title: Finding the slope trend from best fit lines
body: <p>I am trying to figure out how to determine the slope trend from best fit lines that have points. Basically, once I have the trend in the slope, I want to plot multiple other lines with that trend in the same plot. For example: <a href="https://i.stack.imgur.com/ItW24.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ItW24.png" alt="enter image description here"></a></p>
  
  <p>This plot is basically what I want to do, but I am  not sure how to do it. As you can see, it has several best fit lines with points that have slopes and intersect at x = 6. After those lines, it has several lines that are based on the trend from the other slopes. I am assuming that using this code I can do something similar, but I am unsure how to manipulate the code to do what I want.</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  
  # simulate some artificial data
  # =====================================
  df = pd.DataFrame( { 'Age' : np.random.rand(25) * 160 } )
  
  df['Length'] = df['Age'] * 0.88 + np.random.rand(25) * 5000
  
  # plot those data points
  # ==============================
  fig, ax = plt.subplots()
  ax.scatter(df['Length'], df['Age'])
  
  # Now add on a line with a fixed slope of 0.03
  slope = 0.03
  
  # A line with a fixed slope can intercept the axis
  # anywhere so we're going to have it go through 0,0
  x_0 = 0
  y_0 = 0
  
  # And we'll have the line stop at x = 5000
  x_1 = 5000
  y_1 = slope (x_1 - x_0) + y_0
  
  # Draw these two points with big triangles to make it clear
  # where they lie
  ax.scatter([x_0, x_1], [y_0, y_1], marker='^', s=150, c='r')
  
  # And now connect them
  ax.plot([x_0, x_1], [y_0, y_1], c='r')    
  
  plt.show()
  </code></pre>