pd.concat on two (or more) series produces all-NaN dataframe

[timfeirg]

this is from a stackoverflow question here, you can download serialized object here and reproduce using python 2.7 and pandas 0.16.2.

I'm trying to concat two series with multiindex using pd.concat([a, b], axis=1) like so:

>>>payed_orders.head()
dt          product_id
2015-01-15  10001          1
            10007          1
            10016         14
            10022          1
            10023          1
Name: payed_orders, dtype: int64

>>>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

>>>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

I've checked the index type and many other stuff to make sure no obvious were made, I've read the docs to learn that concatenating and merging series and dataframes may introduce NaN, but I didn't find anything in the docs to explain this behavior.

[jreback]

see #10060

you are using datetime.date, which is really-really frowned upon and just about non-supported. Its pretty hard to actually construct a multi-index (in fact we were always coercing them to Timestamps) which is the correct dtype here.

Just use Timestamps/datetime.datetime. Their is NO benefit to using datetime.date and just more and more issues.

This is the correct usage

In [23]: s = Series(['a','b'],index=pd.MultiIndex.from_tuples([(1,Timestamp('20130101')),(2,Timestamp('20140101'))],names=['first','seconds']))

In [24]: s2 = Series(['a','b'],index=pd.MultiIndex.from_tuples([(1,Timestamp('20130101')),(2,Timestamp('20150101'))],names=['first','seconds']))

In [25]: pd.concat([s,s2],axis=1)
Out[25]: 
                    0    1
first seconds             
1     2013-01-01    a    a
2     2014-01-01    b  NaN
      2015-01-01  NaN    b

This is technically a bug, but really really frowned upon.

In [10]: s = Series(['a','b'],index=pd.MultiIndex.from_arrays([[1,2],Index([datetime.date(2013,1,1),datetime.date(2014,1,1)])]))

In [11]: s
Out[11]: 
1  2013-01-01    a
2  2014-01-01    b
dtype: object

In [12]: s = Series(['a','b'],index=pd.MultiIndex.from_arrays([[1,2],Index([datetime.date(2013,1,1),datetime.date(2014,1,1)])],names=['first','second']))

In [13]: s
Out[13]: 
first  second    
1      2013-01-01    a
2      2014-01-01    b
dtype: object

In [14]: s.index.levels[1].values
Out[14]: array([datetime.date(2013, 1, 1), datetime.date(2014, 1, 1)], dtype=object)

In [15]: pd.concat([s,s],axis=1)
Out[15]: 
                  0  1
first second          
1     2013-01-01  a  a
2     2014-01-01  b  b

In [16]: s = Series(['a','b'],index=pd.MultiIndex.from_arrays([[1,2],Index([datetime.date(2013,1,1),datetime.date(2014,1,1)])],names=['first','second']))

In [17]: s2 = Series(['a','b'],index=pd.MultiIndex.from_arrays([[1,2],Index([datetime.date(2013,1,1),datetime.date(2015,1,1)])],names=['first','second']))

In [18]: s
Out[18]: 
first  second    
1      2013-01-01    a
2      2014-01-01    b
dtype: object

In [19]: s.index.levels[1].values
Out[19]: array([datetime.date(2013, 1, 1), datetime.date(2014, 1, 1)], dtype=object)

In [20]: pd.concat([s,s2],axis=1)
Out[20]: 
                    0    1
first second              
1     2013-01-01  NaN  NaN
2     2014-01-01  NaN  NaN
      2015-01-01  NaN  NaN

I prop won't ever get to this. This really requires creation of a new dtype to support these. I don't think its worth it nor is it useful in any way.

I mark it as a bug in any event.

[jreback]

the one thing I think might useful is a note in the doc-string for MultiIndex and a note in the docs to avoid using datetime.date. Would you do a pull-request?

[timfeirg]

I think I will do that.
BTW I notice that if I pd.merge on two dataframes with multiindex, one level of which is datetime.datetime, they gets automatically converted pandas TimeStamp, I wonder if pd.concat could have the same behavior

[mroeschke]

This looks to work on master now. Could use a test

In [106]: s
Out[106]:
first  second
1      2013-01-01    a
2      2014-01-01    b
dtype: object

In [107]: s2
Out[107]:
first  second
1      2013-01-01    a
2      2015-01-01    b
dtype: object

In [108]: pd.concat([s,s2],axis=1)
Out[108]:
                    0    1
first second
1     2013-01-01    a    a
2     2014-01-01    b  NaN
      2015-01-01  NaN    b

In [109]: pd.__version__
Out[109]: '1.1.0.dev0+1027.g767335719'