title: How to plot categorical data with seaborn setting the plot-style for each data column?
body: <h2>Background</h2>
  
  <p>Let's say I have the following dataset:</p>
  
  <pre class="lang-py prettyprint-override"><code>import pandas as pd
  import numpy as np
  
  data = ([["Cheese", x] for x in np.random.normal(0.8, 0.03, 10)] + 
          [["Meat", x] for x in np.random.normal(0.4, 0.05, 14)] + 
          [["Bread", 0.8], ["Bread", 0.65]])
  
  df = pd.DataFrame(data, columns=["Food", "Score"])
  
  
  import seaborn as sns
  import matplotlib.pyplot as plt
  sns.set(style="ticks", color_codes=True)
  sns.set_context("paper")
  sns.catplot(x="Score", y="Food", kind="box", data=df)
  </code></pre>
  
  <p>which yields the following plot (or similar, depending on the generated random numbers):</p>
  
  <p><a href="https://i.stack.imgur.com/Hi8qq.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Hi8qq.png" alt="Sample box plot"></a></p>
  
  <p>The reason I am going for box-plots with my actual data is that individual dots combined with the amount of categories I want to show make the plot visually way too noisy and the boxes give a nice general overview of how the data is distributed which is what I am after. However, the issue is with categories like the "Bread" category.</p>
  
  <h2>Question</h2>
  
  <p>As you can observe, seaborn produced boxes with median, quartiles etc. for all three categories. However, since the category "Bread" does only have two data-points, using a box-plot for this category is not really an appropriate representation. I would much rather have <em>this category only</em> as individual dots.</p>
  
  <p>But when looking at the examples on the <a href="https://seaborn.pydata.org/tutorial/categorical.html" rel="noreferrer">https://seaborn.pydata.org/tutorial/categorical.html</a>, the only suggestion for combining box-plots and simple dots is to plot <em>both</em> for all categories which is not what I am after.</p>
  
  <p><strong>In short:</strong> How do I plot categorical data with seaborn, selecting the appropriate representation for each category?</p>