Plotting series with different frequency yields "ValueError: Incompatible frequency conversion"

[mmajewsk]

So i have two dataframes with simmilar data but, indexed by datetime64
dfA : http://pastebin.com/HenYJxdV
dfB : http://pastebin.com/qAsEqSxf

so i have following problem when i try to plot them this way:

In [107]:

seriesA=dfA["more"]
seriesB=dfB["less"]
In [108]:

plt.figure()
plt.plot(seriesA.index.tolist(),seriesA.tolist())
plt.plot(seriesB.index.tolist(),seriesB.tolist())

it's ok and both series have been plotted on same figure

but when i'll try this:

plt.figure()
seriesA.plot()
seriesB.plot()

it wrecks and i get following output

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-109-5054484e1e31> in <module>()
      1 plt.figure()
      2 seriesA.plot()
----> 3 seriesB.plot()
      4 

C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
   2254                      secondary_y=secondary_y, **kwds)
   2255 
-> 2256     plot_obj.generate()
   2257     plot_obj.draw()
   2258 

C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in generate(self)
    900         self._compute_plot_data()
    901         self._setup_subplots()
--> 902         self._make_plot()
    903         self._add_table()
    904         self._make_legend()

C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in _make_plot(self)
   1549         if self._is_ts_plot():
   1550             data = self._maybe_convert_index(self.data)
-> 1551             self._make_ts_plot(data)
   1552         else:
   1553             from pandas.core.frame import DataFrame

C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in _make_ts_plot(self, data, **kwargs)
   1646             y_values = self._get_stacked_values(y, label)
   1647 
-> 1648             newlines = plotf(y_values, ax, label, style, **kwds)
   1649             self._add_legend_handle(newlines[0], label, index=i)
   1650 

C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tools\plotting.pyc in _plot(data, ax, label, style, **kwds)
   1623             else:
   1624                 lines = tsplot(data, plotf, ax=ax, label=label,
-> 1625                                style=style, **kwds)
   1626                 return lines
   1627         return _plot

C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tseries\plotting.pyc in tsplot(series, plotf, **kwargs)
     57             series = series.to_period(freq=freq)
     58         freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf,
---> 59                                                 kwargs)
     60 
     61     # Set ax with freq info

C:\winpy\WinPython-64bit-2.7.6.4\python-2.7.6.amd64\lib\site-packages\pandas\tseries\plotting.pyc in _maybe_resample(series, ax, freq, plotf, kwargs)
    107             ax_freq = freq
    108         else:  # pragma: no cover
--> 109             raise ValueError('Incompatible frequency conversion')
    110     return freq, ax_freq, series
    111 

ValueError: Incompatible frequency conversion

Which is extremely weird because before, i plotted similar but yet slightly different data and it plotted correctly.

P.S. pd.show_versions():

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 37 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.14.0
nose: 1.3.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None

[mmajewsk]

further investigation revealed that there is a problem with index.inferred_freq which is being set to "100l" and it's done where my code calls df = df.set_index('column_name')