title: Plotting columns of DataFrame as scatterplots against same y-column
body: <p>I have a DataFrame <code>df</code> representing the CSV data found in <a href="http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv" rel="nofollow noreferrer">Advertising.csv</a>.</p>
  
  <pre><code>&gt;&gt;&gt; df = pd.read_csv('Advertising.csv', index_col=0)
  &gt;&gt;&gt; df.head(5)
  
        TV  Radio  Newspaper  Sales
  1  230.1   37.8       69.2   22.1
  2   44.5   39.3       45.1   10.4
  3   17.2   45.9       69.3    9.3
  4  151.5   41.3       58.5   18.5
  5  180.8   10.8       58.4   12.9
  </code></pre>
  
  <p>I would like to plot each column in my DataFrame against the Sales column in their own respective scatterplot, i.e.
  <a href="https://i.stack.imgur.com/oKv3m.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/oKv3m.png" alt="enter image description here"></a></p>
  
  <p>I managed to do so with </p>
  
  <pre><code>import matplotlib.pyplot as plt 
  
  f, ax_l = plt.subplots(1, 3, figsize=(14, 4))
  
  for e, col_name in enumerate(df.loc[:, :'Newspaper'].columns):
      ax_l[e].scatter(df[col_name], df.Sales, alpha=0.5, color='r')
      ax_l[e].set_xlabel(col_name)
      ax_l[e].set_ylabel('Sales')
  </code></pre>
  
  <p><strong>My question is</strong>, is there a construct within <code>df.plot</code> which could make this task easier that going into Matplotlib and looping as I did?</p>
  
  <hr>
  
  <p><em>Motivation</em></p>
  
  <p>I know in R, to achieve a similar result I would do something like </p>
  
  <pre><code>savePar &lt;- par(mfrow=c(1,3))
  col     &lt;- adjustcolor( 'red', 0.5 )
  with( Advertising,
        { plot( TV       , Sales, pch=19, col=col )
          plot( Radio    , Sales, pch=19, col=col )
          plot( Newspaper, Sales, pch=19, col=col )
        }
      )
  </code></pre>
  
  <p>which admittedly seems quite a bit cleaner than my Pandas approach IMO, and got me questioning whether there is a more straightforward way to plot columns of DataFrames in this manner. </p>