title: Change Series inplace in DataFrame after applying function on it
body: <p>I'm trying to use <code>pandas</code> in order to change one of my columns in-place, using simple function.</p>
  <p>After reading the whole Dataframe, I tried to apply function on one Series:</p>
  <pre><code>wanted_data.age.apply(lambda x: x+1)
  </code></pre>
  <p>And it's working great. The only problem occurs when I try to put it back into my DataFrame:</p>
  <pre><code>wanted_data.age = wanted_data.age.apply(lambda x: x+1)
  </code></pre>
  <p>or:</p>
  <pre><code>wanted_data['age'] = wanted_data.age.apply(lambda x: x+1)
  </code></pre>
  <p>Throwing the following warning:</p>
  <pre><code>&gt; C:\Anaconda\lib\site-packages\pandas\core\generic.py:1974:
  &gt; SettingWithCopyWarning: A value is trying to be set on a copy of a
  &gt; slice from a DataFrame. Try using .loc[row_indexer,col_indexer] =
  &gt; value instead
  &gt; 
  &gt; See the the caveats in the documentation:
  &gt; http://pandas.pydata.org/pandas-docs/stable
  &gt; /indexing.html#indexing-view-versus-copy   self[name] = value
  </code></pre>
  <p>Of Course, I can set the DataFrame using the long form of:</p>
  <pre><code>wanted_data.loc[:, 'age'] = wanted_data.age.apply(lambda x: x+1)
  </code></pre>
  <p>But is there no other, easier and more syntactic-nicer way to do it?</p>
  <p>Thanks!</p>