# data[].attributes.headline
홍요한 세계 음식물 쓰레기 데이터 

# data[].attributes.subject
홍요한 세계 음식물 쓰레기 데이터 

# data[].attributes.headline
전 세계 음식물 쓰래기 분석  완성본 1423 홍요한

# data[].attributes.subject
전 세계 음식물 쓰래기 분석  완성본 1423 홍요한

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""Untitled5.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1qR9rhIktgcP4WXKYCDMYx9INcDbgejIL
"""

from google.colab import files
uploaded = files.upload()

# data[].attributes.headline
10415 정성찬 음식물 쓰레기 현황

# data[].attributes.subject
10415 정성찬 음식물 쓰레기 현황

# data[].attributes.attachment_link.title
전세계_음식물스레기_현황_분석(2)

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""20251119 ���������������������������

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1SVFSUq3LCOh8NZ9lAAm39VrozfyMeWml
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_csv('temp.csv')
df.head()

df.info()

df = df.rename(columns = {'2019':'PC2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

df1 = df1.sort_values("PC2019", ascending = False)
df1.head()

import matplotlib.pyplot as plt
plt.figure(figsize = (20, 30))
plt.barh(df1["GeoAreaName"], df1["PC2019"])
plt.xlabel("PC2019")
plt.ylabel("GeoAreaName")
plt.show()

df2 = df[df["Food Waste Sector"].isin(["HHS", "OOHC", "RTL"])]
df2.head()

korea= df2[df2["GeoAreaName"] == "Republic of Korea"]
korea

import matplotlib.pyplot as plt
label = ["HHS", "OOHC", "RTL"]
plt.pie(korea["PC2019"], labels = label, autopct = "%.2f%%")
plt.show()

# data[].attributes.headline
1419 정은샘 전 세계 음식물 쓰레기 현황 분석하기2

# data[].attributes.subject
1419 정은샘 전 세계 음식물 쓰레기 현황 분석하기2

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""20251119 ���������

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1xLMw0uBK1nhsFk6BwdJwUciYX7-lXZpZ
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_csv('yesyes.csv')
df.head()

df.info()

df = df.rename(columns = {'2019':'PC2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

df1 = df1.sort_values("PC2019", ascending = False)
df1.head()

import matplotlib.pyplot as plt
plt.figure(figsize = (20, 30))
plt.barh(df1["GeoAreaName"], df1["PC2019"])
plt.xlabel("PC2019")
plt.ylabel("GeoAreaName")
plt.show()

df2 = df[df["Food Waste Sector"].isin(["HHS", "OOHC", "RTL"])]
df2.head()

korea= df2[df2["GeoAreaName"] == "Republic of Korea"]
korea

import matplotlib.pyplot as plt
label = ["HHS", "OOHC", "RTL"]
plt.pie(korea["PC2019"], labels = label, autopct = "%.2f%%")
plt.show()

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""20251119 ���������

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1SVFSUq3LCOh8NZ9lAAm39VrozfyMeWml
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_csv('yesyes.csv')
df.head()

df.info()

df = df.rename(columns = {'2019':'PC2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

df1 = df1.sort_values("PC2019", ascending = False)
df1.head()

import matplotlib.pyplot as plt
plt.figure(figsize = (20, 30))
plt.barh(df1["GeoAreaName"], df1["PC2019"])
plt.xlabel("PC2019")
plt.ylabel("GeoAreaName")
plt.show()

df2 = df[df["Food Waste Sector"].isin(["HHS", "OOHC", "RTL"])]
df2.head()

korea= df2[df2["GeoAreaName"] == "Republic of Korea"]
korea

import matplotlib.pyplot as plt
label = ["HHS", "OOHC", "RTL"]
plt.pie(korea["PC2019"], labels = label, autopct = "%.2f%%")
plt.show()

# data[].attributes.headline
음식물 쓰래기1423홍요한

# data[].attributes.subject
음식물 쓰래기1423홍요한

# data[].attributes.headline
1421채주연 전 세계 음식물 쓰레기 현황 분석하기

# data[].attributes.subject
1421채주연 전 세계 음식물 쓰레기 현황 분석하기

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""2025.11.171421��������� ��� ������ ��������� ��������� ������ ������������

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1mQowI0W6o3BnvWP6HzQmaTkKX40Jypq8
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_excel('AG_FOOD_WST_PC.xlsx')
df.head()

df.info()

df = df.rename(columns = {'2019':'pc2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

df1 = df1.rename(columns={'2019':'pc2019'})
df1 = df1.sort_values("pc2019", ascending = False)
df1.head()

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""��������� ������������������ ������ ������.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1JfQiadWBxtlRtb7BjnaEMPvMAmQNpqOV

#Memento Mori
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_excel('������������.xlsx')
df.head()

df.info()

df = df.rename(columns = {'2019' : 'PC2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

df1 = df1.sort_values("PC2019", ascending = False)
df1.head()

import matplotlib.pyplot as plt
plt.figure(figsize = (20, 30))
plt.barh(df1["GeoAreaName"], df1["PC2019"])
plt.xlabel("PC2019")
plt.ylabel("GeoAreaName")
plt.show()

from google.colab import files
upload = files.upload()

from PIL import Image
img = Image.open("Memento_Mori.webp")
print(img.size)
print(img.mode)
img

# data[].attributes.attachment_link.title
전세계_음식물스레기_현황_분석

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""1412���������

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1NRomwJt85LLV3W-NccakIY1Kc0bofw32
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_excel('AG.xlsx')
df .head()

df .info()

df = df.rename(columns = {'2019':'PC2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

df1 = df1.sort_values("PC2019", ascending = False)
df1.head()

import matplotlib.pyplot as plt
plt.figure(figsize = (20, 30))
plt.barh(df1["GeoAreaName"], df1["PC2019"])
plt.xlabel("PC2019")
plt.ylabel("GeoAreName")
plt.show()

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""1416

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1ehLweO9-3bEgul7xC_XSyYbKXezjmIiv
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_excel('1416.xlsx')
df.head()

df = df.rename(columns = {'2019':'PC2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

# data[].attributes.headline
1413 윤지원 음식물 쓰레기 현황 분석

# data[].attributes.subject
1413 윤지원 음식물 쓰레기 현황 분석

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""20251117 ���������

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1cR1BpoeUQSFVnMQZ8GRyhd5_MFT8i7Nu
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_csv('YUMMY.csv')
df.head()

df.info()

df = df.rename(columns = {'2019':'PC2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

df1 = df1.sort_values("PC2019", ascending = False)
df1.head()

import matplotlib.pyplot as plt
plt.figure(figsize = (20, 30))
plt.barh(df1["GeoAreaName"], df1["PC2019"])
plt.xlabel("PC2019")
plt.ylabel("GeoAreaName")
plt.show()

df2 = df[df["Food Waste Sector"].isin(["HHS", "OOHC", "RTL"])]
df2.head()

korea= df2[df2["GeoAreaName"] == "Republic of Korea"]
korea

import matplotlib.pyplot as plt
label = ["HHS", "OOHC", "RTL"]
plt.pie(korea["PC2019"], labels = label, autopct = "%.1f%%")
plt.show()

# data[].attributes.headline
1407 문지현  전세계음식물쓰레기현황분석하기

# data[].attributes.subject
1407 문지현  전세계음식물쓰레기현황분석하기

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""20251117 ���������

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1cR1BpoeUQSFVnMQZ8GRyhd5_MFT8i7Nu
"""

from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_csv('YUMMY.csv')
df.head()

df.info()

df = df.rename(columns = {'2019':'PC2019'})
df.head()

df1 = df[df['Food Waste Sector'] == 'ALL']
df1.head()

df1 = df1.sort_values("PC2019", ascending = False)
df1.head()

import matplotlib.pyplot as plt
plt.figure(figsize = (20, 30))
plt.barh(df1["GeoAreaName"], df1["PC2019"])
plt.xlabel("PC2019")
plt.ylabel("GeoAreaName")
plt.show()

df2 = df[df["Food Waste Sector"].isin(["HHS", "OOHC", "RTL"])]
df2.head()

korea= df2[df2["GeoAreaName"] == "Republic of Korea"]
korea

import matplotlib.pyplot as plt
label = ["HHS", "OOHC", "RTL"]
plt.pie(korea["PC2019"], labels = label, autopct = "%.1f%%")
plt.show()

