title: Python3.x Pandas mean and std returning NAN
body: <p>hitting this issue , which I am unable to fix , i have a large Data Frame (Sample Posted below ) , with 5 columns. I want to calculate Std and mean for each row. somehow it keep returning NaN.</p>
  
  <pre><code>   CellName                Apr-2018    Feb-2018    Jan-2018    Mar-2018  mean
  0  BDG652ML_KPBENDULML1    9.450841   24.119474   27.091426   17.527006   NaN
  1  BDG652ML_KPBENDULML2   15.917555   10.548731   11.019208   14.592388   NaN 
  2  BDG652ML_KPBENDULML3   24.957360   21.122519   21.197216   24.950549   NaN
  </code></pre>
  
  <p>I have checked , all my month columns and <code>float64</code> 
  <code>df.types</code> gives: </p>
  
  <pre><code>CellName     object
  Apr-2018    float64
  Feb-2018    float64
  Jan-2018    float64
  Mar-2018    float64
  dtype: object
  </code></pre>
  
  <p>I know that I don't need to exclude cell column and i can get mean easily by using </p>
  
  <p><code>df['mean'] = df.mean(numeric_only =True)</code></p>
  
  <p>i have also tried : </p>
  
  <p><code>df['mean'] = df.iloc[:,1:].mean(numeric_only =True)</code></p>
  
  <p>but still it remains None. Same behavior for Std as well.</p>
  
  <p>Any hints on on what i may be doing wrong?</p>