Quarto 레이아웃 포괄적 테스트

1. 기본 레이아웃 테스트

1.1 단일 컬럼 레이아웃

기본적인 단일 컬럼 레이아웃입니다. 이 섹션은 전체 너비를 사용합니다.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(10, 4))
plt.plot(x, y)
plt.title('기본 플롯')
plt.xlabel('X축')
plt.ylabel('Y축')
plt.grid(True)
plt.show()

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1.2 다중 컬럼 레이아웃

import pandas as pd

data = {
    '이름': ['김철수', '이영희', '박민수'],
    '나이': [25, 30, 28],
    '점수': [85, 92, 88]
}
df = pd.DataFrame(data)
print(df)
plt.figure(figsize=(5, 4))
plt.bar(df['이름'], df['점수'], color='skyblue')
plt.title('점수 분포')
plt.ylabel('점수')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

왼쪽 컬럼

이것은 왼쪽 컬럼의 내용입니다. 두 개의 컬럼으로 나뉘어 표시됩니다.

    이름  나이  점수
0  김철수  25  85
1  이영희  30  92
2  박민수  28  88

오른쪽 컬럼

이것은 오른쪽 컬럼의 내용입니다. 같은 행에 나란히 배치됩니다.

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2. 고급 레이아웃 구성

2.1 사용자 정의 비율 레이아웃

작은 영역

작은 영역 (1/3)

큰 영역

큰 영역 (2/3)

전체 너비 영역

이 영역은 전체 너비를 사용합니다.

2.2 3열 레이아웃

열 1

첫 번째 열의 내용

열 2

두 번째 열의 내용

열 3

세 번째 열의 내용

2.3 복잡한 중첩 레이아웃

import seaborn as sns

# 데이터 생성
np.random.seed(42)
data = np.random.randn(100)

plt.figure(figsize=(10, 3))
plt.hist(data, bins=30, color='coral', alpha=0.7)
plt.title('히스토그램 - 전체 너비')
plt.xlabel('값')
plt.ylabel('빈도')
plt.show()
plt.figure(figsize=(5, 4))
plt.scatter(np.random.rand(50), np.random.rand(50), c='green', alpha=0.5)
plt.title('산점도 1')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
plt.figure(figsize=(5, 4))
plt.scatter(np.random.rand(50), np.random.rand(50), c='purple', alpha=0.5)
plt.title('산점도 2')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

상단 전체 너비

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좌측 하단

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우측 하단

하단 전체 너비

이것은 하단 전체 너비 섹션입니다.

3. 그림 레이아웃 테스트

3.1 나란히 배치된 그림

plt.figure(figsize=(5, 4))
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x), 'b-', label='sin(x)')
plt.legend()
plt.title('사인 함수')
plt.grid(True)
plt.show()
plt.figure(figsize=(5, 4))
plt.plot(x, np.cos(x), 'r-', label='cos(x)')
plt.legend()
plt.title('코사인 함수')
plt.grid(True)
plt.show()

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왼쪽 그림

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오른쪽 그림

3.2 다양한 크기의 그림

plt.figure(figsize=(8, 4))
t = np.linspace(0, 10, 1000)
signal = np.sin(2*np.pi*t) + 0.5*np.sin(4*np.pi*t)
plt.plot(t, signal, 'navy')
plt.title('복합 신호')
plt.xlabel('시간')
plt.ylabel('진폭')
plt.grid(True, alpha=0.3)
plt.show()
plt.figure(figsize=(4, 4))
sizes = [30, 25, 20, 15, 10]
labels = ['A', 'B', 'C', 'D', 'E']
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title('파이 차트')
plt.show()

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큰 그림 (2/3 너비)

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작은 그림 (1/3 너비)

4. 특수 레이아웃 기능

4.1 사이드바 레이아웃

여백 주석

이것은 여백에 표시되는 내용입니다. 추가 정보나 주석을 제공할 수 있습니다.

본문 내용이 여기에 표시되며, 오른쪽 여백에 추가 정보가 나타납니다.

4.2 전체 화면 너비 사용

fig, axes = plt.subplots(1, 4, figsize=(16, 4))

for i, ax in enumerate(axes):
    x = np.linspace(0, 10, 100)
    y = np.sin(x + i)
    ax.plot(x, y)
    ax.set_title(f'서브플롯 {i+1}')
    ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

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페이지 전체 너비 그림

4.3 화면 전체 확장

이 섹션은 화면 전체 너비를 사용합니다. 매우 넓은 표나 시각화에 유용합니다.

