使用matplotlib的plt.axhline()绘制最优拟合线的技术咨询

Hey there! Let's break down how to add that red fit line you've hand-drawn. First, let's clarify two common scenarios since you mentioned plt.axhline()—it's built for horizontal lines, but if your desired line is a sloped regression fit, we'll need a slightly different approach.

Scenario 1: Horizontal Fit Line (e.g., Mean of Your y-values) #

If you want a horizontal line (like the average of your mutation appearance values), plt.axhline() is exactly what you need. Here's how to use it effectively:

Key Parameters for plt.axhline(): #

• y: Required — The y-axis value where the horizontal line will sit (e.g., the mean of your y dataset)
• c/color: Line color (use 'r' for red to match your hand-drawn line)
• linewidth/lw: Thickness of the line to make it stand out
• linestyle/ls: Style of the line (e.g., '--' for dashed, '-' for solid)
• xmin/xmax: Optional — Control the line's start/end as a fraction of the x-axis (0 = left edge, 1 = right edge; default is 0 to 1)

Full Code Example: #

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame(pd.read_csv('test5.csv', sep=','))
x = df["IE ratio"]
y = df["109"]

x1 = df["IE ratio"].mean()
plt.axvline(x1, 0, 1, c='k')  # Your existing vertical midline

# Calculate the mean of y-values and draw the horizontal fit line
y_mean = y.mean()
plt.axhline(y=y_mean, c='r', linewidth=2, linestyle='--', label='Mean Mutation Appearance')

plt.scatter(x, y, s=10)
plt.ylabel('Appearance of mutation')
plt.xlabel('IE spectrum')
plt.legend()
plt.show()

Scenario 2: Sloped Linear Regression Fit Line #

If your hand-drawn red line is a sloped optimal fit (linear regression) through your scatter points, plt.axhline() won't work—we need to calculate the regression first using numpy.polyfit().

Full Code Example: #

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

df = pd.DataFrame(pd.read_csv('test5.csv', sep=','))
x = df["IE ratio"]
y = df["109"]

x1 = df["IE ratio"].mean()
plt.axvline(x1, 0, 1, c='k')  # Your existing vertical midline

# Calculate linear regression coefficients (slope m, intercept b)
m, b = np.polyfit(x, y, 1)
# Generate y-values for the fit line
y_fit = m * x + b
# Plot the sloped optimal fit line
plt.plot(x, y_fit, c='r', linewidth=2, label='Optimal Linear Fit')

plt.scatter(x, y, s=10)
plt.ylabel('Appearance of mutation')
plt.xlabel('IE spectrum')
plt.legend()
plt.show()

This will draw a red line that best fits your scatter data, matching the hand-drawn effect you described.

内容的提问来源于stack exchange，提问作者RJCoder