pandas to_datetime() then concat() on DateTime Index

Question

I'm trying to merge 2 DataFrames using concat , on their DateTime Index, but it's not working as I expected. I copied some of this code from the example in the documentation for this example:

import pandas as pd

df = pd.DataFrame({'year': [2015, 2016],
                   'month': [2, 3],
                   'day': [4, 5],
                   'value': [444,555]})

df.set_index(pd.to_datetime(df.loc[:,['year','month','day']]),inplace=True)

df.drop(['year','month','day'],axis=1,inplace=True)

df2 = pd.DataFrame(data=[222,333],
                   index=pd.to_datetime(['2015-02-04','2016-03-05']))

pd.concat([df,df2])
Out[1]: 
            value      0
2015-02-04  444.0    NaN
2016-03-05  555.0    NaN
2015-02-04    NaN  222.0
2016-03-05    NaN  333.0

Why isn't it recognizing the same dates on the index and merging accordingly? I verified that both Indexes are DateTime:

df.index
Out[2]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)

df2.index
Out[3]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)

Thanks.

Answer 1

pass axis=1 to concatenate column-wise:

In [7]:
pd.concat([df,df2], axis=1)

Out[7]:
            value    0
2015-02-04    444  222
2016-03-05    555  333

Alternatively you could've join ed:

In [5]:
df.join(df2)

Out[5]:
            value    0
2015-02-04    444  222
2016-03-05    555  333

or merge d:

In [8]:
df.merge(df2, left_index=True, right_index=True)

Out[8]:
            value    0
2015-02-04    444  222
2016-03-05    555  333

Answer 2

You need axis=1:

pd.concat([df,df2], axis=1)

Output:

            value    0
2015-02-04    444  222
2016-03-05    555  333