You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I'm not sure whether joining of DFs with non-unique indices is now supported, but it's not giving an error and this simple example don't make sense:
In [11]: df1 = pandas.DataFrame({'x': ['a']}, index=[np.datetime64('2012')])
In [12]: df2 = pandas.DataFrame({'y': ['b', 'c']}, index=[np.datetime64('2012')] * 2)
In [13]: df1
Out[13]:
x
1970-01-16 08:09:36 a
In [14]: df2
Out[14]:
y
1970-01-16 08:09:36 b
1970-01-16 08:09:36 c
In [15]: df1.join(df2, how='inner')
Out[15]:
x y
1970-01-16 08:09:36 a b
Shouldn't the 1st row of df1 join to both rows of df2?
The text was updated successfully, but these errors were encountered:
It looks to me like an edge case, I'll look into it. I'll fix the timestamp issue too; unfortunately the NumPy datetime API is a disaster in NumPy 1.6.1 and I'm doing my best to work around it. Affairs will be much improved in NumPy 1.7 and later
I'm not sure whether joining of DFs with non-unique indices is now supported, but it's not giving an error and this simple example don't make sense:
Shouldn't the 1st row of df1 join to both rows of df2?
The text was updated successfully, but these errors were encountered: