title: pandas.concat produces all NaN
body: <p>I'm trying to join two series with <code>pd.concat([a, b], axis=1)</code>, but the result is a dataframe filled with <code>NaN</code>s, here's what I mean:</p>
  
  <h2>producing the two <code>Series</code></h2>
  
  <pre class="lang-py prettyprint-override"><code>by_status = odr.set_index('order_status')
  
  g = by_status.groupby(['dt', 'product_id'])
  payed_orders = g.size()
  payed_orders.name = 'payed_orders'
  refund_g = by_status.loc[[1,2,3], :].groupby(['dt', 'product_id'])
  refund_orders = refund_g.size()
  refund_orders.name = 'refund_orders'
  # I'm going to concat refund_orders and payed_orders
  </code></pre>
  
  <pre class="lang-py prettyprint-override"><code>&gt;&gt;&gt;payed_orders.head()
  dt          product_id
  2015-01-15  10001          1
              10007          1
              10016         14
              10022          1
              10023          1
  Name: payed_orders, dtype: int64
  
  &gt;&gt;&gt;refund_orders.head()
  dt          product_id
  2015-01-15  10007         1
              10016         4
              10030         1
  2015-01-16  10007         3
              10008         1
  Name: refund_orders, dtype: int64
  
  &gt;&gt;&gt;pd.concat([payed_orders.head(), refund_orders.head()], axis=1, ignore_index=False)
          payed_orders    refund_orders
  dt  product_id      
  2015-01-15  10001   NaN NaN
              10007   NaN NaN
              10016   NaN NaN
              10022   NaN NaN
              10023   NaN NaN
              10030   NaN NaN
  2015-01-16  10007   NaN NaN
              10008   NaN NaN
  </code></pre>
  
  <p>I don't think I must've made some obvious mistakes but I really can't figure it out, please help.</p>
  
  <p>P.S. the code is copied from ipython notebook, don't feel strange about the format.</p>
  
  <h2>update</h2>
  
  <p>Tried passing <code>ignore_index=True</code>, here's what happened:</p>
  
  <pre class="lang-py prettyprint-override"><code>&gt;&gt;&gt;pd.concat([payed_orders.tail(), refund_orders.tail()], axis=1, ignore_index=True)
          0   1
  dt  product_id      
  2015-09-07  1000081 NaN NaN
              1000084 NaN NaN
              1000094 NaN NaN
              1000096 NaN NaN
              1000124 NaN NaN
              1000131 NaN NaN
              1000132 NaN NaN
              1000133 NaN NaN
              1000134 NaN NaN
              1000137 NaN NaN
  </code></pre>
  
  <h2>as for index formatting issue</h2>
  
  <p>so here's two series that won't concat well together:</p>
  
  <pre class="lang-py prettyprint-override"><code>&gt;&gt;&gt;a4.head().to_dict()
  {'actual_suborders': {(datetime.date(2015, 1, 15), 10001): 1,
    (datetime.date(2015, 1, 15), 10016): 10,
    (datetime.date(2015, 1, 15), 10022): 1,
    (datetime.date(2015, 1, 15), 10023): 1,
    (datetime.date(2015, 1, 15), 10024): 1}}
  
  &gt;&gt;&gt;a5.head().to_dict()
  {'refund_suborders': {(datetime.date(2015, 1, 15), 10007): 1,
    (datetime.date(2015, 1, 15), 10016): 4,
    (datetime.date(2015, 1, 15), 10030): 1,
    (datetime.date(2015, 1, 16), 10007): 4,
    (datetime.date(2015, 1, 16), 10008): 1}}
  
  &gt;&gt;&gt;pd.concat([a4.head(), a5.head()], axis=1)
          actual_suborders    refund_suborders
  dt  product_id      
  2015-01-15  10001   NaN NaN
              10007   NaN NaN
              10016   NaN NaN
              10022   NaN NaN
              10023   NaN NaN
              10024   NaN NaN
              10030   NaN NaN
  2015-01-16  10007   NaN NaN
              10008   NaN NaN
  </code></pre>
  
  <h2>last resort</h2>
  
  <p>thanks for everyone that decide to take a look at this, great community.</p>
  
  <p>I've serialized the head of above series, uploaded to evernote, contains code to load and concat them</p>
  
  <p><a href="https://www.evernote.com/l/AH4AdfgOJJROuZSfGfDR_jZvA0zEpIHgyq0" rel="nofollow">https://www.evernote.com/l/AH4AdfgOJJROuZSfGfDR_jZvA0zEpIHgyq0</a></p>