s_id stringlengths 10 10 | p_id stringlengths 6 6 | u_id stringlengths 10 10 | date stringlengths 10 10 | language stringclasses 1
value | original_language stringclasses 11
values | filename_ext stringclasses 1
value | status stringclasses 1
value | cpu_time int64 0 100 | memory stringlengths 4 6 | code_size int64 15 14.7k | code stringlengths 15 14.7k | problem_id stringlengths 6 6 | problem_description stringlengths 358 9.83k | input stringlengths 2 4.87k | output stringclasses 807
values | __index_level_0__ int64 1.1k 1.22M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s480075396 | p01105 | u260980560 | 1501178752 | Python | Python3 | py | Accepted | 40 | 8508 | 926 | a=65280; b=61680; c=52428; d=43690; e=65535
from heapq import heappush, heappop
Q = [(1, a), (1, b), (1, c), (1, d)]
L = {a: 1, b: 1, c: 1, d: 1, e: 1, e: 1, 0: 1}
H = []
get = L.get
push = H.append
while Q:
l, p = heappop(Q)
if L[p] < l: continue
if l+1 < get(p ^ e, 17):
L[p ^ e] = l+1
l < ... | p01105 |
<h3><u>Boolean Expression Compressor</u></h3>
<p>
You are asked to build a compressor for Boolean expressions
that transforms expressions to the shortest form keeping their meaning.
</p>
<p>
The grammar of the Boolean expressions has terminals
<tt>0</tt> <tt>1</tt> <tt>a</tt> <tt>b</tt> <tt>c<... | 0
(a*(1*b))
(1^a)
(-(-a*-b)*a)
(a^(b^(c^d)))
.
| 1
5
2
1
13
| 25,544 |
s878037441 | p01105 | u260980560 | 1501179743 | Python | Python3 | py | Accepted | 40 | 8308 | 1,033 | a=65280; b=61680; c=52428; d=43690; e=65535
QS = [[] for i in range(17)]
QS[1] = [a, b, c, d]
L = {a: 1, b: 1, c: 1, d: 1, e: 1, e: 1, 0: 1}
H = []
get = L.get
push = H.append
for l in range(1, 16):
Q = QS[l]
QN = QS[l+1]
while Q:
p = Q.pop()
if L[p] < l: continue
if l+1 < get(p ^ e,... | p01105 |
<h3><u>Boolean Expression Compressor</u></h3>
<p>
You are asked to build a compressor for Boolean expressions
that transforms expressions to the shortest form keeping their meaning.
</p>
<p>
The grammar of the Boolean expressions has terminals
<tt>0</tt> <tt>1</tt> <tt>a</tt> <tt>b</tt> <tt>c<... | 0
(a*(1*b))
(1^a)
(-(-a*-b)*a)
(a^(b^(c^d)))
.
| 1
5
2
1
13
| 25,545 |
s505979665 | p01105 | u260980560 | 1501207740 | Python | Python3 | py | Accepted | 40 | 8256 | 1,022 | a=65280; b=61680; c=52428; d=43690; e=65535
QS = [[] for i in range(17)]
QS[1] = [a, b, c, d]
L = {a: 1, b: 1, c: 1, d: 1, e: 1, e: 1, 0: 1}
H = []
get = L.get
push = H.append
for l in range(1, 16):
Q = QS[l]
li = 13-l; l3 = l+3; l1 = l+1
pop = Q.pop
pushQN = QS[l1].append
while Q:
p = pop()... | p01105 |
<h3><u>Boolean Expression Compressor</u></h3>
<p>
You are asked to build a compressor for Boolean expressions
that transforms expressions to the shortest form keeping their meaning.
</p>
<p>
The grammar of the Boolean expressions has terminals
<tt>0</tt> <tt>1</tt> <tt>a</tt> <tt>b</tt> <tt>c<... | 0
(a*(1*b))
(1^a)
(-(-a*-b)*a)
(a^(b^(c^d)))
.
| 1
5
2
1
13
| 25,546 |
s417331199 | p01105 | u260980560 | 1501208956 | Python | Python3 | py | Accepted | 40 | 8336 | 1,071 | a=65280; b=61680; c=52428; d=43690; e=65535
QS = [[] for i in range(17)]
QS[1] = [a, b, c, d]
L = {a: 1, b: 1, c: 1, d: 1, e: 1, e: 1, 0: 1}
H = []
get = L.get
push = H.append
for l in range(1, 16):
Q = QS[l]
li = 13-l; l3 = l+3; l1 = l+1
pop = Q.pop
pushQN = QS[l1].append
while Q:
p = pop()... | p01105 |
<h3><u>Boolean Expression Compressor</u></h3>
<p>
You are asked to build a compressor for Boolean expressions
that transforms expressions to the shortest form keeping their meaning.
</p>
<p>
The grammar of the Boolean expressions has terminals
<tt>0</tt> <tt>1</tt> <tt>a</tt> <tt>b</tt> <tt>c<... | 0
(a*(1*b))
(1^a)
(-(-a*-b)*a)
(a^(b^(c^d)))
.
