title: Pandas: drop_duplicates with condition
body: <p>Is there any way to use <code>drop_duplicates</code> together with conditions? For example, let's take the following Dataframe:</p>
  
  <pre><code>import pandas as pd
  df = pd.DataFrame({
  'Customer_Name': ['Carl', 'Carl', 'Mark', 'Joe', 'Joe'],
  'Customer_Id': [1000,None,None,None,50000]
  })
  </code></pre>
  
  <p>Based on this Dataframe I would like to derive a Dataframe with distinct rows for <code>Customer_Id</code> and <code>Customer_Name</code> </p>
  
  <pre><code>    Customer_Id Customer_Name
  0    1000        Carl
  2    NaN         Mark
  5    50000       Joe
  </code></pre>
  
  <p>Unfortunately, I cannot use the <code>drop_duplicates</code> method for this as this method would always delete the first or last duplicated occurrences. </p>
  
  <p>However, in my case this differs (see Carl and Joe). Moreover, I cannot just delete all rows with None entries in the Customer_Id column as this would also delete the entry for Mark.</p>
  
  <p>I deeply appreciate any help.</p>