title: Left join on multiple columns
body: <p>I'm used to using dplyr with R where I would do something like</p>
  
  <pre><code>library(dplyr)
  mtcars2=mtcars
  mtcars3 = mtcars %&gt;% left_join(mtcars2[,c("mpg","vs","hp")], by =c("mpg",'hp') )
  
  # what this does is I do a left join with multiple columns and then bring over only *1* additional column.  This means that mtcars3 only has one additional field - a duplicated 'vs'
  </code></pre>
  
  <p>I can't figure out how to use pd.merge to do the same thing.
  I would want to join by two columns and then bring over <em>only</em> the 3rd column - not every column in the joined table except for the join-bys if that makes sense</p>
  
  <pre><code>import pandas as pd
  mtcars = pd.read_csv('mtcars.csv')
  mtcars2=mtcars
  
  mtcars3  = pd.merge(mtcars, mtcars2['vs','hp','mpg'],how='left', on = ['mpg','hp'])
  </code></pre>