Inconsistent Timestamp arithmetic

[willpan]

Subtracting a scalar datetime from a series returns timedeltas, but doesn't do the same for a dataframe.

import pandas as pd 
series = pd.Series(pd.date_range('2000-1-1', periods=100), index=range(0, 100))
df = pd.DataFrame(series)
date = pd.Timestamp('1995-1-1')

series - date
#0    1826 days                                                                                 
#1    1827 days                                                                                 
#2    1828 days
# ....   

df - date
#             0
#0  1975-01-01                                                                                  
#1  1975-01-02                                                                                  
#2  1975-01-03
# ....

It seems like both should return timedeltas.

[jreback]

hmm, this should work, marking as a bug

[jreback]

this seems trivial, but is actually complicated because of this:

In [11]: df = DataFrame({'A' : np.arange(5), 'B' : pd.date_range('20130101',periods=5),'C' : 'foo'})

In [12]: df
Out[12]: 
   A          B    C
0  0 2013-01-01  foo
1  1 2013-01-02  foo
2  2 2013-01-03  foo
3  3 2013-01-04  foo
4  4 2013-01-05  foo

In [13]: df.dtypes
Out[13]: 
A             int64
B    datetime64[ns]
C            object
dtype: object

practically no arithmetic operations make sense with this frame, .eg
df+1, df-Timestamp('20130101') all blow up

that said I agree a compatible dtyped op should work

e.g.

df.select_dtypes(include=['datetime'])-Timestamp('20130101') should work
(and .... + Timestamp('20130101') should raise.

If these were series all is good, and will all work appropriately (and is easy to achive
by applying these ops).