Issue 1:
I have DataFrame:

import pandas as pd
import datetime as datetime

a = pd.DataFrame({'id': [1, 2, 3, 2],
'my_date': [datetime.datetime(2017, 1, i) for i in range(1, 4)] + [datetime.datetime(2017, 1, 1)],
'num': [2, 3, 1, 4]
})

print (a.dtypes)
id int64
my_date datetime64[ns]
num int64
dtype: object

I try aggreagate by pd.Series.nunique, but get wrong output for datetimes:

grouped_a = a.groupby('id').agg({'my_date': pd.Series.nunique,
'num': pd.Series.nunique}).reset_index()
grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date']

print (grouped_a)
id num_unique_num num_unique_my_date
0 1 1 1970-01-01 00:00:00.000000001
1 2 2 1970-01-01 00:00:00.000000002
2 3 1 1970-01-01 00:00:00.000000001

My solution is use nunique which works nice.

grouped_a = a.groupby('id').agg({'my_date': 'nunique',
'num': 'nunique'}).reset_index()
grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date']
print (grouped_a)
id num_unique_num num_unique_my_date
0 1 1 1
1 2 2 2
2 3 1 1

But why does not work first solution? Bug?

SO question.

print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
LOCALE: None.None

pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
None

Issue 2:
xref #14873 (boolean casts)

xref #14849 (datetime)

A small, complete example of the issue

import pandas as pd

def predictions(tool):
out = pd.Series(index=['p1', 'p2', 'useTime'], dtype=object)
if 'step1' in list(tool.State):
out['p1'] = str(tool[tool.State == 'step1'].Machine.values[0])
if 'step2' in list(tool.State):
out['p2'] = str(tool[tool.State == 'step2'].Machine.values[0])
out['useTime'] = str(tool[tool.State == 'step2'].oTime.values[0])
return out

df1 = pd.DataFrame({'Key': ['B', 'B', 'A', 'A'],
'State': ['step1', 'step2', 'step1', 'step2'],
'oTime': ['', '2016-09-19 05:24:33', '', '2016-09-19 23:59:04'],
'Machine': ['23', '36L', '36R', '36R']})

df2 = df1.copy()
df2.oTime = pd.to_datetime(df2.oTime)

pred1 = df1.groupby('Key').apply(predictions)
pred2 = df2.groupby('Key').apply(predictions)

print(pred1)
print(pred2)

Actual Output:

p1 p2 useTime
Key
A 36R 36R 2016-09-19 23:59:04
B 23 36L 2016-09-19 05:24:33
p1 p2 useTime
Key
A NaN 36R 2016-09-19T23:59:04.000000000
B 23.0 36L 2016-09-19T05:24:33.000000000

Expected Output

pred1 and pred2 should have the same values in column p1.

pred1 is correct whereas pred2 is changing type to float64.

Output of pd.show_versions()

## INSTALLED VERSIONS

commit: None

python: 3.5.2.final.0

python-bits: 64

OS: Darwin

OS-release: 14.5.0

machine: x86_64

processor: i386

byteorder: little

LC_ALL: en_US.UTF-8

LANG: en_US.UTF-8

LOCALE: en_US.UTF-8

pandas: 0.19.0

nose: None

pip: 8.1.2

setuptools: 27.2.0

Cython: None

numpy: 1.11.2

scipy: 0.18.1

statsmodels: None

xarray: None

IPython: 5.1.0

sphinx: None

patsy: None

dateutil: 2.5.3

pytz: 2016.7

blosc: None

bottleneck: None

tables: 3.2.3.1

numexpr: 2.6.1

matplotlib: 1.5.3

openpyxl: None

xlrd: None

xlwt: None

xlsxwriter: None

lxml: None

bs4: None

html5lib: None

httplib2: None

apiclient: None

sqlalchemy: 1.1.0

pymysql: None

psycopg2: None

jinja2: 2.8

boto: None

pandas_datareader: None