title: How to avoid overwriting column (multiple condition statements that flag a column) pandas
body: <p>I need to update a list of columns to appropriate datatypes. There are 1500 columns but a lot of them are repeats. I am trying to write a script where I could just say something like 'Key' and 'claim' as BIGINT, while anything that ends in DT should be a date and 'name' should be VARCHAR.</p>
  
  <p>The code below identifies the rows appropriately, but only when you run it individually. So how can you combine into one so that the last line doesn't overwrite the previous ones.</p>
  
  <p>Here is my df and code thus far:</p>
  
  <pre><code>df = pd.DataFrame({'ColumnTitles': ['Key', 'Claim', 'First_dt', 'Second_dt', 'Third_dt','name','Claim', 'Key'], 
                 'CurrentDataTypes': ['String','String','String','String','String','String','String','String']})
  
  bigint = ['Key','Claim']
  bigint = ['name']
  df['Updated'] = df[df['ColumnTitles'].isin(bigint)]
  df['Updated'] = df[df['ColumnTitles'].isin(bigint)]
  df['Updated'] = np.where(df['ColumnTitles'].str.contains("_dt", case=False, na=False), 'Date', '')
  </code></pre>