Python数据可视化中常见的4种标注方式及示例详解

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
sales = [100, 120, 90, 150, 200, 230, 210, 190, 220, 240, 250, 270]

plt.plot(months, sales, marker='o')
plt.title('Monthly Sales Data')
plt.xlabel('Month')
plt.ylabel('Sales')

# adding text annotations
plt.text('May', 200, 'Product Launch', fontsize=9, ha='center', color='red')
plt.text('Nov', 250,

marketing_spend = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
sales = [12, 25, 27, 35, 50, 52, 60, 65, 78, 85]

plt.scatter(marketing_spend, sales)
plt.xlabel('Marketing Spend (in $1000)')
plt.ylabel('Sales (in $1000)')

# adding arrow annotations
plt.annotate('High ROI', xy=(20, 25), xytext=(30, 40), arrowprops=dict(facecolor='blue', shrink=0.05))
plt.annotate('Low ROI', xy=(60, 52), xytext=(60, 90), arrowprops=dict(facecolor='red', shrink=0.05))

plt.show()

import numpy as np

dates = np.arange('2023-01', '2024-01', dtype='datetime64[M]')
stock_prices = np.random.randn(len(dates)).cumsum() + 100

plt.plot(dates, stock_prices)
plt.title('Stock Prices Over Time')
plt.xlabel('Date')
plt.ylabel('Price')

# highlighting an area
plt.axvspan('2023-06', '2023-09', color='yellow', alpha=0.3, label='Summer Period')

plt.legend()

study_hours = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
scores = np.array([50, 55, 60, 65, 70, 75, 80, 85, 90, 95])

plt.scatter(study_hours, scores)
plt.title('Study Hours vs Exam Scores')
plt.xlabel('Study Hours')
plt.ylabel('Scores')

# adding a trend line
m, b = np.polyfit(study_hours, scores, 1)
plt.plot(study_hours, m*study_hours + b, color='red', label='Trend Line')

plt.legend()