title: pandas: Dataframe.replace() with regex
body: <p>I have a table which looks like this:</p>
  
  <pre><code>df_raw = pd.DataFrame(dict(A = pd.Series(['1.00','-1']), B = pd.Series(['1.0','-45.00','-'])))
  
      A       B
  0   1.00    1.0
  1   -1      -45.00
  2   NaN     -
  </code></pre>
  
  <p>I would like to replace '-' to '0.00' using dataframe.replace() but it struggles because of the negative values, '-1', '-45.00'. </p>
  
  <p>How can I ignore the negative values and replace only '-' to '0.00' ?</p>
  
  <p>my code:</p>
  
  <pre><code>df_raw = df_raw.replace(['-','\*'], ['0.00','0.00'], regex=True).astype(np.float64)
  </code></pre>
  
  <p>error code:</p>
  
  <pre><code>ValueError: invalid literal for float(): 0.0045.00
  </code></pre>