Unwanted conversion of `Timestamp` to `long`

[eike-welk]

Method combine_first performs an unwanted conversion of Timestamp to long. Pandas version is "0.11.0". Here is an IPython session demonstrating the behavior, which is IMHO a bug:

In [1]: import pandas as pd

In [2]: pd.__version__
Out[2]: '0.11.0'

In [4]: from datetime import datetime

In [5]: df0 = pd.DataFrame({"a":[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]})

In [6]: df0
Out[6]: 
                    a
0 2000-01-01 00:00:00
1 2000-01-02 00:00:00
2 2000-01-03 00:00:00

In [7]: df1 = pd.DataFrame({"a":[None, None, None]})

In [8]: df1
Out[8]: 
      a
0  None
1  None
2  None

In [9]: df2 = df1.combine_first(df0)

In [10]: df2
Out[10]: 
                    a
0  946684800000000000
1  946771200000000000
2  946857600000000000

In [11]: type(df2["a"][0])
Out[11]: long

In [12]: type(df0["a"][0])
Out[12]: pandas.tslib.Timestamp

I think df2 should contain time stamps like df0.

[jreback]

this is a bug, fixed in #3595 (merging soon)

[jreback]

FYI, using None can result in sometimes weird results, prefer to use np.nan. None are normally converted to np.nan, but if you have a series with all None this does not occur and you have a dtype of object. You can solve this by specifying dtype=float64 or just use np.nan

[jreback]

merged - thanks for the report

[eike-welk]

Thanks for fixing this bug so fast!

And thanks for mentioning None vs. nan in a column that contains timestamps. These Nones are leftovers from the time when I used Pandas 0.7.x (a few days ago) which would not convert datetime objects into Timestamp objects (and further into int64 I suppose).

[jreback]

the None issue was refering to how the series are actually created. For datetime64[ns], missing is represented by NaT (which setting with np.nan will turn into). A bit confusing, but unfortunately this is how numpy deals with types. see here: http://pandas.pydata.org/pandas-docs/dev/missing_data.html#datetimes