Code Sample, a copy-pastable example if possible

# Your code here
>>> loc1series4 = pd.Series([6,8,10,12,14,9,11,7,13,11])

Problem description

loc1series4.quantile(0.25) 8.25 loc1series4.quantile(0.75) 11.75 Wrong result. [this should explain why the current behaviour is a problem and why the expected output is a better solution.]

Expected Output

loc1series4.quantile(0.25) 7.75 loc1series4.quantile(0.75) 12.25

Output of pd.show_versions()

# Paste the output here pd.show_versions() here >>> pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Darwin OS-release: 16.3.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 pandas: 0.19.1 nose: None pip: 9.0.1 setuptools: 27.2.0 Cython: None numpy: 1.11.2 scipy: None statsmodels: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: None tables: None numexpr: None matplotlib: 1.5.3 openpyxl: None xlrd: 1.0.0 xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None boto: None pandas_datareader: None

Comment From: dileepponna

This is using 'interpolation=linear' scenario.

Comment From: Dr-Irv

The docs say this is like numpy.percentile. If you look at those docs at https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.percentile.html, you will see that the quantile is defined as being between the minimum and the maximum, i.e., the minimum=0, the maximum=1.0. So the result is correct.

Comment From: jreback

yep, this is outlined in the docs.