if -lp + 1 < 0:
ln = a1[-lp + 1:]
else:
ln = ""
countn = count(p, a1)
a1 = a1 + a0
i += 1
if -lp + 1 < 0:
lnp1 = a1[-lp + 1:]
else:
lnp1 = ""
k = 0
countn1 = count(p, a1)
for j in range(i + 1, n + 1):
temp = countn1
countn1 += countn
countn = temp
if k % 2 == 0:
string = lnp1 + fn
else:
string = ln + fn
k += 1
countn1 += count(p, string)
return countn1
def atring(n):
a0 = "0"
a1 = "1"
if n == 0 or n == 1:
return str(n)
for i in range(2, n + 1):
temp = a1
a1 += a0
a0 = temp
return a1
def fn():
a = 100
p = '10'
print( occurence(p, a))
if __name__ == "__main__":
fn()

Python given an array A of N integers, returns the smallest positive integer (greater than 0) that does not occur in A in O(n) time complexity

For example:
input: A = [ 6 4 3 -5 0 2 -7 1 ]
output: 5
Since 5 is the smallest positive integer that does not occur in the array.
I have written two solutions to that problem. The first one is good but I don't want to use any external libraries + its O(n)*log(n) complexity. The second solution "In which I need your help to optimize it" gives an error when the input is chaotic sequences length=10005 (with minus).
Solution 1:
from itertools import count, filterfalse
def minpositive(a):
return(next(filterfalse(set(a).__contains__, count(1))))
Solution 2:
def minpositive(a):
count = 0
b = list(set([i for i in a if i>0]))
if min(b, default = 0) > 1 or min(b, default = 0) == 0 :
min_val = 1
else:
min_val = min([b[i-1]+1 for i, x in enumerate(b) if x - b[i - 1] >1], default=b[-1]+1)
return min_val
Note: This was a demo test in codility, solution 1 got 100% and
solution 2 got 77 %.
Error in "solution2" was due to:
Performance tests ->
medium chaotic sequences length=10005 (with minus) got 3 expected
10000
Performance tests -> large chaotic + many -1, 1, 2, 3 (with
minus) got 5 expected 10000

Testing for the presence of a number in a set is fast in Python so you could try something like this:
def minpositive(a):
A = set(a)
ans = 1
while ans in A:
ans += 1
return ans

Fast for large arrays.
def minpositive(arr):
if 1 not in arr: # protection from error if ( max(arr) < 0 )
return 1
else:
maxArr = max(arr) # find max element in 'arr'
c1 = set(range(2, maxArr+2)) # create array from 2 to max
c2 = c1 - set(arr) # find all positive elements outside the array
return min(c2)

I have an easy solution. No need to sort.
def solution(A):
s = set(A)
m = max(A) + 2
for N in range(1, m):
if N not in s:
return N
return 1
Note: It is 100% total score (Correctness & Performance)

def minpositive(A):
"""Given an list A of N integers,
returns the smallest positive integer (greater than 0)
that does not occur in A in O(n) time complexity
Args:
A: list of integers
Returns:
integer: smallest positive integer
e.g:
A = [1,2,3]
smallest_positive_int = 4
"""
len_nrs_list = len(A)
N = set(range(1, len_nrs_list+2))
return min(N-set(A)) #gets the min value using the N integers

This solution passes the performance test with a score of 100%
def solution(A):
n = sorted(i for i in set(A) if i > 0) # Remove duplicates and negative numbers
if not n:
return 1
ln = len(n)
for i in range(1, ln + 1):
if i != n[i - 1]:
return i
return ln + 1

def solution(A):
B = set(sorted(A))
m = 1
for x in B:
if x == m:
m+=1
return m

Continuing on from Niroj Shrestha and najeeb-jebreel, added an initial portion to avoid iteration in case of a complete set. Especially important if the array is very large.
def smallest_positive_int(A):
sorted_A = sorted(A)
last_in_sorted_A = sorted_A[-1]
#check if straight continuous list
if len(sorted_A) == last_in_sorted_A:
return last_in_sorted_A + 1
else:
#incomplete list, iterate to find the smallest missing number
sol=1
for x in sorted_A:
if x == sol:
sol += 1
else:
break
return sol
A = [1,2,7,4,5,6]
print(smallest_positive_int(A))