# data[].attributes.headline
1419 정은샘 전 세계 음식물 쓰레기 현황 분석하기

# data[].attributes.subject
1419 정은샘 전 세계 음식물 쓰레기 현황 분석하기

# data[].attributes.headline
우리 동네 기상 정보 분석하기 수업자료

# data[].attributes.subject
우리 동네 기상 정보 분석하기 수업자료

# data[].attributes.headline
이미지 처리 프로그램 수업 자료

# data[].attributes.subject
이미지 처리 프로그램 수업 자료

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""2025.11.10 1421��������� ������ ������ ������

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/15iKeLNBEBgfbzOAiPhFoaBWkbMNSps52
"""

from google.colab import files
upload = files.upload()

import pandas as pd
df = pd.read_csv("temp.csv")
print(df.head())

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""Untitled5.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1qR9rhIktgcP4WXKYCDMYx9INcDbgejIL
"""

from google.colab import files
uploaded = files.upload()

# data[].attributes.attachment_link.readable_text
import sys

sys.setrecursionlimit(2000)
MOD = 1000000007
N = 0
A = 0
DP_SIZE_N = 1001
DP_SIZE_DIFF = 10
DP_SIZE_BEF = 10
DP_SIZE_CNT = 10

dp = [
    [
        [
            [-1] * DP_SIZE_CNT for _ in range(DP_SIZE_BEF)
        ] for _ in range(DP_SIZE_DIFF)
    ] for _ in range(DP_SIZE_N)
]

def solve(Idx, diff, bef, cnt):
    if Idx > N:
        return 1 if cnt == A else 0
    if cnt >= DP_SIZE_CNT:
        return 0
    ret = dp[Idx][diff][bef][cnt]
    if ret != -1:
        return ret
    ret = 0
    for i in range(bef, 10):
        new_diff = i - bef
        if diff == 9:
            ret += solve(Idx + 1, new_diff, i, cnt)
        else:
            if new_diff != diff:
                ret += solve(Idx + 1, 9, i, cnt + 1)
            else:
                ret += solve(Idx + 1, diff, i, cnt)
        if ret >= MOD:
            ret -= MOD
    dp[Idx][diff][bef][cnt] = ret
    return ret

def main():
    global N, A
    try:
        data = sys.stdin.read().split()
        if len(data) < 2:
            return
        N = int(data[0])
        A = int(data[1])
    except:
        return
    if N == 1:
        if A == 1:
            print(9)
        else:
            print(0)
        return
    ans = 0
    for i in range(1, 10):
        for j in range(i, 10):
            ans += solve(3, j - i, j, 1)
            
            if ans >= MOD:
                ans -= MOD
    print(ans)

if __name__ == "__main__":
    main()

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""Untitled4.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1SHwB6vaTc68lHHJOrF2DRf4rd7LjMZcZ
"""

import matplotlib.pyplot as plt
item = ["mobile", "delivery", "trip", "shopping"]
cost = [75000, 138450, 94800, 100500]
plt.pie(cost, labels = item, autopct = "%.0f%%")
plt.show()

from google.colab import files
upload = files.upload()

import pandas as pd
df = pd.read_csv("temp.csv")
print(df.head())

# data[].attributes.headline
1420 조영재 기온측정

# data[].attributes.subject
1420 조영재 기온측정

# data[].attributes.attachment_link.title
동네기온측정_1420_조영재

# data[].attributes.headline
1407 우리 동네 기온 현황 분석하기

# data[].attributes.subject
1407 우리 동네 기온 현황 분석하기

# data[].attributes.headline
1419 정은샘 우리 동네 기온 현황 분석하기

# data[].attributes.subject
1419 정은샘 우리 동네 기온 현황 분석하기

# data[].attributes.attachment_link.readable_text
# -*- coding: utf-8 -*-
"""Untitled3.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/15lF0qwkjLFVUb0J0yMNDIcxEOzIqff4b
"""

def f() :
     print("��������������� ���������������.")