| 1
5
2
1
13
| 25,547 |
s494475291 | p01106 | u847467233 | 1531451474 | Python | Python3 | py | Accepted | 30 | 5608 | 386 | # AOJ 1621: Folding a Ribbon
# Python3 2018.7.13 bal4u
ans, low = [0]*62, [0]*62
while True:
n, i, j = map(int, input().split())
if n == 0: break
i -= 1; j -= 1
for k in range(1, n+1):
low[n-k] = (i >> (n-k)) & 1
if low[n-k] == 0: i = ~i
for k in range(1, n+1):
ans[k] = 'L' if ((j >> (n-k)) & 1) == low[k-1]... | p01106 |
<h3><u>Folding a Ribbon</u></h3>
<p>
Think of repetitively folding a very long and thin ribbon. First,
the ribbon is spread out from left to right, then it is creased at
its center, and one half of the ribbon is laid over the other. You
can either fold it from the left to the right, picking up the left
en... | 3 3 2
12 578 2214
59 471605241352156968 431565444592236940
0 0 0
| LRR
RLLLRRRLRRLL
LRRRLRRLLRRRRLLLLRLLRRRLRRLLRLLLLLLRLRLLRLRLLLRLRLLRLLRRRLL
| 25,548 |
s277786832 | p01106 | u260980560 | 1501000153 | Python | Python3 | py | Accepted | 40 | 7660 | 675 | while 1:
n, i, j = map(int, input().split())
if n+i+j == 0:
break
i = 2**n - i
up = [0]*n
for k in range(n):
if 2**(n-1-k) <= i:
up[k] = 1
i = 2**(n-k)-1 - i
up.reverse()
ans = ""
j -= 1
for k in range(n):
if up[k]==0 and j < 2**(n-1-k)... | p01106 |
<h3><u>Folding a Ribbon</u></h3>
<p>
Think of repetitively folding a very long and thin ribbon. First,
the ribbon is spread out from left to right, then it is creased at
its center, and one half of the ribbon is laid over the other. You
can either fold it from the left to the right, picking up the left
en... | 3 3 2
12 578 2214
59 471605241352156968 431565444592236940
0 0 0
| LRR
RLLLRRRLRRLL
LRRRLRRLLRRRRLLLLRLLRRRLRRLLRLLLLLLRLRLLRLRLLLRLRLLRLLRRRLL
| 25,549 |
s351614613 | p01106 | u260980560 | 1501000382 | Python | Python3 | py | Accepted | 40 | 7584 | 638 | while 1:
n, i, j = map(int, input().split())
if n+i+j == 0:
break
i = 2**n - i
up = [0]*n
for k in range(n):
if 2**(n-1-k) <= i:
up[k] = 1
i = 2**(n-k)-1 - i
up.reverse()
ans = ""
j -= 1
for k in range(n):
if up[k]:
if j < 2... | p01106 |
<h3><u>Folding a Ribbon</u></h3>
<p>
Think of repetitively folding a very long and thin ribbon. First,
the ribbon is spread out from left to right, then it is creased at
its center, and one half of the ribbon is laid over the other. You
can either fold it from the left to the right, picking up the left
en... | 3 3 2
12 578 2214
59 471605241352156968 431565444592236940
0 0 0
| LRR
RLLLRRRLRRLL
LRRRLRRLLRRRRLLLLRLLRRRLRRLLRLLLLLLRLRLLRLRLLLRLRLLRLLRRRLL
| 25,550 |
s878216666 | p01106 | u009961299 | 1502698700 | Python | Python3 | py | Accepted | 40 | 7784 | 1,225 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
def rdp_trace(n: int, i: int) -> list:
def loop(n: int, i: int) -> list:
if n == 1:
return []
if i <= n // 2:
rval = loop(n // 2, (n // 2) - i + 1)
rval.append(i)
return rval
else:
rva... | p01106 |
<h3><u>Folding a Ribbon</u></h3>
<p>
Think of repetitively folding a very long and thin ribbon. First,
the ribbon is spread out from left to right, then it is creased at
its center, and one half of the ribbon is laid over the other. You
can either fold it from the left to the right, picking up the left
en... | 3 3 2
12 578 2214
59 471605241352156968 431565444592236940
0 0 0
| LRR
RLLLRRRLRRLL
LRRRLRRLLRRRRLLLLRLLRRRLRRLLRLLLLLLRLRLLRLRLLLRLRLLRLLRRRLL
| 25,551 |
s210805888 | p01106 | u467175809 | 1563383188 | Python | Python | py | Accepted | 20 | 5028 | 778 | #!/usr/bin/env python
from collections import deque
import itertools as ite
import sys
import math
sys.setrecursionlimit(1000000)
INF = 10 ** 18
MOD = 10 ** 9 + 7
while True:
n, y, x = map(int, raw_input().split())
if n == 0:
break
ys = [y]
for i in range(1, n)[::-1]:
if y <= 2 ** i:... | p01106 |
<h3><u>Folding a Ribbon</u></h3>
<p>
Think of repetitively folding a very long and thin ribbon. First,
the ribbon is spread out from left to right, then it is creased at
its center, and one half of the ribbon is laid over the other. You
can either fold it from the left to the right, picking up the left
en... | 3 3 2
12 578 2214
59 471605241352156968 431565444592236940
0 0 0
| LRR
RLLLRRRLRRLL
LRRRLRRLLRRRRLLLLRLLRRRLRRLLRLLLLLLRLRLLRLRLLLRLRLLRLLRRRLL
| 25,552 |
s872429020 | p01106 | u003059790 | 1562485977 | Python | Python3 | py | Accepted | 40 | 5608 | 692 | while True:
N,I,J = map(int,input().split())
if N == 0:
break
L = [I]*N
for i in range(N-2,-1,-1):
if L[i+1]<=2**(i+1):
L[i] = 2**(i+1) - L[i+1] + 1
else:
L[i] = L[i+1] - 2**(i+1)
ans = ""
for i in range(N):
if L[i] > 2**i:
if J... | p01106 |
<h3><u>Folding a Ribbon</u></h3>
<p>
Think of repetitively folding a very long and thin ribbon. First,
the ribbon is spread out from left to right, then it is creased at
its center, and one half of the ribbon is laid over the other. You
can either fold it from the left to the right, picking up the left
en... | 3 3 2
12 578 2214
59 471605241352156968 431565444592236940
0 0 0
| LRR
RLLLRRRLRRLL
LRRRLRRLLRRRRLLLLRLLRRRLRRLLRLLLLLLRLRLLRLRLLLRLRLLRLLRRRLL
| 25,553 |
