如何使用Matplotlib基于Numpy数组绘制多技术年份趋势图表

Got it, let's walk through exactly how to create that plot matching your example style. I'll assume you want a line plot (the most common choice for time-based trends) with each tech as a distinct line, clear labels, and the x-axis showing actual years instead of array indices.

Step 1: Import Required Libraries #

First, make sure you have numpy and matplotlib installed, then import them into your script:

import numpy as np
import matplotlib.pyplot as plt

Step 2: Organize Your Data & Years #

You already have your NumPy arrays, so replace the sample values below with your actual data:

# Replace these with your real arrays!
php = np.array([85, 80, 75, 70, 65, 60, 58, 55])
js = np.array([60, 65, 70, 78, 85, 90, 92, 95])
html = np.array([70, 72, 74, 76, 75, 73, 72, 70])
html5 = np.array([40, 50, 60, 70, 78, 85, 88, 90])
my_sql = np.array([75, 73, 70, 68, 65, 62, 60, 58])

# Create a years array to map indices to actual years (0=2015, 7=2022)
years = np.arange(2015, 2023)

Step 3: Build the Plot with Example-Matching Styles #

Here's the core code to generate the plot. Tweak colors, line styles, and markers to match your example exactly:

# Set figure size (adjust width/height to match your example's dimensions)
plt.figure(figsize=(10, 6))

# Plot each tech with custom styling (adjust these to match your example)
plt.plot(years, php, label='PHP', color='#777777', linestyle='-', marker='o')
plt.plot(years, js, label='JavaScript', color='#f7df1e', linestyle='--', marker='s')
plt.plot(years, html, label='HTML', color='#e34c26', linestyle='-.', marker='^')
plt.plot(years, html5, label='HTML5', color='#e34c26', linestyle=':', marker='D')
plt.plot(years, my_sql, label='MySQL', color='#00758f', linestyle='-', marker='*')

# Add title and axis labels (customize text to match your example)
plt.title('Tech Popularity Trends (2015-2022)', fontsize=14, fontweight='bold')
plt.xlabel('Year', fontsize=12)
plt.ylabel('Trend Value', fontsize=12)

# Set x-axis ticks to display every year (instead of default auto-ticks)
plt.xticks(years)

# Add a legend to identify each line (adjust position if needed)
plt.legend(loc='best')

# Add a subtle grid for readability (common in example plots)
plt.grid(True, alpha=0.3)

# Adjust layout to prevent label cutoff
plt.tight_layout()

# Show the plot (or save it to a file with plt.savefig('tech_trends.png'))
plt.show()

Key Customizations to Match Your Example #

If your target plot has specific design elements, tweak these parts:

• Colors: Replace hex codes with exact colors from your example (use tools like a color picker to get the right values).
• Line Styles: Use linestyle='-', '--', '-.': to match line patterns.
• Markers: Choose markers like 'o', 's', '^' to replicate data point shapes.
• Legend Position: Change loc='best' to 'upper left', 'lower right', etc., if your example places the legend elsewhere.
• Text: Update the title and axis labels to match the exact wording in your example.
• Plot Type: If your example is a bar chart instead of a line plot, replace plt.plot() with plt.bar() (you’ll need to offset bars for each tech to avoid overlap).

Content is sourced from Stack Exchange, question author Proger228