title: map two subplots with seaborn FacetGrid
body: <p>I'm trying to produce two seaborn kernel density plots (<code>kdeplot</code>) side by side. </p>
  
  <p>Three features (Community School?, Economic Need Index, School Income Estimate) are used here. The only categorical feature 'Community School?' is shown as green-blue colors representing its levels. 'Economic Need Index' and 'School Income Estimate' are for the two kdeplots respectively.</p>
  
  <p>The image created using the code shown below is the best result I could get, but it has problems.</p>
  
  <p>1) <s>y-axis scale of the second plot is wrong (it should be some integer scales like the first plot)</s> correction: <code>kdeplot</code> is normed (everything sums up to 1), so y-axis is correct given its x values.</p>
  
  <p>2) an extra axis(?) is produced along below the two plots</p>
  
  <p>3) I want to add a title for each subplot</p>
  
  <p>I found <code>kdeplot</code> doesn't support <code>hue</code> so I tried to make it work with <code>FacetGrid</code>. Not sure if it's the right way to do it. Would appreciate if a better method is provided.</p>
  
  <pre><code>fig, (ax1, ax2) = plt.subplots(1, 2)
  fig.subplots_adjust(wspace=.8)
  
  fg = sns.FacetGrid(df, hue='Community School?', size=3)
  fg.map(sns.kdeplot, 'Economic Need Index', shade=True, ax=ax1, label='Economic Need Index')
  fg.map(sns.kdeplot, 'School Income Estimate', shade=True, ax=ax2, label='School Income Estimate')
  plt.show()
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/E6Wmq.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/E6Wmq.png" alt="enter image description here"></a></p>
  
  <pre><code># my dataset looks like:
  
  Community School? / Economic Need Index / School Income Estimate
  0   Yes 0.919   31141.72
  1   No  0.641   56462.88
  2   No  0.744   44342.61
  3   No  0.860   31454.00
  4   No  0.730   46435.59
  5   No  0.858   39415.45
  6   No  0.499   43706.73
  7   No  0.833   28820.67
  8   No  0.849   34889.24
  9   No  0.861   35545.10
  10  No  0.559   40809.90
  11  Yes 0.917   27881.59
  12  Yes 0.832   NaN
  13  No  0.791   NaN
  14  No  0.362   63760.00
  15  No  0.771   NaN
  16  No  0.451   62519.57
  17  No  0.430   57504.48
  18  No  0.448   56787.20
  19  No  0.764   NaN
  20  No  0.610   NaN
  21  No  0.257   76833.96
  22  No  0.597   NaN
  23  No  0.769   32817.79
  24  No  0.858   26114.78
  25  No  0.176   103399.19
  26  No  0.101   144270.13
  27  No  0.293   98455.77
  28  No  0.430   88011.14
  29  No  0.153   102421.46
  ... ... ... ...
  </code></pre>
  
  <p>And a full dataset can be found <a href="https://www.kaggle.com/passnyc/data-science-for-good" rel="nofollow noreferrer">here</a>.</p>