This question doesn't really need another answer, but there is a solution that has not been proposed yet, that I believe to be faster than what's been presented so far.
As others have pointed out, we know the answer lies in the range [1, len(A)+1], inclusively. We can turn that into a set and take the minimum element in the set difference with A. That's a good O(N) solution since set operations are O(1).
However, we don't need to use a Python set to store [1, len(A)+1], because we're starting with a dense set. We can use an array instead, which will replace set hashing by list indexing and give us another O(N) solution with a lower constant.
def minpositive(a):
# the "set" of possible answer - values_found[i-1] will tell us whether i is in a
values_found = [False] * (len(a)+1)
# note any values in a in the range [1, len(a)+1] as found
for i in a:
if i > 0 and i <= len(a)+1:
values_found[i-1] = True
# extract the smallest value not found
for i, found in enumerate(values_found):
if not found:
return i+1
We know the final for loop always finds a value that was not marked, because it has one more element than a, so at least one of its cells was not set to True.

def check_min(a):
x= max(a)
if x-1 in a:
return x+1
elif x <= 0:
return 1
else:
return x-1
Correct me if i'm wrong but this works for me.

def solution(A):
clone = 1
A.sort()
for itr in range(max(A) + 2):
if itr not in A and itr >= 1:
clone = itr
break
return clone
print(solution([2,1,4,7]))
#returns 3

def solution(A):
n = 1
for i in A:
if n in A:
n = n+1
else:
return n
return n

def not_in_A(a):
a=sorted(a)
if max(a)<1:
return(1)
for i in range(0,len(a)-1):
if a[i+1]-a[i]>1:
out=a[i]+1
if out==0 or out<1:
continue
return(out)
return(max(a)+1)

mark and then find the first one that didn't find
nums = [ 6, 4, 3, -5, 0, 2, -7, 1 ]
def check_min(nums):
marks = [-1] * len(nums)
for idx, num in enumerate(nums):
if num >= 0:
marks[num] = idx
for idx, mark in enumerate(marks):
if mark == -1:
return idx
return idx + 1

I just modified the answer by #najeeb-jebreel and now the function gives an optimal solution.
def solution(A):
sorted_set = set(sorted(A))
sol = 1
for x in sorted_set:
if x == sol:
sol += 1
else:
break
return sol

I reduced the length of set before comparing
a=[1,222,3,4,24,5,6,7,8,9,10,15,2,3,3,11,-1]
#a=[1,2,3,6,3]
def sol(a_array):
a_set=set()
b_set=set()
cnt=1
for i in a_array:
#In order to get the greater performance
#Checking if element is greater than length+1
#then it can't be output( our result in solution)
if i<=len(a) and i >=1:
a_set.add(i) # Adding array element in set
b_set.add(cnt) # Adding iterator in set
cnt=cnt+1
b_set=b_set.difference(a_set)
if((len(b_set)) > 1):
return(min(b_set))
else:
return max(a_set)+1
sol(a)

def solution(A):
nw_A = sorted(set(A))
if all(i < 0 for i in nw_A):
return 1
else:
ans = 1
while ans in nw_A:
ans += 1
if ans not in nw_A:
return ans
For better performance if there is a possibility to import numpy package.
def solution(A):
import numpy as np
nw_A = np.unique(np.array(A))
if np.all((nw_A < 0)):
return 1
else:
ans = 1
while ans in nw_A:
ans += 1
if ans not in nw_A:
return ans

def solution(A):
# write your code in Python 3.6
min_num = float("inf")
set_A = set(A)
# finding the smallest number
for num in set_A:
if num < min_num:
min_num = num
# print(min_num)
#if negative make positive
if min_num < 0 or min_num == 0:
min_num = 1
# print(min_num)
# if in set add 1 until not
while min_num in set_A:
min_num += 1
return min_num
Not sure why this is not 100% in correctness. It is 100% performance

def solution(A):
arr = set(A)
N = set(range(1, 100001))
while N in arr:
N += 1
return min(N - arr)
solution([1, 2, 6, 4])
#returns 3

