title: Merging Pandas rows based on index (time series)
body: <p>I used <code>Pandas</code> <code>.append()</code> to add columns from a number of <code>Pandas</code> <code>timeseries</code> by their index (date). However, instead of combining all data from common dates into one row, the data looks like this:</p>
  
  <pre><code>sve2_all.sort(inplace=True)
  print sve2_all['20000101':'20000104']
  
  
  
  Hgtot ng/l     Q l/s  DOC_mg/L  Flow_mm/day  MeHg ng/l Site  \
  2000-01-01          NaN       NaN       NaN         0.18        NaN  NaN   
  2000-01-01          NaN  0.613234       NaN          NaN        NaN  SVE   
  2000-01-02          NaN       NaN       NaN         0.18        NaN  NaN   
  2000-01-02          NaN  0.614410       NaN          NaN        NaN  SVE   
  2000-01-03          NaN       NaN       NaN          NaN        NaN    2   
  2000-01-03          NaN  0.617371       NaN          NaN        NaN  SVE   
  2000-01-03          NaN       NaN       NaN          NaN        NaN  NaN   
  2000-01-03          NaN       NaN       NaN         0.18        NaN  NaN   
  2000-01-04          NaN  0.627733       NaN          NaN        NaN  SVE   
  2000-01-04          NaN       NaN       NaN         0.18        NaN  NaN   
  
              TOC_filt.TOC  TOC_unfilt.TOC  Temp oC  pH  
  2000-01-01           NaN             NaN      NaN NaN  
  2000-01-01           NaN             NaN -12.6117 NaN  
  2000-01-02           NaN             NaN      NaN NaN  
  2000-01-02           NaN             NaN  -2.3901 NaN  
  2000-01-03           NaN        8.224648      NaN NaN  
  2000-01-03           NaN             NaN  -5.0064 NaN  
  2000-01-03           NaN             NaN      NaN NaN  
  2000-01-03           NaN             NaN      NaN NaN  
  2000-01-04           NaN             NaN  -1.5868 NaN  
  2000-01-04           NaN             NaN      NaN NaN  
  
  [10 rows x 10 columns]
  </code></pre>
  
  <p>I've tried to resample this data by day using:</p>
  
  <pre><code>sve2_all.resample('D', how='mean')
  </code></pre>
  
  <p>And also to group by day using:</p>
  
  <pre><code>sve2_all.groupby(sve2_all.index.map(lambda t: t.day))
  </code></pre>
  
  <p>However, the <code>DataFrame</code> remains unchanged. How can I collapse the rows for the same date into one date? Thanks.</p>
  
  <p><strong>Additional information:</strong> I tried using <code>pd.concat()</code> as suggested by Joris (I had to pass 0 as the axis argument as 1 resulted in <code>ValueError:cannot reindex from a duplicate axis</code>) instead of <code>.append()</code> but the resulting <code>DataFrame</code> is the same as with <code>.append()</code>, a non-uniform non-monotonic time series. I think the index is the problem but I'm not sure what I can do to fix it, I thought that some time stamps might contain hour information while other not so I tried I've also tried using <code>.resample('D',how='mean')</code> on each <code>DataFrame</code> before using <code>.concat()</code> but it didn't make a difference.</p>
  
  <p><strong>Solution:</strong> Joris solution was correct, I didn't realise that <code>.resample()</code> wasn't inplace. Once the <code>.resample()</code> was assigned to a new <code>DataFrame</code> Joris' suggestion provided the desired result.</p>