title: removing unicode from text in pandas
body: <p>for one string, the code below removes unicode characters &amp; new lines/carriage returns:</p>
  
  <pre><code>t = "We've\xe5\xcabeen invited to attend TEDxTeen, an independently organized TED event focused on encouraging youth to find \x89\xdb\xcfsimply irresistible\x89\xdb\x9d solutions to the complex issues we face every day.,"
  
  t2 = t.decode('unicode_escape').encode('ascii', 'ignore').strip()
  import sys
  sys.stdout.write(t2.strip('\n\r'))
  </code></pre>
  
  <p>but when I try to write a function in pandas to apply this to every cell of a column, it either fails because of an attribute error or I get a warning that a value is trying to be set on a copy of a slice from a DataFrame</p>
  
  <pre><code>def clean_text(row):
      row= row["text"].decode('unicode_escape').encode('ascii', 'ignore')#.strip()
      import sys
      sys.stdout.write(row.strip('\n\r'))
      return row
  </code></pre>
  
  <p>applied to my dataframe:</p>
  
  <pre><code>df["text"] = df.apply(clean_text, axis=1)
  </code></pre>
  
  <p>how can I apply this code to each element of a Series?</p>