How to produce the i-th combination/permutation without iterating

Given any iterable, for example: "ABCDEF"
Treating it almost like a numeral system as such:
A
B
C
D
E
F
AA
AB
AC
AD
AE
AF
BA
BB
BC
....
FF
AAA
AAB
....
How would I go about finding the ith member in this list? Efficiently, not by counting up through all of them. I want to find the billionth (for example) member in this list. I'm trying to do this in python and I am using 2.4 (not by choice) which might be relevant because I do not have access to itertools.
Nice, but not required: Could the solution be generalized for pseudo-"mixed radix" system?
--- RESULTS ---
# ------ paul -----
def f0(x, alph='ABCDE'):
result = ''
ct = len(alph)
while x>=0:
result += alph[x%ct]
x /= ct-1
return result[::-1]
# ----- Glenn Maynard -----
import math
def idx_to_length_and_value(n, length):
chars = 1
while True:
cnt = pow(length, chars)
if cnt > n:
return chars, n
chars += 1
n -= cnt
def conv_base(chars, n, values):
ret = []
for i in range(0, chars):
c = values[n % len(values)]
ret.append(c)
n /= len(values)
return reversed(ret)
def f1(i, values = "ABCDEF"):
chars, n = idx_to_length_and_value(i, len(values))
return "".join(conv_base(chars, n, values))
# -------- Laurence Gonsalves ------
def f2(i, seq):
seq = tuple(seq)
n = len(seq)
max = n # number of perms with 'digits' digits
digits = 1
last_max = 0
while i >= max:
last_max = max
max = n * (max + 1)
digits += 1
result = ''
i -= last_max
while digits:
digits -= 1
result = seq[i % n] + result
i //= n
return result
# -------- yairchu -------
def f3(x, alphabet = 'ABCDEF'):
x += 1 # Make us skip "" as a valid word
group_size = 1
num_letters = 0
while 1: #for num_letters in itertools.count():
if x < group_size:
break
x -= group_size
group_size *= len(alphabet)
num_letters +=1
letters = []
for i in range(num_letters):
x, m = divmod(x, len(alphabet))
letters.append(alphabet[m])
return ''.join(reversed(letters))
# ----- testing ----
import time
import random
tries = [random.randint(1,1000000000000) for i in range(10000)]
numbs = 'ABCDEF'
time0 = time.time()
s0 = [f1(i, numbs) for i in tries]
print 's0 paul',time.time()-time0, 'sec'
time0 = time.time()
s1 = [f1(i, numbs) for i in tries]
print 's1 Glenn Maynard',time.time()-time0, 'sec'
time0 = time.time()
s2 = [f2(i, numbs) for i in tries]
print 's2 Laurence Gonsalves',time.time()-time0, 'sec'
time0 = time.time()
s3 = [f3(i,numbs) for i in tries]
print 's3 yairchu',time.time()-time0, 'sec'
times:
s0 paul 0.470999956131 sec
s1 Glenn Maynard 0.472999811172 sec
s2 Laurence Gonsalves 0.259000062943 sec
s3 yairchu 0.325000047684 sec
>>> s0==s1==s2==s3
True

Third time's the charm:
def perm(i, seq):
seq = tuple(seq)
n = len(seq)
max = n # number of perms with 'digits' digits
digits = 1
last_max = 0
while i >= max:
last_max = max
max = n * (max + 1)
digits += 1
result = ''
i -= last_max
while digits:
digits -= 1
result = seq[i % n] + result
i //= n
return result

