Inconsistent plotting and crashing python on plotting with irregular time series

[vfilimonov]

I believe it relates to the discussion in #15071, but I still think that reporting a test-case would be useful.

Synthetic example of two series

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

d1 = ['2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',
       '2015-01-09', '2015-01-12', '2015-01-13', '2015-01-14',
       '2015-01-15', '2015-01-16', '2015-01-19', '2015-01-20',
       '2015-01-21', '2015-01-22', '2015-01-23', '2015-01-26',
       '2015-01-27', '2015-01-28', '2015-01-29', '2015-01-30',
       '2015-02-02', '2015-02-03', '2015-02-04', '2015-02-05',
       '2015-02-06', '2015-02-09', '2015-02-10', '2015-02-11',
       '2015-02-12', '2015-02-13', '2015-02-16', '2015-02-17',
       '2015-02-25', '2015-02-26', '2015-02-27', '2015-03-02',
       '2015-03-03', '2015-03-04', '2015-03-05', '2015-03-06',
       '2015-03-09', '2015-03-10', '2015-03-11']
d2 = ['2015-01-31', '2015-02-28', '2015-03-31', '2015-04-30']

t1 = pd.Series(np.random.randn(len(d1)), index=pd.to_datetime(d1)).cumsum()+10
t2 = pd.Series(np.random.randn(len(d2)), index=pd.to_datetime(d2)).cumsum()+1

Problem description

Plotting it this way is done correctly:

t1.plot()
t2.plot()

If plotting in opposite order, it gives incorrect result:

t2.plot()
t1.plot()

Same problem if using secondary_y:

t2.plot()
t1.plot(secondary_y=True)

And if I try to plot it in the first order using secondary_y - it crashes the python (in IPython notebook - on the cell evaluation, in console - on plt.show()):

t1.plot()
t2.plot(secondary_y=True)

P.s. using plot(..., x_compat=True) resolves these issues.

Output of pd.show_versions()

The pandas version is 0.19.2+0.g825876c.dirty, which was installed as a default option by anaconda.

INSTALLED VERSIONS ------------------ commit: None python: 2.7.12.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 63 Stepping 0, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None

pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.0
bs4: 4.5.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: 0.2.0

[jong1prk]

sorry for late comment, but I just check what you said. Can you explain the reason why that results happens? I don't understand why the result changes by just changing the order of codes.