title: Search Through Columns With the Same Name (in the same dataset) and Create a New Column That Takes the Value the First Value Populated
body: <p>I currently have the following dataframe</p>
  <pre><code>| Number | Number  | Name | Name | Country | Country  |
  | ------ | ------- | ---- | ---- | ------- | -------  |
  | 1      | Nan     | Bob  | Nan  | USA     | Nan      |
  | 4      | Nan     | Nan  | Nan  | India   | Nan      |
  | Nan    | 12      | Joe  | Nan  | Nan     | China    |
  | Nan    | 6       | Nan  | Matt | Nan     | Australia|
  </code></pre>
  <p>My question is, how do I create new Number, Name, Country columns (etc.) that just finds the like column and then sees which column is populated and pulls that value. Note in this example I only have two duplicate columns but would ideally like to be able to handle many different duplicate columns not just two.</p>
  <p>So far I have the following code to identify the duplicate columns and then create a dictionary a key which is the desired column name and then the values are just the numbered original columns. (e.g. {'country': ['country_2', 'country_1']})</p>
  <p>How can I now traverse through this dictionary and create a new &quot;country&quot; column that parses through all of the values in the list dictionary to find which column is populated?</p>