항목1	항목2	항목3	항목4	항목5	항목6	항목7	항목8	항목9	항목10
A	B	C	D	E	F	G	H	I	J
1	2	3	4	5	6	7	8	9	10
α	β	γ	δ	ε	ζ	η	θ	ι	κ

5. 패널 레이아웃

5.1 탭셋 레이아웃

파이썬 코드

def fibonacci(n):
    """피보나치 수열 생성"""
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)

# 처음 10개 출력
fib_numbers = [fibonacci(i) for i in range(10)]
print("피보나치 수열:", fib_numbers)

피보나치 수열: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

시각화

plt.figure(figsize=(10, 4))
plt.plot(range(10), fib_numbers, marker='o', linestyle='-', color='darkgreen')
plt.title('피보나치 수열 시각화')
plt.xlabel('인덱스')
plt.ylabel('값')
plt.grid(True, alpha=0.3)
plt.show()

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설명

피보나치 수열은 각 항이 앞의 두 항의 합인 수열입니다. 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, …

5.2 접을 수 있는 섹션

Note추가 정보 보기

이것은 접을 수 있는 콜아웃 박스입니다. 클릭하면 내용이 표시됩니다.

# 간단한 통계 계산
data = np.random.normal(100, 15, 1000)
print(f"평균: {np.mean(data):.2f}")
print(f"표준편차: {np.std(data):.2f}")
print(f"최솟값: {np.min(data):.2f}")
print(f"최댓값: {np.max(data):.2f}")

평균: 100.70
표준편차: 14.89
최솟값: 51.38
최댓값: 146.18

6. 반응형 레이아웃

6.1 조건부 레이아웃

셀 1

내용 1

셀 2

내용 2

셀 3

내용 3

셀 4

내용 4

6.2 복합 그리드

plt.figure(figsize=(3, 3))
plt.plot([1,2,3], [1,4,9], 'ro-')
plt.title('그림 1')
plt.show()
plt.figure(figsize=(3, 3))
plt.plot([1,2,3], [1,2,3], 'bs-')
plt.title('그림 2')
plt.show()
plt.figure(figsize=(3, 3))
plt.plot([1,2,3], [3,2,1], 'g^-')
plt.title('그림 3')
plt.show()
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot([1,2,3], [1,4,9], 'ro-', label='데이터 1')
ax.plot([1,2,3], [1,2,3], 'bs-', label='데이터 2')
ax.plot([1,2,3], [3,2,1], 'g^-', label='데이터 3')
ax.set_title('세 데이터셋 비교')
ax.legend()
ax.grid(True, alpha=0.3)
plt.show()

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그림 1

그림 2

그림 3

하단 전체 너비 요약

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종합 비교

7. 테이블 레이아웃 테스트

7.1 기본 마크다운 테이블

이름	나이	직업	거주지
김철수	28	개발자	서울
이영희	32	디자이너	부산
박민수	25	기획자	대전
최지현	30	마케터	인천

7.2 정렬된 테이블

왼쪽 정렬	가운데 정렬	오른쪽 정렬	숫자
텍스트 A	텍스트 B	텍스트 C	100
긴 텍스트 항목	중간	짧음	1,234
Short	Medium Length	Very Long Text	56.78

7.3 Pandas 데이터프레임 테이블

import pandas as pd
import numpy as np

# 샘플 데이터 생성
np.random.seed(42)
df_sales = pd.DataFrame({
    '제품': ['노트북', '마우스', '키보드', '모니터', '헤드셋'],
    '1월': np.random.randint(100, 500, 5),
    '2월': np.random.randint(100, 500, 5),
    '3월': np.random.randint(100, 500, 5),
    '합계': [0] * 5
})
df_sales['합계'] = df_sales[['1월', '2월', '3월']].sum(axis=1)

print(df_sales.to_markdown(index=False))

| 제품   |   1월 |   2월 |   3월 |   합계 |
|:-------|------:|------:|------:|-------:|
| 노트북 |   202 |   288 |   430 |    920 |
| 마우스 |   448 |   120 |   187 |    755 |
| 키보드 |   370 |   202 |   472 |   1044 |
| 모니터 |   206 |   221 |   199 |    626 |
| 헤드셋 |   171 |   314 |   459 |    944 |

7.4 다중 컬럼 테이블 배치

왼쪽 테이블

과목	점수
국어	85
영어	92
수학	78

오른쪽 테이블

요일	온도(°C)
월	15
화	18
수	20

7.5 복잡한 Pandas 테이블

# 더 복잡한 데이터프레임
df_complex = pd.DataFrame({
    '지역': ['서울', '경기', '인천', '부산', '대구'],
    '인구(만명)': [967, 1356, 296, 339, 242],
    '면적(km²)': [605, 10171, 1063, 770, 884],
    'GDP(조원)': [418, 529, 79, 92, 60]
})

