Description
Code Sample
Setup:
import numpy as np
import pandas as pd
index = pd.date_range(start='2018', freq='M', periods=6)
data = np.ones(6)
data[3:6] = np.nan
datetime = pd.Series(data, index)
period = datetime.to_period()Resampling and summing (min_count=1) the Series with a DatetimeIndex:
datetime
datetime.resample('Q').sum(min_count=1)2018-01-31 1.0
2018-02-28 1.0
2018-03-31 1.0
2018-04-30 NaN
2018-05-31 NaN
2018-06-30 NaN
Freq: M, dtype: float64
2018-03-31 3.0
2018-06-30 NaN
Freq: Q-DEC, dtype: float64
Resampling and summing (min_count=1) the Series with a PeriodIndex:
period
period.resample('Q').sum(min_count=1)2018-01 1.0
2018-02 1.0
2018-03 1.0
2018-04 NaN
2018-05 NaN
2018-06 NaN
Freq: M, dtype: float64
2018Q1 3.0
2018Q2 0.0
Freq: Q-DEC, dtype: float64
Problem description
sum() and prod() seem to ignore the min_count argument when used on a resampled series or dataframe with a PeriodIndex.
Expected Output
I would expect the same result whether using a DatetimeIndex or PeriodIndex. Specifically, in the example above, 2018Q2 should be NaN.
Output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.22.0
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
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: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None