s379745126 | p01106 | u598745142 | 1557551800 | Python | Python3 | py | Accepted | 30 | 5612 | 1,146 | while 1:
s = input()
n, i, j = map(int, s.split())
if n==0:
break
updown = [None for _ in range(n+1)];
whole = 2**n
fold = n
from_top = i
while 1:
if whole == 1:
assert fold == 0
break
half = whole // 2
if from_top <= half:
... | p01106 |
<h3><u>Folding a Ribbon</u></h3>
<p>
Think of repetitively folding a very long and thin ribbon. First,
the ribbon is spread out from left to right, then it is creased at
its center, and one half of the ribbon is laid over the other. You
can either fold it from the left to the right, picking up the left
en... | 3 3 2
12 578 2214
59 471605241352156968 431565444592236940
0 0 0
| LRR
RLLLRRRLRRLL
LRRRLRRLLRRRRLLLLRLLRRRLRRLLRLLLLLLRLRLLRLRLLLRLRLLRLLRRRLL
| 25,554 |
s420681707 | p01106 | u281836941 | 1530822141 | Python | Python3 | py | Accepted | 40 | 5608 | 887 | while 1:
n,a,b=map(int,input().split())
if n==0:
break
h_pos=[a]
all=2**n
for i in range(n):
if 1<=h_pos[-1]<=all//4:
h_pos.append(all//4+all//4-h_pos[-1]+1)
elif all//4+1<=h_pos[-1]<=all//2:
h_pos.append(all//2-h_pos[-1]+1)
elif all//2+1<=h_po... | p01106 |
<h3><u>Folding a Ribbon</u></h3>
<p>
Think of repetitively folding a very long and thin ribbon. First,
the ribbon is spread out from left to right, then it is creased at
its center, and one half of the ribbon is laid over the other. You
can either fold it from the left to the right, picking up the left
en... | 3 3 2
12 578 2214
59 471605241352156968 431565444592236940
0 0 0
| LRR
RLLLRRRLRRLL
LRRRLRRLLRRRRLLLLRLLRRRLRRLLRLLLLLLRLRLLRLRLLLRLRLLRLLRRRLL
| 25,555 |
s748842638 | p01107 | u260980560 | 1501042513 | Python | Python3 | py | Accepted | 70 | 7840 | 1,162 | dd = [(-1, 0), (0, -1), (1, 0), (0, 1)]
while 1:
n, m = map(int, input().split())
if n == m == 0:
break
C = [list(input() + "#") for i in range(n)] + ["#"*(m+2)]
used = [[0]*m for i in range(n)]
def move(x0, y0, x1, y1, d):
x = x0; y = y0
moved = 0; cnt = 0
history =... | p01107 |
<h3><u>Go around the Labyrinth</u></h3>
<p>
Explorer Taro got a floor plan of a labyrinth.
The floor of this labyrinth is in the form of a two-dimensional grid.
Each of the cells on the floor plan corresponds to a room and is indicated whether it can be entered or not.
The labyrinth has only one entrance located at ... | 3 3
...
.#.
...
5 5
..#..
.....
#....
.....
.....
3 8
..#.....
........
.....#..
3 5
..#..
.....
..#..
4 4
....
....
..##
..#.
0 0
| YES
NO
YES
NO
NO
| 25,556 |
s509421684 | p01109 | u209989098 | 1531356186 | Python | Python3 | py | Accepted | 50 | 6612 | 220 | a = int(input())
su = 0
while a != 0:
b = list(map(int,input().split()))
c = sum(b)/len(b)
for i in range(len(b)):
if b[i] <= c:
su += 1
print(su)
su = 0
a = int(input())
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,557 |
s124419304 | p01109 | u847467233 | 1531380313 | Python | Python3 | py | Accepted | 50 | 6604 | 216 | # AOJ 1624 Income Inequality
# Python 2018.7.12 bal4u
while True:
n = int(input())
if n == 0: break
a = list(map(int, input().split()))
s = int(sum(a)//n)
ans = 0
for x in a:
if x <= s: ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,558 |
s656068525 | p01109 | u258535552 | 1535948625 | Python | Python3 | py | Accepted | 60 | 6612 | 194 | while(1):
n=int(input())
if n==0:
break
a=[int(i) for i in input().split()]
total=0
for i in a:
total+=i
hei=total/n
count=0
for i in a:
if i <=hei:
count+=1
print(count)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,559 |
s544284734 | p01109 | u623996423 | 1541072353 | Python | Python3 | py | Accepted | 50 | 6604 | 174 | while True:
n = int(input())
if n == 0:
break;
a = list(map(int, input().split()))
mean = sum(a)/n
print(len(list(filter(lambda x:x<=mean, a))))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,560 |
s452292938 | p01109 | u352394527 | 1545331304 | Python | Python3 | py | Accepted | 60 | 6620 | 150 | while True:
n = int(input())
if n == 0:break
alst = list(map(int, input().split()))
ave = sum(alst) / n
print(sum(a <= ave for a in alst))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,561 |
s947362608 | p01109 | u328199937 | 1555818229 | Python | Python3 | py | Accepted | 50 | 6616 | 274 | anslist = []
while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
ans = 0
ave = sum(a) / n
for i in range(n):
if a[i] <= ave:
ans += 1
anslist.append(ans)
for i in anslist:
print(i)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,562 |
s291689246 | p01109 | u845643816 | 1555922804 | Python | Python3 | py | Accepted | 50 | 6608 | 207 | while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
m = sum(a) / n
ans = 0
for aa in a:
if aa <= m:
ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,563 |
s338169534 | p01109 | u444626963 | 1597762900 | Python | Python3 | py | Accepted | 60 | 6612 | 224 |
while True:
a = 0
num = int(input())
if num == 0: break
ls = list(map(int,input().split()))
ave = sum(ls) / num
for i in range(num):
if ls[i] <= ave :
a += 1
print(a)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,564 |
s497180319 | p01109 | u322947441 | 1597743670 | Python | Python3 | py | Accepted | 60 | 6608 | 221 | while True:
n = int(input());
if n == 0:
break;
a = list(map(int, input().split()));
h = sum(a)/n;
k = 0;
for i in range(0,n):
if a[i] <= h:
k += 1;
print(k);
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,565 |
s668429158 | p01109 | u593595530 | 1597742953 | Python | Python3 | py | Accepted | 40 | 6620 | 170 |
while True:
x=int(input())
if x==0:
break
alst = list(map(int,input().split()))
num = sum(alst) / x
print(sum(a <= num for a in alst))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,566 |
s709589008 | p01109 | u288578617 | 1597688227 | Python | Python3 | py | Accepted | 70 | 6808 | 307 | while True:
t=[]
n=int(input())
if n==0:
break
else:
m=input().strip().split()
k=[int(i) for i in m]
x=sum(k)/n
for i in range(n):
if k[i]<=x:
t.append(k[i])
else:
pass
print(len(t))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,567 |
s871018037 | p01109 | u057189799 | 1597546082 | Python | Python3 | py | Accepted | 60 | 6600 | 216 | while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
sum_a = sum(a)
ans = 0
for e in a:
if e * n <= sum_a:
ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,568 |
s847630476 | p01109 | u695568874 | 1597508298 | Python | Python3 | py | Accepted | 60 | 6608 | 207 | while True:
n=int(input())
if n==0:
break
m=list(map(int,input().split()))
M=sum(m)//n
A=0
for i in range(n):
S=m[i]-M
if S<=0:
A=A+1
print(A)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,569 |
s613344635 | p01109 | u711365732 | 1597476335 | Python | Python3 | py | Accepted | 60 | 6616 | 194 | while True:
n = int(input())
if n==0:
break
a = [int(x) for x in input().split()]
ave = sum(a)/n
c = 0
for j in range(n):
if a[j]<=ave:
c += 1
print(c)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,570 |
s295212656 | p01109 | u173393391 | 1597324885 | Python | Python3 | py | Accepted | 70 | 6348 | 223 | while True:
n=int(input())
if n==0:
break
l=(input().split())
for i in range(n):
l[i]=int(l[i])
m=sum(l)/n
a=0
for i in range(n):
if l[i]<=m:
a+=1
print(a)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,571 |
s831486842 | p01109 | u583329397 | 1597220361 | Python | Python3 | py | Accepted | 60 | 6608 | 268 | while True:
n=int(input())
if n==0:
break
a=list(map(int,input().split()))
sum=0
for i in range(n):
sum+=a[i]
ave=sum/n
count=0
for i in range(n):
if a[i]<=ave:
count+=1
print(count)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,572 |
s606005435 | p01109 | u596129030 | 1597052311 | Python | Python3 | py | Accepted | 60 | 6604 | 258 | while True:
n=int(input())
if n==0:
break
a=list(map(int,input().split()))
c=0
ave=0
for i in range(n):
ave=ave+a[i]
ave=ave//n
for i in range(n):
if ave>=a[i]:
c=c+1
print(c)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,573 |
s879912555 | p01109 | u392970366 | 1596626963 | Python | Python3 | py | Accepted | 40 | 6620 | 195 | ans = []
while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
avg = sum(a) / n
ans.append(sum(avg >= x for x in a))
print(*ans, sep="\n")
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,574 |
s185955047 | p01109 | u309196579 | 1596605074 | Python | Python3 | py | Accepted | 40 | 6620 | 195 | ans = []
while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
avg = sum(a) / n
ans.append(sum(avg >= x for x in a))
print(*ans, sep="\n")
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,575 |
s648922118 | p01109 | u826807985 | 1596441882 | Python | Python3 | py | Accepted | 50 | 6612 | 281 | try:
while True:
n = int(input())
if n == 0:
break
ika = 0
a = list(map(int,input().split()))
m = sum(a) / n
for j in a:
if j <= m:
ika += 1
print(ika)
except EOFError:
pass
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,576 |
s790866875 | p01109 | u290304811 | 1596100021 | Python | Python3 | py | Accepted | 40 | 6616 | 239 |
while 1:
n = int(input())
if n==0: break
a = list(map(int, input().split()))
ave = sum(a)/n
print(len([i for i in a if i <= ave]))
#リスト内包表記、条件を満たす要素の数をlen()で表示
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,577 |
s931555703 | p01109 | u630948380 | 1595898350 | Python | Python3 | py | Accepted | 50 | 6612 | 234 | while True:
M=0
A=int(input())
if A==0:
break
L=list(map(int,input().split()))
B=sum(L)/len(L)
for i in range(A):
if L[i]<=B:
M+=1
print(M)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,578 |
s754211897 | p01109 | u128671689 | 1595896772 | Python | Python3 | py | Accepted | 50 | 6612 | 268 | while True:
a=int(input())
if a==0:
break
else:
b=list(map(int,input().split()))
h=sum(b)/a
c=0
for i in range(a):
if h>=b[i]:
c+=1
else:
pass
print(c)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,579 |
s086978255 | p01109 | u647921435 | 1595292183 | Python | Python3 | py | Accepted | 60 | 6616 | 232 | while True:
n=int(input())
if n==0:
break
else:
X=[int(i) for i in input().split()]
a=sum(X)/n
s=0
for i in range(n):
if X[i]<=a:
s+=1
print(s)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,580 |
s451643806 | p01109 | u395654950 | 1595223117 | Python | Python3 | py | Accepted | 50 | 6612 | 273 | # coding: utf-8
