Description
Code Sample, a copy-pastable example if possible
import pandas as pd
pd.Period(pd.to_datetime('20170101 11:52:30'), freq='4H')
Out[1]: Period('2017-01-01 11:00', '4H')
pd.Period(pd.to_datetime('20170101 10:32'), '1H')
Out[2]: Period('2017-01-01 10:00', 'H')
pd.Period(pd.to_datetime('20170101 11:56'), '1H')
Out[3]: Period('2017-01-01 11:00', 'H')
# All three correctly rounding down the time.
# Using a frequency less than 1H introduces this problem.
pd.Period(pd.to_datetime('20170101 11:56'), '30min')
Out[4]: Period('2017-01-01 11:56', '30T')
# Notice the time here. instead of round 11:30 it is 11:56
pd.Period(pd.to_datetime('20170101 11:52'), '30min')
Out[5]: Period('2017-01-01 11:52', '30T')Problem description
When using pd.Period with frequency over one hour, everything works as expected, but using freq=30min or similar, does not produce the expected results as the ones of freq=1H.
This issue looks similar to #17053 but not for the same timescale.
Expected Output
pd.Period(pd.to_datetime('20170101 11:56'), '30min')
Out[3]: Period('2017-01-01 11:30', '30T')
pd.Period(pd.to_datetime('20170101 11:52'), '30min')
Out[4]: Period('2017-01-01 11:30', '30T')Output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.20.3
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.4.0
Cython: 0.26
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 0.9.8
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None