f()

def f() :
     a = "dkssudgktpdy"
     return a

b = f()
print(b)

d f(a) :
  print(a)

f(3)

def f(a) :
   x = a + 3
   return x

c = 3
b = f(c)
print(b)

count = 20
def func() :
  count = 10
  print("������ ������ ������:", count )

func()
print("������ ������ ������:", count)

import math
print(math.gcd(1109309432,349249372))

import math
print(math.sqrt(144))

import matplotlib. pyplot as plt
item = ["mobile", "delivery", "trip", "shiopping"]
cost = [77000, 138450, 94800, 100500]
plt.pie(cost, labels = item, autopct = "%.2f%%")
plt.show

# data[].attributes.headline
1420 조영재 함수와 라이브러

# data[].attributes.subject
1420 조영재 함수와 라이브러

# data[].attributes.attachment_link.title
함수와_라이브러리_1420조영재

# data[].attributes.body
세계유명대학(하버드, 상하이, 서울대 등)

중 단 하나의 대학도 못 푼 문제

# data[].attributes.attachment_link.readable_text
import pulp

def main():
    s, r, d = map(int, input().split())

    out_ducts = [[] for _ in range(s + 1)]
    in_contrib = [[] for _ in range(s + r + 1)]

    for k in range(d):
        parts = list(map(int, input().split()))
        i = parts[0]
        n = parts[1]
        out_ducts[i].append(k)
        for j in range(n):
            o = parts[2 + 2*j]
            p = parts[3 + 2*j]
            in_contrib[o].append((k, p / 100.0))

    prob = pulp.LpProblem("Flubber", pulp.LpMaximize)
    f = pulp.LpVariable("f", lowBound=0)
    x = {k: pulp.LpVariable(f"x_{k}", lowBound=0) for k in range(d)}
    flow_in = {j: pulp.LpVariable(f"fin_{j}", lowBound=0) for j in range(2, s + r + 1)}

    prob += f

    for i in range(1, s + 1):
        out_expr = pulp.lpSum(x[k] for k in out_ducts[i])
        if i == 1:
            prob += out_expr == 100
        else:
            prob += out_expr == flow_in[i]

    for j in range(2, s + r + 1):
        in_expr = pulp.lpSum(coeff * x[k] for k, coeff in in_contrib[j])
        prob += flow_in[j] == in_expr

    for k in range(s + 1, s + r + 1):
        prob += flow_in[k] >= f

    # frac ������ ��� msg=0������ ������ ������
    solver = pulp.PULP_CBC_CMD(msg=0)
    prob.solve(solver)

    val = pulp.value(f)
    if val is None:
        # ������ ��������� ������ ������ ��������� ������ ������ ������
        print("0.0000000000")
    else:
        print(f"{round(val, 10):.10f}")

if __name__ == "__main__":
    main()

# data[].attributes.body
1

# data[].attributes.headline
10415 정성찬 제어구조의 응용 추가

# data[].attributes.subject
10415 정성찬 제어구조의 응용 추가

# data[].attributes.headline
10415 정성찬 제어구조의 응용

# data[].attributes.subject
10415 정성찬 제어구조의 응용

# data[].attributes.headline
1421채주연 2차원 리스트

# data[].attributes.subject
1421채주연 2차원 리스트

# data[].attributes.headline
다차원 리스트 홍요한 1423

# data[].attributes.subject
다차원 리스트 홍요한 1423

# data[].attributes.headline
1420 조영재 다차원 리스트

# data[].attributes.subject
1420 조영재 다차원 리스트

# data[].attributes.headline
10415 정성찬 다차원 데이터 구

# data[].attributes.subject
10415 정성찬 다차원 데이터 구