# Your code here!
while True:
n = int(input()) # 国民の人数
if n == 0: break
income = list(map(int, input().split()))
avg = sum(income) // n
c = 0
for i in range(n):
if income[i] <= avg:
c += 1
print(c)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,581 |
s986234852 | p01109 | u253463111 | 1594617422 | Python | Python3 | py | Accepted | 50 | 6612 | 201 | while True:
n=int(input())
if n==0:
break
a=list(map(int,input().split()))
ans=0
ave=sum(a)/n
for i in range(n):
if a[i]<=ave:
ans+=1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,582 |
s682322746 | p01109 | u862272701 | 1594005746 | Python | Python3 | py | Accepted | 80 | 6616 | 339 | while True:
try:
n = int(input())
a = list(map(int,input().split()))
s = sum(a)
ave = s/n
i = 0
x = []
while i <= n-1:
if a[i] <= ave:
x.append(a[i])
i += 1
print(len(x))
except:
if n == 0:... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,583 |
s280354636 | p01109 | u228556128 | 1593562017 | Python | Python3 | py | Accepted | 50 | 6608 | 193 | while True:
n=int(input())
if n==0:
break
lst=list(map(int,input().split()))
x=sum(lst)/n
ans=0
for i in lst:
if x>=i:
ans+=1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,584 |
s847780405 | p01109 | u994684803 | 1593407667 | Python | Python3 | py | Accepted | 50 | 6604 | 235 | while True:
n = int(input())
if n == 0:
break
a = list(map(int,input().split()))
b = sum(a)/n
ans = 0
for i in a:
if i <= b:
ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,585 |
s663227436 | p01109 | u747915832 | 1591092054 | Python | Python3 | py | Accepted | 70 | 6616 | 220 | while True:
n = int(input())
if n==0:
break
a = [int(a) for a in input().split()]
ave = sum(a)/n
count = 0
for i in range(n):
if a[i]<=ave:
count += 1
print(count)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,586 |
s567328603 | p01109 | u240091169 | 1590975057 | Python | Python3 | py | Accepted | 60 | 6608 | 248 | while True :
n = int(input())
if n == 0 :
break
lst = list(map(int, input().split()))
ave_lst = sum(lst) / len(lst)
Sum = 0
for i in range(n) :
if lst[i] <= ave_lst :
Sum += 1
print(Sum)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,587 |
s100225681 | p01109 | u842461513 | 1589960154 | Python | Python3 | py | Accepted | 60 | 6608 | 439 | #無限ループと標準入力とカウンタ変数のリセット
while True:
kai = 0
num = int(input())
#入力値が0だったら終わる
if num == 0:break
#標準入力をし、平均値を求める
line = list(map(int,input().split()))
heikin = sum(line) / num
#平均値以下の個数を調べ個数を出力
for i in range(num):
if line[i] <= heikin:kai += 1
print(kai)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,588 |
s705786735 | p01109 | u037441960 | 1589779933 | Python | Python3 | py | Accepted | 60 | 6612 | 345 | while True :
n = int(input())
if n == 0 :
break
else :
A = list(map(int, input().split()))
cnt = 0
ave = sum(A) / n
# 平均 average
for i in range(len(A)) :
if A[i] <= ave :
cnt += 1
... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,589 |
s865264946 | p01109 | u014861569 | 1589526864 | Python | Python3 | py | Accepted | 50 | 6612 | 225 | while True:
t=0
n=int(input())
if n==0:
break
else:
y=list(map(int,input().split()))
s=sum(y)/n
for i in range(n):
if y[i]<=s:
t+=1
print(t)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,590 |
s453626544 | p01109 | u353888999 | 1586886262 | Python | Python3 | py | Accepted | 50 | 6612 | 204 | while 1:
n = int(input())
if n == 0:
break
a = list(map(int,input().split()))
cnt = 0
ave = sum(a) / n
for i in a:
if ave >= i:
cnt +=1
print(cnt)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,591 |
s329649529 | p01109 | u115626571 | 1584446791 | Python | Python3 | py | Accepted | 60 | 6608 | 254 | def solve():
while(True):
N = int(input())
if N == 0:
break
l = list(map(int,input().split()))
ave = sum(l)//len(l)
ans = 0
for a in l:
if a <= ave:
ans+=1
print(ans)
if __name__ == '__main__':
solve()
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,592 |
s018410104 | p01109 | u153447291 | 1577962859 | Python | Python3 | py | Accepted | 50 | 6608 | 203 | while True:
c = int(input())
if c == 0:
break
a = list(map(int,input().split()))
az = sum(a)/c
ans = 0
for x in a:
if x <= az:
ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,593 |
s676269470 | p01109 | u829695570 | 1577792472 | Python | Python3 | py | Accepted | 50 | 6604 | 199 | while 1:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
m = sum(a)/n
cnt = 0
for i in a:
if i <= m:
cnt += 1
print(cnt)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,594 |
s414363253 | p01109 | u798961710 | 1574695566 | Python | Python3 | py | Accepted | 70 | 6348 | 308 | while True:
num = int(input())
if num == 0:
break
else:
x = input().split()
for i in range(num):
x[i] = int(x[i])
ave = sum(x)/num
count = 0
for i in range(num):
if x[i]<=ave:
count += 1
print(count)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,595 |
s724271803 | p01109 | u803862921 | 1571702654 | Python | Python3 | py | Accepted | 50 | 6612 | 232 | while True:
num = int(input())
if num == 0:
break
L = [int(x) for x in input().split()]
ave = sum(L)/ len(L)
c = 0
for i in L:
if i <= ave:
c += 1
print(c)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,596 |
s885582536 | p01109 | u784856415 | 1565658021 | Python | Python3 | py | Accepted | 70 | 7552 | 484 | import os,re,sys,operator
from collections import Counter,deque
from operator import itemgetter
from itertools import accumulate,combinations,groupby
from sys import stdin,setrecursionlimit
from copy import deepcopy
import heapq
setrecursionlimit(10**6)
while 1:
n=int(stdin.readline().rstrip())
if n==0:
... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,597 |
