如何在Python代码中集成plt.subplots批量绘制子图？

Hey there! No worries at all—we all start somewhere with Python and data visualization. Let's tweak your code to use plt.subplots so you can plot every outlier's window in one go. Here's how to do it step by step:

Key Idea #

Instead of manually picking a single num to plot, we'll:

1. Count how many outliers we have
2. Create a grid of subplots matching that number
3. Loop through each outlier, plot its window on a separate subplot

Modified Full Code #

import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

# Load your data (same as before)
df = pd.read_csv(r"/Users/aaronhuang/Desktop/ffp/exfileCLEAN2.csv", skiprows=[1])
magnitudes = df['Magnitude '].values
times = df['Time '].values

# Calculate outliers (same as before)
zscores = np.abs(stats.zscore(magnitudes, ddof=1))
outlier_indicies = np.argwhere(zscores > 3).flatten()
n_outliers = len(outlier_indicies)

# Exit early if no outliers are found (avoids errors)
if n_outliers == 0:
    print("No outliers detected!")
else:
    # Set up subplots: adjust rows/cols based on number of outliers
    # Here we use 2 columns, rows are calculated automatically
    fig, axes = plt.subplots(nrows=(n_outliers + 1) // 2, ncols=2, figsize=(14, 6 * ((n_outliers + 1) // 2)))
    # Flatten axes into a 1D array to make looping easier (works even for 1 row)
    axes = axes.flatten()

    window = 2  # Your original window size

    # Loop through each outlier and plot its window
    for i, idx in enumerate(outlier_indicies):
        # Get the time and magnitude slice for this outlier's window
        x = times[idx - window : idx + window + 1]
        y = magnitudes[idx - window : idx + window + 1]
        
        # Plot on the i-th subplot
        axes[i].plot(x, y, marker='o', label=f'Outlier at index {idx}')
        axes[i].set_xlabel('Time (units)')
        axes[i].set_ylabel('Magnitude (units)')
        axes[i].set_title(f'Outlier #{i+1} (Time: {times[idx]:.2f})')
        axes[i].legend()
        axes[i].grid(True)  # Optional: adds grid for readability

    # Hide any empty subplots (if number of outliers is odd)
    for j in range(i + 1, len(axes)):
        axes[j].axis('off')

    # Adjust layout so plots don't overlap
    plt.tight_layout()
    plt.show()

What Changed? #

• We added n_outliers = len(outlier_indicies) to know how many subplots we need
• plt.subplots() creates a grid of plots: we use 2 columns, and calculate rows based on the number of outliers
• axes.flatten() turns the 2D axes array into a 1D list, making it easy to loop through each plot
• The for loop goes through every outlier index, plots its window on the corresponding subplot
• We added titles, legends, and grid lines to make each subplot clearer
• plt.tight_layout() ensures labels/titles don't overlap between subplots
• We handle the case where there are no outliers to avoid errors

Feel free to adjust the figsize or number of columns (change ncols=2 to ncols=3 if you want more plots per row) to fit your needs!

内容的提问来源于stack exchange，提问作者Aaron Huang