title: Is there a way to efficiently convert the data type in a DataFrame's column?
body: <p>A DataFrame <code>df</code> has a column named <code>LastPrice</code>. Each of <code>df['LastPrice']</code> is a <code>string</code>, I want to convert <code>string</code> to <code>float</code>. I use <code>map</code> to solve it.</p>
  
  <pre><code>#-*- encoding:utf-8 -*-
  import pandas as pd
  from pandas import Series,DataFrame
  
  df = DataFrame({'Time':['1-14','1-15','1-16'],'LastPrice':['1.0','2.0','3.0']},columns = ['Time','LastPrice'])
  f_L = df['LastPrice'].map(float)
  print type(f_L[0])
  print type(df['LastPrice'][0])
  </code></pre>
  
  <p>The result is:</p>
  
  <pre><code>&lt;type 'numpy.float64'&gt;
  &lt;type 'str'&gt;
  [Finished in 1.2s]
  </code></pre>
  
  <p>So <code>map</code> can't convert data types in-placce, I just do:</p>
  
  <pre><code>df['LastPrice'] = f_L
  </code></pre>
  
  <p>Is there a way to covert the data type in a DataFrame's column efficiently? 
  What I mean is coverting data type in-place, instead of reassignment.</p>