pandas.plotting.parallel_coordinates resets random seed!

[jasonzutty]

Code Sample, a copy-pastable example if possible

>>> x = pd.DataFrame(np.zeros((10,10)))
>>> x
     0    1    2    3    4    5    6    7    8    9
0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
6  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
7  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
8  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
9  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
>>> pandas.plotting.parallel_coordinates(x,0)
<matplotlib.axes._subplots.AxesSubplot object at 0x10b6b11d0>
>>> random.random()
0.25891675029296335
>>> pandas.plotting.parallel_coordinates(x,0)
<matplotlib.axes._subplots.AxesSubplot object at 0x10b6b11d0>
>>> random.random()
0.25891675029296335

Problem description

A call to the pandas.plotting.parallel_coordinates method resets the random seed

Expected Output

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]
INSTALLED VERSIONS

commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.20.3
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.2.7
Cython: 0.26
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 5.3.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[jasonzutty]

Checked it on another computer and it resets to the same random seed, yielding the same output from random.random()

Here is the version info for that computer:
INSTALLED VERSIONS

commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
statsmodels: None
xarray: None
IPython: 5.3.0
sphinx: 1.5.3
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None

[TomAugspurger]

Thanks for the bug report. Looks like the issue is at

pandas/pandas/plotting/_style.py

Lines 113 to 120 in f11bbf2

	elif color_type == 'random':
	import random
	def random_color(column):
	random.seed(column)
	return [random.random() for _ in range(3)]
	colors = lmap(random_color, lrange(num_colors))

Could you submit a fix @jasonzutty?

[jasonzutty]

I'll take a crack at it, just remove the seed?

[TomAugspurger]

Maybe a numpy RandomContext, to get reproducible colors? I haven't looked closely.

[cmazzullo]

Just submitted a pull request with a fix using random.getstate and random.setstate. Here's some output showing that the behavior looks right:

>>> # The random seed isn't reset by the function now:
>>> x = pandas.DataFrame(numpy.zeros((10,10)))
>>> pandas.plotting.parallel_coordinates(x,0)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fc1408b7d68>
>>> random.random()
0.5274572977380592
>>> pandas.plotting.parallel_coordinates(x,0)
<matplotlib.axes._subplots.AxesSubplot object at 0x7fc1408b7d68>
>>> random.random()
0.23087467871474676
>>> # And the function still produces consistent colors:
>>> pandas.plotting._style._get_standard_colors(1, color_type='random')
[[0.8444218515250481, 0.7579544029403025, 0.420571580830845]]
>>> pandas.plotting._style._get_standard_colors(1, color_type='random')
[[0.8444218515250481, 0.7579544029403025, 0.420571580830845]]