title: How to apply group by on data frame with neglecting NaN values in Pandas?
body: <p>I am sorry if this is too simple, but I have searched allot and couldn't find a solution for this problem.</p>
  
  <p>I am populating my data frame (df) as below:</p>
  
  <pre><code>weather = pd.read_csv(weather_path)
  weather_stn1 = weather[weather['Station'] == 1][['Tavg']]
  weather_stn2 = weather[weather['Station'] == 2][['Tavg']]
  
  df = pd.DataFrame(columns=['xAxis', 'yAxis1', 'yAxis2'])
  df['xAxis'] = pd.to_datetime(weather['Date'])
  df['yAxis1'] = weather_stn1['Tavg']
  df['yAxis2'] = weather_stn2['Tavg']
  </code></pre>
  
  <p>My data frame is as below:</p>
  
  <pre><code>     xAxis        yAxis1  yAxis2
  0   2009-05-01      53     NaN
  1   2009-05-01     NaN      55
  2   2009-05-02      55     NaN
  3   2009-05-02     NaN      55
  4   2009-05-03      57     NaN
  5   2009-05-03     NaN      58
  </code></pre>
  
  <p>but I want to have my results as below:</p>
  
  <pre><code>     xAxis       yAxis1  yAxis2
  0   2009-05-01      53     55
  2   2009-05-02      55     55
  4   2009-05-03      57     58
  </code></pre>
  
  <p>I have been working on reindexing of weather_stn1 and weather_stn2 and in applying group by but it is not working as I want to do. It ends up with me having nothing at all to display!</p>
  
  <p>How should I approach this problem?</p>
  
  <p>Thanks allot for your time in advance.</p>