# 인구 밀도 계산
df_complex['인구밀도'] = (df_complex['인구(만명)'] * 10000 / df_complex['면적(km²)']).round(1)

# 1인당 GDP 계산 (백만원)
df_complex['1인당GDP'] = (df_complex['GDP(조원)'] * 1000 / df_complex['인구(만명)']).round(2)

print(df_complex.to_markdown(index=False))

| 지역   |   인구(만명) |   면적(km²) |   GDP(조원) |   인구밀도 |   1인당GDP |
|:-------|-------------:|------------:|------------:|-----------:|-----------:|
| 서울   |          967 |         605 |         418 |    15983.5 |     432.26 |
| 경기   |         1356 |       10171 |         529 |     1333.2 |     390.12 |
| 인천   |          296 |        1063 |          79 |     2784.6 |     266.89 |
| 부산   |          339 |         770 |          92 |     4402.6 |     271.39 |
| 대구   |          242 |         884 |          60 |     2737.6 |     247.93 |

7.6 스타일이 적용된 테이블

# 조건부 서식이 적용된 데이터프레임 생성
df_scores = pd.DataFrame({
    '학생': ['김A', '이B', '박C', '최D', '정E'],
    '국어': [85, 92, 78, 88, 95],
    '영어': [90, 85, 92, 78, 88],
    '수학': [88, 90, 85, 92, 82],
    '과학': [92, 88, 90, 85, 90]
})

df_scores['평균'] = df_scores[['국어', '영어', '수학', '과학']].mean(axis=1).round(1)
df_scores['등급'] = pd.cut(df_scores['평균'], 
                          bins=[0, 70, 80, 90, 100], 
                          labels=['D', 'C', 'B', 'A'])

print(df_scores.to_markdown(index=False))

| 학생   |   국어 |   영어 |   수학 |   과학 |   평균 | 등급   |
|:-------|-------:|-------:|-------:|-------:|-------:|:-------|
| 김A    |     85 |     90 |     88 |     92 |   88.8 | B      |
| 이B    |     92 |     85 |     90 |     88 |   88.8 | B      |
| 박C    |     78 |     92 |     85 |     90 |   86.2 | B      |
| 최D    |     88 |     78 |     92 |     85 |   85.8 | B      |
| 정E    |     95 |     88 |     82 |     90 |   88.8 | B      |

7.7 전체 너비 테이블

# 넓은 테이블 생성
df_wide = pd.DataFrame({
    '항목': ['제품A', '제품B', '제품C', '제품D', '제품E'],
    '1월': np.random.randint(1000, 5000, 5),
    '2월': np.random.randint(1000, 5000, 5),
    '3월': np.random.randint(1000, 5000, 5),
    '4월': np.random.randint(1000, 5000, 5),
    '5월': np.random.randint(1000, 5000, 5),
    '6월': np.random.randint(1000, 5000, 5),
    '7월': np.random.randint(1000, 5000, 5),
    '8월': np.random.randint(1000, 5000, 5),
    '9월': np.random.randint(1000, 5000, 5),
    '10월': np.random.randint(1000, 5000, 5),
    '11월': np.random.randint(1000, 5000, 5),
    '12월': np.random.randint(1000, 5000, 5),
})

df_wide['연간합계'] = df_wide.iloc[:, 1:].sum(axis=1)
print(df_wide.to_markdown(index=False))

| 항목   |   1월 |   2월 |   3월 |   4월 |   5월 |   6월 |   7월 |   8월 |   9월 |   10월 |   11월 |   12월 |   연간합계 |
|:-------|------:|------:|------:|------:|------:|------:|------:|------:|------:|-------:|-------:|-------:|-----------:|
| 제품A  |  4735 |  3391 |  2215 |  4385 |  4632 |  3047 |  1189 |  2899 |  4202 |   3888 |   3363 |   4417 |      42363 |
| 제품B  |  1130 |  2515 |  1955 |  1021 |  1474 |  4547 |  4005 |  4638 |  4556 |   3214 |   3061 |   3612 |      35728 |
| 제품C  |  2685 |  4485 |  3324 |  3300 |  2082 |  3747 |  3734 |  2267 |  4890 |   2297 |   1241 |   3945 |      37997 |
| 제품D  |  4380 |  3853 |  2184 |  1747 |  3558 |  1975 |  4005 |  3879 |  1646 |   3435 |   3041 |   2363 |      36066 |
| 제품E  |  1769 |  3433 |  1459 |  3904 |  4753 |  2806 |  1562 |  2528 |  3068 |   1600 |   3824 |   3139 |      33845 |

