title: Issue With Updating Pandas Dataframe Column Based on Reference Table Targeting Another Column
body: <p>I have a dataframe that I'm trying to update based on information that I have in an external reference table (that is currently a small ~20 entry csv), and I'm having some difficulty figuring out how to get it to work.</p>
  
  <p>The dataframe looks like this:</p>
  
  <pre><code>id    company    value1    value2
  1     foo        10.00     0.00
  2     bar        10.00     0.00
  3     lorem      15.00     0.00
  4     ipsum      10.00     0.00
  5     foo        50.00     0.00
  6     lorem      40.00     0.00
  7     foo        0.00      0.00
  </code></pre>
  
  <p>The reference table csv contains percentages to multiply value1 by to get value2, like this (they were originally decimals, but vd = pd.read_csv('$name.csv', index_col=0).to_dict() was causing floating point issues, so I changed the reference table instead):</p>
  
  <pre><code>company   percentage
  foo       15
  bar       50
  ipsum     40
  </code></pre>
  
  <p>I am looking for the output to appear as the following:</p>
  
  <pre><code>id    company    value1    value2
  1     foo        10.00     1.50
  2     bar        10.00     5.00
  3     lorem      15.00     15.00
  4     ipsum      10.00     4.00
  5     foo        50.00     7.50
  6     lorem      40.00     40.00
  7     foo        0.00      0.00
  </code></pre>
  
  <p>At the moment, I am importing the reference table into a dictionary (as integers), however when I go to match it against the dataframe, the results (value2) come back as NaN. I think my issue might be with the matching code, which currently looks like this:</p>
  
  <pre><code>df['value2'] = df['value1'] * (df["company"].map(vd)/100)
  </code></pre>
  
  <p>Am I on the right track here? This is my first time using pandas, so I might be missing something obvious.</p>
  
  <p>Thank you!</p>
  
  <p>edit: accidentally forgot to obfuscate one variable. "vd" was just the name of the dictionary once it was imported.</p>