s388759701 | p01109 | u314166831 | 1564066482 | Python | Python3 | py | Accepted | 50 | 6736 | 3,219 | # coding=utf-8
###
### for python program
###
import sys
import math
# math class
class mymath:
### pi
pi = 3.14159265358979323846264338
### Prime Number
def pnum_eratosthenes(self, n):
ptable = [0 for i in range(n+1)]
plist = []
for i in range(2, n+1):
if ptable... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,598 |
s013803871 | p01109 | u955885671 | 1563450780 | Python | Python3 | py | Accepted | 40 | 6616 | 165 | while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
avg = sum(a)/n
print(len([i for i in a if avg >= i]))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,599 |
s638118585 | p01109 | u212736010 | 1563449842 | Python | Python3 | py | Accepted | 60 | 7360 | 284 | nl=[]
al=[]
while 1:
n=input()
if n=="0":
break
nl.append(int(n))
a = list(map(lambda x: int(x), input().split()))
al.append(a)
for i in range(len(nl)):
ave = sum(al[i]) / nl[i]
num = len(list(filter(lambda x: x <= ave, al[i])))
print(num)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,600 |
s116956964 | p01109 | u314932236 | 1563199882 | Python | Python3 | py | Accepted | 60 | 6612 | 193 | def main(n):
if n == 0:
return False
a = list(map(int,input().split()))
m = sum(a)/n
ans = 0
for i in a:
if i<= m:
ans += 1
print(ans)
return True
while main(int(input())): pass
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,601 |
s114825438 | p01109 | u530906291 | 1562923316 | Python | Python3 | py | Accepted | 50 | 6624 | 154 | while True:
n=int(input())
if n==0:
break
A=list(map(int,input().split()))
AVE=sum(A)/n
print(len([a for a in A if a<=AVE]))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,602 |
s352501780 | p01109 | u320121447 | 1562843313 | Python | Python3 | py | Accepted | 40 | 6616 | 189 | while True:
n = int(input())
if n == 0:
break
A = list(map(int, input().split()))
assert len(A) == n
ave = sum(A) // n
print(sum(1 for a in A if a <= ave))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,603 |
s366856332 | p01109 | u317942270 | 1562813173 | Python | Python3 | py | Accepted | 50 | 6612 | 274 | ans_list = []
while True:
n = int(input())
if n == 0:
break
A = list(map(int,input().split()))
ave = sum(A) / len(A)
cnt = 0
for a in A:
if a <= ave:
cnt += 1
ans_list.append(cnt)
for ans in ans_list:
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,604 |
s570992246 | p01109 | u099037643 | 1562668927 | Python | Python3 | py | Accepted | 60 | 7452 | 448 | x=[]
a=[] #appendのために宣言が必要
while True:
try:
a.append(list(map(int,input().split())))
except:
break;
#または、quit(),os.exit()をして止める。
#[[1, 2, 2, 3, 1], [4, 5, 3, 4, 1, 2, 3, 4], [2, 3, 5, 6, 0, 2]]
for i in range(len(a)//2):
x.append(i)
n=i*2
m=i*2+1
s=sum(a[m])/a[n][0]
... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,605 |
s377429346 | p01109 | u215870135 | 1562471991 | Python | Python3 | py | Accepted | 50 | 6608 | 252 | # import sys
# input = sys.stdin.readline
while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
m = sum(a)/n
count = 0
for i in a:
if i <= m:
count += 1
print(count)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,606 |
s763514200 | p01109 | u701749469 | 1562390873 | Python | Python3 | py | Accepted | 60 | 6608 | 233 | while 1:
n = int(input())
if n == 0: break
a = sorted(list(map(int, input().split())))
mean = sum(a) / len(a)
count = 0
for item in a:
if item <= mean: count += 1
else: break
print(count)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,607 |
s356434831 | p01109 | u937153068 | 1562319237 | Python | Python3 | py | Accepted | 80 | 6360 | 378 | while(True):
n = input()
if n=="0":
break
elif (int)(n)>50000:
continue
hairetu = input()
tarou = hairetu.split(" ")
avg=0
for i in range(0,(int)(n)):
avg=avg+(int)(tarou[i])
avg=avg/(int)(n)
cdnt=0
for j in range(0,(int)(n)):
if (int)(tarou[j])... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,608 |
s028835174 | p01109 | u827710398 | 1562316620 | Python | Python3 | py | Accepted | 70 | 6340 | 279 | while True:
n = int(input())
if n == 0 :
break
lst = input().split()
sum = 0
for i in lst:
sum = sum + int(i)
mean = sum / len(lst)
poor = 0
for i in lst:
if mean >= int(i):
poor = poor + 1
print(poor)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,609 |
s644249398 | p01109 | u574566605 | 1562291162 | Python | Python3 | py | Accepted | 50 | 6612 | 248 | def solve(N, A):
m = sum(A) / N
ans = 0
for i in range(N):
if A[i] <= m:
ans += 1
print(ans)
while True:
N = int(input())
if N == 0:
break
A = list(map(int, input().split()))
solve(N, A)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,610 |
s918426544 | p01109 | u529214876 | 1562146726 | Python | Python3 | py | Accepted | 50 | 6604 | 272 | ans_list = []
while True:
n = int(input())
if n==0:
break
A = list(map(int,input().split()))
ave = sum(A) // len(A)
cnt = 0
for a in A:
if a <= ave:
cnt += 1
ans_list.append(cnt)
for ans in ans_list:
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,611 |
s114538575 | p01109 | u114628916 | 1562145486 | Python | Python3 | py | Accepted | 50 | 6620 | 168 | while True:
n = int(input())
if n <= 0: break
a = [int(i) for i in input().split()]
ave = sum(a)/n
print(len(list(filter(lambda x: x <= ave, a))))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,612 |
s754641739 | p01109 | u464321546 | 1562043147 | Python | Python3 | py | Accepted | 50 | 6648 | 195 | import bisect
def main(n):
a = list(map(int, input().split()))
a.sort()
print(bisect.bisect_right(a,sum(a)/n))
while 1:
n = int(input())
if n == 0:
break
