Ts.plot () and dataFrame.plot (): "NameError: name" _converter "not defined"

Question

When starting a method plot()for a data block or series, python throws an error. Last line of errorNameError: name '_converter' is not defined

I am using Python 3.6, and all other functions work as expected, so are not sure what might cause this.

Below is an example of the code that causes the problem, and below is the error that occurs.

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

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()

The returned error is shown below:

NameError                                 Traceback (most recent call last)
<ipython-input-336-8fe4bd433d4d> in <module>()
----> 1 ts.plot()
      2 
      3 plt.plot(ts)

c:\users\fguih\appdata\local\programs\python\python36\lib\site-packages\pandas\plotting\_core.py in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   2501                            colormap=colormap, table=table, yerr=yerr,
   2502                            xerr=xerr, label=label, secondary_y=secondary_y,
-> 2503                            **kwds)
   2504     __call__.__doc__ = plot_series.__doc__
   2505 

c:\users\fguih\appdata\local\programs\python\python36\lib\site-packages\pandas\plotting\_core.py in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   1925                  yerr=yerr, xerr=xerr,
   1926                  label=label, secondary_y=secondary_y,
-> 1927                  **kwds)
   1928 
   1929 

c:\users\fguih\appdata\local\programs\python\python36\lib\site-packages\pandas\plotting\_core.py in _plot(data, x, y, subplots, ax, kind, **kwds)
   1725                             pass
   1726                 data = series
-> 1727         plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
   1728 
   1729     plot_obj.generate()

c:\users\fguih\appdata\local\programs\python\python36\lib\site-packages\pandas\plotting\_core.py in __init__(self, data, **kwargs)
    929 
    930     def __init__(self, data, **kwargs):
--> 931         MPLPlot.__init__(self, data, **kwargs)
    932         if self.stacked:
    933             self.data = self.data.fillna(value=0)

c:\users\fguih\appdata\local\programs\python\python36\lib\site-packages\pandas\plotting\_core.py in __init__(self, data, kind, by, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, fig, title, xlim, ylim, xticks, yticks, sort_columns, fontsize, secondary_y, colormap, table, layout, **kwds)
     98                  table=False, layout=None, **kwds):
     99 
--> 100         _converter._WARN = False
    101         self.data = data
    102         self.by = by

NameError: name '_converter' is not defined

Answer 1

You can get this to work if you tie the time series to the daily frequency before plotting using pd.Series.asfreq:

ts.asfreq('D').plot(); 
plt.show()

Answer 2

nbkhope:

python

jupyter notebook

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