title: Creating a Pandas column based on a value in a specific row and column with .map or similar
body: <p>I have a use case where I need to fill a new pandas column with the contents of a specific cell in the same table. There are 60 countries in Europe, so I need to fill a shared currency column with the content's of one country's currency (as an example only)</p>
  <p>I need an SQL &quot;Where&quot; clause for Pandas - that:</p>
  <pre><code>1. Searches the dataframe rows for the single occurrence of &quot;Britain&quot; in column &quot;country&quot;
  2. Returns a single, unique value &quot;pound&quot; from df['currency'].
  3. Creates a new column filled with just this value = string &quot;pound&quot;
  
  
  w['Euro_currency'] = w['Euro_currency'].map(w.loc[&quot;country&quot;]==&quot;Britain&quot;[&quot;currency&quot;])
  
  # [Britain][currency] - contains the value - &quot;Pound&quot;
  </code></pre>
  <p>When this works correctly, every row in the new column 'Euro_currency' contains the value &quot;pound&quot;</p>