main(n)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,613 |
s850204586 | p01109 | u302205058 | 1561945749 | Python | Python3 | py | Accepted | 60 | 6608 | 228 | while True:
n = int(input())
if n == 0:
break
datum = list(map(int, input().split()))
ave = sum(datum) / n
res = 0
for data in datum:
if data <= ave:
res += 1
print(res)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,614 |
s215856234 | p01109 | u760605054 | 1561736005 | Python | Python3 | py | Accepted | 70 | 6616 | 324 | for v in range(10000):
x = 0
n = input()
n = int(n)
if n == 0:
break
else:
a = input().split()
b = [int(s) for s in a]
for i in range(n):
x += b[i]
i += 1
y = x/n
z = 0
for j in range(n):
if y >= b[j]:
z+=1
j += 1
else:
j+=1
print(z... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,615 |
s247978943 | p01109 | u146816547 | 1561698764 | Python | Python3 | py | Accepted | 60 | 6604 | 242 | #!/usr/bin/env python3
while True:
n = int(input())
if n == 0: break
a = list(map(int, input().split()))
ave_num = sum(a) / n
cnt = 0
for item in a:
if item <= ave_num:
cnt += 1
print(cnt)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,616 |
s351368637 | p01109 | u489876484 | 1561531117 | Python | Python3 | py | Accepted | 50 | 6624 | 246 | def main():
while True:
n = int(input())
if not n:
return
aa = list(map(int,input().split()))
ave = sum(aa)/n
print(len([x for x in aa if x <= ave]))
if __name__ == '__main__':
main()
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,617 |
s678319733 | p01109 | u806617625 | 1561104305 | Python | Python3 | py | Accepted | 40 | 6624 | 453 | import sys
def input(): return sys.stdin.readline().strip()
def LIST(): return list(map(int, input().split()))
def main():
ans = []
while 1:
n = int(input())
if n == 0:
break
else:
a = LIST()
heikin = sum(a) / n
tmp = [x for x in a i... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,618 |
s327415871 | p01109 | u449777933 | 1561103793 | Python | Python3 | py | Accepted | 50 | 6608 | 318 | import sys
def input(): return sys.stdin.readline().strip()
def INT(): return int(input())
def LIST(): return list(map(int, input().split()))
ans = []
while 1:
n = INT()
if n == 0:
break
a = LIST()
ave = sum(a)/n
count = 0
for i in a:
if i <= ave:
count += 1
ans.append(count)
for i in ans:
print(i)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,619 |
s461796519 | p01109 | u317583692 | 1561030734 | Python | Python3 | py | Accepted | 50 | 6620 | 237 | while True:
n = int(input())
if n == 0:
exit()
else:
sample = list(map(int,input().split()))
average = sum(sample)/n
cnt = len([1 for i in range(n) if sample[i] <= average])
print(cnt)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,620 |
s862603300 | p01109 | u396086105 | 1560768215 | Python | Python3 | py | Accepted | 40 | 6616 | 324 | import sys
def solve(n):
a = list(map(int, input().split()))
ave = sum(a) / n
count = 0
for i in a:
if i <= ave:
count += 1
print(count)
def main():
while(True):
n = int(input())
if n == 0:
sys.exit(0)
else:
solve(n)
main... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,621 |
s392745283 | p01109 | u508524893 | 1560767351 | Python | Python3 | py | Accepted | 60 | 6612 | 278 | while(True):
n = int(input())
if n==0:
exit()
l = list(map(int,input().split()))
l.sort()
s = sum(l)/n
res = 0
if l[-1]==l[0]:
print(n)
continue
for i in range(n):
if l[i]>s:
print(i)
break
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,622 |
s199047527 | p01109 | u420485708 | 1560767286 | Python | Python3 | py | Accepted | 60 | 6604 | 231 | while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
hei = sum(a)/len(a)
count = 0
for i in a:
if hei >= i:
count += 1
else:
print(count)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,623 |
s414947821 | p01109 | u595029599 | 1560693381 | Python | Python3 | py | Accepted | 60 | 6620 | 166 | while True:
n = int(input())
if not n:
break
a = list(map(int, input().split()))
ave = sum(a) / n
print(len([x for x in a if x <= ave]))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,624 |
s109407375 | p01109 | u808258067 | 1560508873 | Python | Python3 | py | Accepted | 60 | 6604 | 208 | while True:
N = int(input())
if N == 0:
break
s = list(map(int,input().split()))
avg = sum(s) / N
ans = 0
for c in s:
if c <= avg:
ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,625 |
s956240859 | p01109 | u226888928 | 1560504927 | Python | Python3 | py | Accepted | 50 | 6616 | 151 | while True:
n=int(input())
if n == 0: break
xs=list(map(int,input().split()))
ave=sum(xs)/n
print(sum(1 for x in xs if x <= ave))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,626 |
s658953598 | p01109 | u193874819 | 1560173277 | Python | Python3 | py | Accepted | 50 | 6672 | 232 | from itertools import takewhile
while True:
n = int(input())
if n == 0:break
num = sorted(list(map(int, input().split())))
ave = int(sum(num))/int(len(num))
print(len(list(takewhile (lambda x:x <= ave, num))))
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,627 |
s017706605 | p01109 | u788553535 | 1559916563 | Python | Python3 | py | Accepted | 60 | 6608 | 201 | while True:
n = int(input())
if n == 0:
break
a = list(map(int,input().split()))
s = sum(a)/n
a.sort()
ans = 0
for i in range(n):
if a[i]>s:
break
ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,628 |
s173997815 | p01109 | u313600138 | 1559916099 | Python | Python3 | py | Accepted | 50 | 6608 | 208 | while True:
n=int(input())
if n == 0:
break
A = list(map(int,input().split()))
# print(A)
a =sum(A)
bar = a/n
# print(bar)
count=0
for i in A:
if i<=bar:
count+=1
