title: Populate new dataframe based on secondary dataframe values (pandas dataframe)
body: <p>I have two data frames which consist of CVE vulnerability findings. I have scraped the vulnerability descriptions from a website and generated a second data frame with the results. Based on the Vulnerability column, I need to populate a new Description column in the first data frame.</p>
  <p>It loops through multiple files and data frames, this is the current status of the function I'm trying to create to return a combined data frame (not in a working state).</p>
  <pre><code>def combineDfs(cve_list, desc_list, old_df):
      cve_descriptions = pd.DataFrame((zip(cve_list, desc_list)))
      result = {}
      for df in old_df:
          result = pd.concat([df, cve_descriptions], keys='Vulnerability')
      return result
  </code></pre>
  <p>I've tried the .groupby method and a few others, but I can't seem to get the new rows to align with the already existing rows versus tacking the new info below the existing rows.</p>
  <p>Below shows an example of the contents of the first dataframe,</p>
  <pre><code>Vulnerability     Severity     Package
  CVE-2021-34245    High         curl
  CVE-2022-27090    Moderte      vim
  </code></pre>
  <p>The second data frame deduplicates the Vulnerability column and pulls the Description down for each CVE to create a second data frame that looks like the below,</p>
  <pre><code>Vulnerability     Description
  CVE-2022-27090    Example description xyz.
  CVE-2021-34245    Second example description.
  </code></pre>
  <p>The final dataframe should output the following,</p>
  <pre><code>Vulnerability     Severity     Package     Description
  CVE-2021-34245    High         curl        Second example description.
  CVE-2022-27090    Moderate     vim         Example description xyz.
  </code></pre>