Description
Code Sample, a copy-pastable example if possible
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'Number' : [1, 2], 'Date' : ["2017-03-02"] * 2, 'Str' : ["foo", "inf"]})
In [3]: df
Out[3]:
Date Number Str
0 2017-03-02 1 foo
1 2017-03-02 2 inf
In [4]: df.groupby(['Number']).apply(lambda x: x.iloc[0])
Out[4]:
Date Number Str
Number
1 2017-03-02 1 foo
2 2017-03-02 2 inf
In [5]: df.Date = pd.to_datetime(df.Date)
In [6]: df
Out[6]:
Date Number Str
0 2017-03-02 1 foo
1 2017-03-02 2 inf
In [7]: df.groupby(['Number']).apply(lambda x: x.iloc[0])
Out[7]:
Date Number Str
Number
1 2017-03-02 1 NaN
2 2017-03-02 2 infProblem description
When I change the type of the Date column to a Pandas datetime, it causes other columns' types to change in unexpected ways when doing a group-by/apply. Notice the contents of the "Str" column changes to a numeric type in the final group-by/apply (a contributing factor is probably that one of the elements is the string "inf"). The "inf" value has become inf, and the "foo" value has become NaN.
Expected Output
I expect the Str column to remain a string type, and contain the original strings. I.e.:
Date Number Str
0 2017-03-02 1 foo
1 2017-03-02 2 infOutput of pd.show_versions()
pandas: 0.18.1
nose: 1.3.7
pip: None
setuptools: 0.6
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: 0.7.0
IPython: 5.0.0
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.1
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.6.7.None
psycopg2: 2.5.4 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None