title: Plot quadratic function / model with matplotlib
body: <p>I'm playing around with python and want to plot a quadratic linear regression with matplotlib. The problem is, that my plot ends up being a lot of connected lines/dots instead of just the one function:</p>
  
  <p><a href="http://i.stack.imgur.com/EljBs.png" rel="nofollow">Plot</a></p>
  
  <p><a href="http://i.stack.imgur.com/wQxfD.png" rel="nofollow">Zoomed plot</a></p>
  
  <p>Normally I would think of this being a problem with row vs. column vectors. But nothing seems to change when I transpose. </p>
  
  <p>Here's my code:</p>
  
  <pre><code>from sklearn import datasets, linear_model
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  
  # Load diabetes dataset JSON
  dsDiabetes = datasets.load_diabetes()
  
  # Create feature- and outcome vectors
  lin_train  = np.array(dsDiabetes.data[:, np.newaxis, 2])
  quad_train = np.concatenate((lin_train, lin_train**2), axis=1)
  outcome    = np.array(dsDiabetes.target)
  
  # Create regression objects
  lin_model = linear_model.LinearRegression()
  quad_model = linear_model.LinearRegression()
  
  # lin_train model
  lin_model.fit(lin_train, outcome)
  quad_model.fit(quad_train, outcome)
  
  # Plot
  plt.style.use('fivethirtyeight')
  plt.scatter(lin_train, outcome, color='black')
  # plt.plot(lin_train, quad_model.predict(quad_train))
  # plt.plot(lin_train, lin_model.predict(lin_train), color='blue', linewidth=1)
  plt.plot(lin_train, quad_model.predict(quad_train), color='red', linewidth=1)
  plt.show()
  </code></pre>
  
  <p>What am I missing?</p>