title: Merging two columns while eliminating duplicate strings in pandas dataframe
body: <p>I have a dataframe with the original column 'All' , which I split into RegionName1 and RegioName2 columns. There are duplicate entries, for example, Duluth and Duluth (University of Minnesota Duluth.
  I want to convert strings like Duluth (University of Minnesota Duluth  to NaN values.
  So I have tried</p>
  
  <p>unitown['RegionName2'] = [np.nan if '(' in x else x for x in unitown['RegionName2']]</p>
  
  <p>and got an error that 
  TypeError: argument of type 'float' is not iterable. What else can I try?</p>
  
  <p><a href="https://i.stack.imgur.com/oe8qj.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/oe8qj.jpg" alt="enter image description here"></a></p>
  
  <pre><code>unitown=pd.read_table('university_towns.txt', header=None).rename(columns={0:'All'})
  unitown['State']=unitown['All'].apply(lambda x: x.split('[edi')[0].strip() if x.count('[edi') else np.NaN).fillna(method="ffill")                       #.fillna(method="ffill")
  unitown['RegionName1'] = unitown['All'].apply(lambda x: x.split('(')[0].strip() if x.count('(') else np.NaN)
  unitown['RegionName2'] = unitown['All'].apply(lambda x: x.split(',')[0].strip() if x.count(',') else np.NaN)
  unitown['RegionName2'] = [np.nan if '(' in x else x for x in     unitown['RegionName2']]
  return unitown[unitown.State=='Minnesota']  
  </code></pre>