7.8 화면 전체 너비 테이블

# 매우 넓은 테이블
months = ['1월', '2월', '3월', '4월', '5월', '6월', '7월', '8월', '9월', '10월', '11월', '12월']
df_full = pd.DataFrame({
    '제품명': ['노트북 Pro 15"', '무선마우스 MX3', '기계식키보드 RGB', '27" 모니터 4K', 'USB-C 허브'],
})

for month in months:
    df_full[month] = np.random.randint(50, 200, 5)

df_full['Q1'] = df_full[['1월', '2월', '3월']].sum(axis=1)
df_full['Q2'] = df_full[['4월', '5월', '6월']].sum(axis=1)
df_full['Q3'] = df_full[['7월', '8월', '9월']].sum(axis=1)
df_full['Q4'] = df_full[['10월', '11월', '12월']].sum(axis=1)
df_full['연간총계'] = df_full[['Q1', 'Q2', 'Q3', 'Q4']].sum(axis=1)

print(df_full.to_markdown(index=False))

| 제품명           |   1월 |   2월 |   3월 |   4월 |   5월 |   6월 |   7월 |   8월 |   9월 |   10월 |   11월 |   12월 |   Q1 |   Q2 |   Q3 |   Q4 |   연간총계 |
|:-----------------|------:|------:|------:|------:|------:|------:|------:|------:|------:|-------:|-------:|-------:|-----:|-----:|-----:|-----:|-----------:|
| 노트북 Pro 15"   |   160 |   153 |   155 |    63 |   131 |    90 |   120 |   112 |   172 |    121 |     86 |    180 |  468 |  284 |  404 |  387 |       1543 |
| 무선마우스 MX3   |    57 |   181 |    53 |   144 |   160 |    64 |    58 |   188 |    54 |     61 |    148 |     50 |  291 |  368 |  300 |  259 |       1218 |
| 기계식키보드 RGB |    84 |    51 |   103 |    97 |   102 |    94 |   137 |   130 |    90 |     82 |    153 |     54 |  238 |  293 |  357 |  289 |       1177 |
| 27" 모니터 4K    |   130 |   183 |   195 |    64 |    73 |   114 |   178 |   185 |    77 |     97 |     84 |    191 |  508 |  251 |  440 |  372 |       1571 |
| USB-C 허브       |    99 |   103 |    93 |    89 |   173 |   138 |   185 |    82 |   184 |    111 |    150 |    152 |  295 |  400 |  451 |  413 |       1559 |

7.9 테이블과 그래프 조합

# 요약 통계 테이블
summary_data = pd.DataFrame({
    '지표': ['평균', '중앙값', '표준편차', '최솟값', '최댓값'],
    '값': [
        df_scores['평균'].mean(),
        df_scores['평균'].median(),
        df_scores['평균'].std(),
        df_scores['평균'].min(),
        df_scores['평균'].max()
    ]
})
summary_data['값'] = summary_data['값'].round(2)
print(summary_data.to_markdown(index=False))
plt.figure(figsize=(6, 4))
plt.barh(df_scores['학생'], df_scores['평균'], color='steelblue')
plt.xlabel('평균 점수')
plt.ylabel('학생')
plt.title('학생별 평균 성적')
plt.xlim(0, 100)
plt.grid(axis='x', alpha=0.3)
plt.tight_layout()
plt.show()

지표	값
평균	87.68
중앙값	88.8
표준편차	1.54
최솟값	85.8
최댓값	88.8

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학생별 평균 점수

7.10 피벗 테이블

# 피벗 테이블 예제
np.random.seed(123)
sales_data = pd.DataFrame({
    '지역': ['서울', '서울', '부산', '부산', '대구', '대구'] * 4,
    '분기': ['Q1', 'Q2', 'Q1', 'Q2', 'Q1', 'Q2'] * 4,
    '제품': ['A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B',
             'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B'],
    '매출': np.random.randint(100, 500, 24)
})

pivot_table = sales_data.pivot_table(
    values='매출',
    index='지역',
    columns=['제품', '분기'],
    aggfunc='sum',
    fill_value=0
)

print(pivot_table.to_markdown())

| 지역   |   ('A', 'Q1') |   ('A', 'Q2') |   ('B', 'Q1') |   ('B', 'Q2') |
|:-------|--------------:|--------------:|--------------:|--------------:|
| 대구   |           503 |           249 |           667 |           521 |
| 부산   |           648 |           345 |           662 |           591 |
| 서울   |           661 |           695 |           507 |           417 |

8. 결론

이 문서는 Quarto의 다양한 레이아웃 기능을 테스트했습니다:

• ✓ 단일/다중 컬럼 레이아웃
• ✓ 사용자 정의 비율 레이아웃
• ✓ 중첩된 복잡한 레이아웃
• ✓ 그림 배치 및 크기 조정
• ✓ 여백 및 전체 너비 활용
• ✓ 탭셋 및 접을 수 있는 요소
• ✓ 반응형 그리드 시스템
• ✓ 다양한 형태의 테이블 레이아웃
• ✓ 테이블과 그래프 조합
• ✓ 피벗 테이블 및 복잡한 데이터 표현