Multi-radix solution at the bottom.
import math
def idx_to_length_and_value(n, length):
chars = 1
while True:
cnt = pow(length, chars)
if cnt > n:
return chars, n
chars += 1
n -= cnt
def conv_base(chars, n, values):
ret = []
for i in range(0, chars):
c = values[n % len(values)]
ret.append(c)
n /= len(values)
return reversed(ret)
values = "ABCDEF"
for i in range(0, 100):
chars, n = idx_to_length_and_value(i, len(values))
print "".join(conv_base(chars, n, values))
import math
def get_max_value_for_digits(digits_list):
max_vals = []
for val in digits_list:
val = len(val)
if max_vals:
val *= max_vals[-1]
max_vals.append(val)
return max_vals
def idx_to_length_and_value(n, digits_list):
chars = 1
max_vals = get_max_value_for_digits(digits_list)
while True:
if chars-1 >= len(max_vals):
raise OverflowError, "number not representable"
max_val = max_vals[chars-1]
if n < max_val:
return chars, n
chars += 1
n -= max_val
def conv_base(chars, n, digits_list):
ret = []
for i in range(chars-1, -1, -1):
digits = digits_list[i]
radix = len(digits)
c = digits[n % len(digits)]
ret.append(c)
n /= radix
return reversed(ret)
digits_list = ["ABCDEF", "ABC", "AB"]
for i in range(0, 120):
chars, n = idx_to_length_and_value(i, digits_list)
print "".join(conv_base(chars, n, digits_list))

What you're doing is close to a conversion from base 10 (your number) to base 6, with ABCDEF being your digits. The only difference is "AA" and "A" are different, which is wrong if you consider "A" the zero-digit.
If you add the next greater power of six to your number, and then do a base conversion to base 6 using these digits, and finally strip the first digit (which should be a "B", i.e. a "1"), you've got the result.
I just want to post an idea here, not an implementation, because the question smells a lot like homework to me (I do give the benefit of the doubt; it's just my feeling).

First compute the length by summing up powers of six until you exceed your index (or better use the formula for the geometric series).
Subtract the sum of smaller powers from the index.
Compute the representation to base 6, fill leading zeros and map 0 -> A, ..., 5 -> F.

This works (and is what i finally settled on), and thought it was worth posting because it is tidy. However it is slower than most answers. Can i perform % and / in the same operation?
def f0(x, alph='ABCDE'):
result = ''
ct = len(alph)
while x>=0:
result += alph[x%ct]
x /= ct-1
return result[::-1]

alphabet = 'ABCDEF'
def idx_to_excel_column_name(x):
x += 1 # Make us skip "" as a valid word
group_size = 1
for num_letters in itertools.count():
if x < group_size:
break
x -= group_size
group_size *= len(alphabet)
letters = []
for i in range(num_letters):
x, m = divmod(x, len(alphabet))
letters.append(alphabet[m])
return ''.join(reversed(letters))
def excel_column_name_to_idx(name):
q = len(alphabet)
x = 0
for letter in name:
x *= q
x += alphabet.index(letter)
return x+q**len(name)//(q-1)-1

Since we are converting from a number Base(10) to a number Base(7), whilst avoiding all "0" in the output, we will have to adjust the orginal number, so we do skip by one every time the result would contain a "0".
1 => A, or 1 in base [0ABCDEF]
7 => AA, or 8 in base [0ABCDEF]
13 => BA, or 15 in base [0ABCDEF]
42 => FF, or 48 in base [0ABCDEF]
43 =>AAA, or 50 in base [0ABCDEF]
Here's some Perl code that shows what I'm trying to explain
(sorry, didn't see this is a Python request)
use strict;
use warnings;
my #Symbols=qw/0 A B C D E F/;
my $BaseSize=#Symbols ;
for my $NR ( 1 .. 45) {
printf ("Convert %3i => %s\n",$NR ,convert($NR));
}
sub convert {
my ($nr,$res)=#_;
return $res unless $nr>0;
$res="" unless defined($res);
#Adjust to skip '0'
$nr=$nr + int(($nr-1)/($BaseSize-1));
return convert(int($nr/$BaseSize),$Symbols[($nr % ($BaseSize))] . $res);
}

In perl you'd just convert your input i from base(10) to base(length of "ABCDEF"), then do a tr/012345/ABCDEF/ which is the same as y/0-5/A-F/. Surely Python has a similar feature set.
Oh, as pointed out by Yarichu the combinations are a tad different because if A represented 0, then there would be no combinations with leading A (though he said it a bit different). It seems I thought the problem to be more trivial than it is. You cannot just transliterate different base numbers, because numbers containing the equivalent of 0 would be
skipped in the sequence.
So what I suggested is actually only the last step of what starblue suggested, which is essentially what Laurence Gonsalves implemented ftw. Oh, and there is no transliteration (tr// or y//) operation in Python, what a shame.