numpy - Python Pandas - Reformat Datetime Index -

i have 2 data frames both multi-indexed on 'date' , 'name', , want sql style join combine them. i've tried

pd.merge(df1.reset_index(), df2.reset_index(), on=['name', 'date'], how='inner') 

which results in empty dataframe.

if inspect data frames can see index of 1 represented '2015-01-01' , other represented '2015-01-01 00:00:00' explains issues joining.

is there way 'recast' index specific format within pandas?

i've included tables see data i'm working with.

df1= +-------------+------+------+------+ |    date     | name | col1 | col2 | +-------------+------+------+------+ | 2015-01-01  | mary |   12 |  123 | | 2015-01-02  | mary |   23 |   33 | | 2015-01-03  | mary |   34 |   45 | | 2015-01-01  | john |   65 |   76 | | 2015-01-02  | john |   67 |   78 | | 2015-01-03  | john |   25 |   86 | +-------------+------+------+------+  df2=  +------------+------+-------+-------+ |    date    | name | col3  | col4  | +------------+------+-------+-------+ | 2015-01-01 | mary | 80809 | 09885 | | 2015-01-02 | mary | 53879 | 58972 | | 2015-01-03 | mary | 23887 |  3908 | | 2015-01-01 | john |  9238 |  2348 | | 2015-01-02 | john |   234 |   234 | | 2015-01-03 | john |  5325 |  6436 | +------------+------+-------+-------+ 

desired result:

+-------------+------+------+-------+-------+-------+ |    date     | name | col1 | col2  | col3  | col4  | +-------------+------+------+-------+-------+-------+ | 2015-01-01  | mary |   12 |   123 | 80809 | 09885 | | 2015-01-02  | mary |   23 |    33 | 53879 | 58972 | | 2015-01-03  | mary |   34 |    45 | 23887 |  3908 | | 2015-01-01  | john |   65 |    76 |  9238 |  2348 | | 2015-01-02  | john |   67 |    78 |   234 |   234 | | 2015-01-03  | john |   25 |    86 |  5325 |  6436 | +-------------+------+------+-------+-------+-------+ 

the reason cannot join because have different dtypes on indicies. pandas silently fails if indicies have different dtypes.

you can change indicies string representations of time proper pandas datetimes this:

df = pd.dataframe({"data":range(1,30)}, index=['2015-04-{}'.format(d) d in range(1,30)])  df.index.dtype dtype('o')  df.index = df.index.to_series().apply(pd.to_datetime)  df.index.dtype dtype('<m8[ns]') 

now can merge dataframes on index:

pd.merge(left=df, left_index=true,          right=df2, right_index=true) 

assuming have df2, example omitting...