sample not using numpy's random state #13143
Description
After fixing a random seed with numpy.random.seed, I expect sample to yield the same results.
Expected behavior of numpy.random.choice but found something different. Here is pandas:
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(np.arange(1000))
In [12]: np.random.seed(5); df.sample(2)
Out[12]:
0
824 824
225 225
In [13]: np.random.seed(5); df.sample(2)
Out[13]:
0
182 182
586 586Whereas numpy.random.choice is consistent
In [6]: np.random.seed(5); np.random.choice(1000)
Out[6]: 867
In [7]: np.random.seed(5); np.random.choice(1000)
Out[7]: 867
output of pd.show_versions()
In [8]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-67-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.1
pip: 8.1.1
setuptools: 18.4
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None