Matplotlib 막대 그래프: plt.bar()와 plt.barh() 완전 가이드

import matplotlib.pyplot as plt
 
categories = ['Python', 'JavaScript', 'Java', 'C++', 'Go']
values = [35, 28, 22, 15, 12]
 
plt.figure(figsize=(8, 5))
plt.bar(categories, values, color='steelblue', edgecolor='black', linewidth=0.5)
plt.xlabel('언어')
plt.ylabel('인기 점수')
plt.title('프로그래밍 언어 인기도')
plt.show()

import matplotlib.pyplot as plt
 
frameworks = ['React', 'Vue.js', 'Angular', 'Svelte', 'Next.js',
              'Django', 'Flask', 'FastAPI', 'Express', 'Rails']
stars = [220, 207, 95, 78, 124, 78, 67, 74, 64, 55]
 
plt.figure(figsize=(10, 6))
plt.barh(frameworks, stars, color='#3498db', edgecolor='white')
plt.xlabel('GitHub 스타 (천)')
plt.title('GitHub 스타 기준 프레임워크 인기도')
plt.gca().invert_yaxis()  # 가장 높은 것을 위로
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
 
categories = ['Q1', 'Q2', 'Q3', 'Q4']
revenue = [120, 150, 180, 200]
 
# 막대마다 다른 색상
colors = ['#e74c3c', '#f39c12', '#2ecc71', '#3498db']
 
plt.figure(figsize=(8, 5))
bars = plt.bar(categories, revenue, color=colors, edgecolor='black', linewidth=0.5)
plt.ylabel('매출 ($K)')
plt.title('분기별 매출')
plt.show()

import matplotlib.pyplot as plt
 
categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 45, 56, 78, 32]
 
plt.figure(figsize=(8, 5))
bars = plt.bar(categories, values, color='steelblue')
 
# 각 막대 위에 값 레이블 추가
for bar, val in zip(bars, values):
    plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 1,
             str(val), ha='center', va='bottom', fontsize=11)
 
plt.ylabel('값')
plt.title('값 레이블이 있는 막대 그래프')
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
categories = ['Q1', 'Q2', 'Q3', 'Q4']
product_a = [20, 35, 30, 35]
product_b = [25, 32, 34, 20]
product_c = [15, 20, 25, 30]
 
x = np.arange(len(categories))
width = 0.25
 
plt.figure(figsize=(10, 6))
plt.bar(x - width, product_a, width, label='제품 A', color='#e74c3c')
plt.bar(x, product_b, width, label='제품 B', color='#3498db')
plt.bar(x + width, product_c, width, label='제품 C', color='#2ecc71')
 
plt.xlabel('분기')
plt.ylabel('매출 ($K)')
plt.title('제품별 분기 매출')
plt.xticks(x, categories)
plt.legend()
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
 
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
mobile = [30, 35, 40, 45]
desktop = [50, 45, 35, 30]
tablet = [20, 20, 25, 25]
 
plt.figure(figsize=(8, 6))
plt.bar(quarters, mobile, label='모바일', color='#3498db')
plt.bar(quarters, desktop, bottom=mobile, label='데스크톱', color='#e74c3c')
 
# 세 번째 누적을 위해 누적 bottom 계산
import numpy as np
bottom_2 = np.array(mobile) + np.array(desktop)
plt.bar(quarters, tablet, bottom=bottom_2, label='태블릿', color='#2ecc71')
 
plt.ylabel('트래픽 (%)')
plt.title('분기별 트래픽 소스')
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
categories = ['대조군', '처리 A', '처리 B', '처리 C']
means = [10.2, 15.8, 14.3, 18.1]
errors = [1.2, 2.1, 1.8, 2.5]
 
plt.figure(figsize=(8, 5))
plt.bar(categories, means, yerr=errors, capsize=5,
        color='steelblue', edgecolor='black', linewidth=0.5)
plt.ylabel('응답 값')
plt.title('오차 막대가 있는 처리 효과')
plt.show()

import pandas as pd
import pygwalker as pyg
 
df = pd.DataFrame({
    'Quarter': ['Q1', 'Q2', 'Q3', 'Q4'] * 3,
    'Product': ['A'] * 4 + ['B'] * 4 + ['C'] * 4,
    'Sales': [20, 35, 30, 35, 25, 32, 34, 20, 15, 20, 25, 30]
})
 
walker = pyg.walk(df)