title: Want to replace NaN by taking average of other sensors
body: <p>I have a <code>df</code> with <code>DateTimeIndex</code> (hourly readings) from multiple sensors</p>
  
  <pre><code>Time                   Temp1   Temp2   Temp3  Humidity1 Humidity2 
  1/2/2017 13:00          31       23      NA     66        48
  1/2/2017 14:00           22      NA      NA      63        43
  1/2/2017 15:00           25      25      21      41        39
  </code></pre>
  
  <p>I would like to replace missing values of Temperature sensor 3 (Temp3) with available data from Temp1 and Temp2. If both Temp1 and Temp2 are not null, I want to take an average. If only 1 is available, I will take that value.</p>
  
  <p>Expected Output:</p>
  
  <pre><code>Time                      Temp3   
  1/2/2017 13:00               27     
  1/2/2017 14:00               22      
  1/2/2017 15:00               21     
  </code></pre>
  
  <p>I tried to use apply with lambda, but running into issues when one of the data is missing. </p>
  
  <pre><code>Df['Temp3'] = Df.apply(
      lambda row: (row['Temp1']+row['Temp2'])/2 if np.isnan(row['Temp3']) 
      else row['Temp3'],
      axis=1
  )
  </code></pre>