print(count)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,629 |
s984162372 | p01109 | u744506422 | 1559905563 | Python | Python3 | py | Accepted | 60 | 6608 | 202 | while(True):
N=int(input())
if N==0:
break
a=[int(i) for i in input().split()]
S=sum(a)
ans=0
for i in range(N):
if a[i]*N<=S:
ans+=1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,630 |
s806518864 | p01109 | u158979022 | 1558106801 | Python | Python3 | py | Accepted | 50 | 6600 | 182 | while True:
n = int(input())
if n==0:
break
a = list(map(int, input().split()))
ave = int(sum(a) // n)
cnt = 0
for i in a:
if i<=ave:
cnt += 1
print(cnt)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,631 |
s675550539 | p01109 | u418561899 | 1557985082 | Python | Python3 | py | Accepted | 50 | 6612 | 314 | while True:
#1行目nを読み込む
cnt = 0
n =int(input())
#もしn==0ならbreak
if n==0:
break
#n>0ならば
if n>0:
#配列a[i]を読み込む(1<=i<=n)
a = list(map(int, input().split()))
ans = sum(a) //n
for i in range(n):
if a[i] <= ans:
cnt+=1
print(cnt)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,632 |
s287203601 | p01109 | u886122084 | 1557731059 | Python | Python3 | py | Accepted | 50 | 6684 | 328 | import bisect
# python template for atcoder1
import sys
sys.setrecursionlimit(10**9)
input = sys.stdin.readline
def solve():
N=int(input())
if N==0:
exit()
L=list(map(int,input().split()))
ave = sum(L)//N
L = sorted(L)
low = bisect.bisect_right(L,ave)
print(low)
while True:
so... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,633 |
s649985952 | p01109 | u292492602 | 1557201023 | Python | Python3 | py | Accepted | 40 | 6600 | 181 | while True:
n = int(input())
if n == 0:
break
a = list(map(int, input().split()))
s = int(sum(a) // n)
ans = 0
for x in a:
if x <= s:
ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,634 |
s444626134 | p01109 | u853158149 | 1555661496 | Python | Python3 | py | Accepted | 60 | 9140 | 1,298 | #!usr/bin/env python3
from collections import defaultdict
from collections import deque
from heapq import heappush, heappop
import sys
import math
import bisect
import random
def LI(): return list(map(int, sys.stdin.readline().split()))
def I(): return int(sys.stdin.readline())
def LS():return list(map(list, sys.stdin.... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,635 |
s641395813 | p01109 | u048151615 | 1554894788 | Python | Python3 | py | Accepted | 70 | 6340 | 187 | while True :
n=int(input())
if n==0 : break
a=input().split()
tot=0;ans=0
for i in a : tot+=int(i)
for i in a :
if (tot/n)>=int(i) : ans+=1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,636 |
s936025940 | p01109 | u925703181 | 1554730187 | Python | Python3 | py | Accepted | 60 | 6612 | 216 | while True:
n = int(input())
if n == 0:
break
a = list(map(int,input().split()))
ave = sum(a)/n
ans = 0
for i in range(n):
if a[i] <= ave:
ans += 1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,637 |
s788577014 | p01109 | u009332121 | 1553745373 | Python | Python3 | py | Accepted | 50 | 6632 | 466 | def income_inequality():
n = int(input())
low_income_num = []
while n:
incomes = [int(i) for i in input().split()]
sum = 0.0
for i in incomes:
sum += i
everage = sum / len(incomes)
count = 0
for i in incomes:
if i <= everage:
... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,638 |
s166091341 | p01109 | u990228206 | 1552909346 | Python | Python3 | py | Accepted | 50 | 6612 | 174 | while 1:
n=int(input())
if n==0:break
a=list(map(int,input().split()))
aave=sum(a)/len(a)
ans=0
for i in a:
if aave>=i: ans+=1
print(ans)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,639 |
s015962935 | p01109 | u073204138 | 1552564014 | Python | Python3 | py | Accepted | 60 | 6620 | 241 |
def main():
while(1):
a = int(input())
if a == 0:
break
b = list(map(int, input().split()))
mean = sum(b)/a
count=0
for i in range(a):
if mean>= b[i]:
count+=1
print(count)
if __name__ == '__main__':
main()
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,640 |
s129305348 | p01109 | u266648647 | 1549382789 | Python | Python3 | py | Accepted | 70 | 6344 | 252 | n = int(input())
while n != 0:
list = input().split()
for i in range(len(list)):
list[i] = int(list[i])
avgs = sum(list) / n
i = 0
a = 0
for i in range(len(list)):
if list[i] <= avgs:
a = a + 1
print(a)
n = int(input())
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,641 |
s707630804 | p01109 | u717526540 | 1544598558 | Python | Python3 | py | Accepted | 50 | 6608 | 220 | while 1:
n = int(input())
if n == 0:
break
data = list(map(int, input().split()))
mean = sum(data) / n
cnt = 0
for d in data:
if d <= mean:
cnt += 1
print(cnt)
| p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,642 |
s585142993 | p01109 | u509278866 | 1543212749 | Python | Python3 | py | Accepted | 80 | 10076 | 948 | import math,string,itertools,fractions,heapq,collections,re,array,bisect,sys,random,time,copy,functools
sys.setrecursionlimit(10**7)
inf = 10**20
eps = 1.0 / 10**13
mod = 10**9+7
dd = [(-1,0),(0,1),(1,0),(0,-1)]
ddn = [(-1,0),(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1),(-1,-1)]
def LI(): return [int(x) for x in sys.stdin.... | p01109 | <h3>Income Inequality</h3>
<!-- end en only -->
<!-- begin en only -->
<p>
We often compute the average as the first step
in processing statistical data.
Yes, the average is a good tendency measure of data,
but it is not always the best.
In some cases, the average may hinder the understanding
of the data.
</p>
<p>
... | 7
15 15 15 15 15 15 15
4
10 20 30 60
10
1 1 1 1 1 1 1 1 1 100
7
90 90 90 90 90 90 10
7
2 7 1 8 2 8 4
0
| 7
3
9
1
4
| 25,643 |
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