Please provide the full traceback. Could you also show df.info()? In any
case, I suspect that the problem is that pandas recently started using
datetime64 for their timeseries, and matplotlib hasn't implemented the unit
converter for it. There was a post recently showing how to add pandas's
converter to matplotlib's unit framework, but I can't find it right now...
Cheers!
Ben Root
On Tue, Dec 2, 2014 at 9:24 AM, Fabien <[email protected]> wrote:
> Folks,
>
> I'm trying to use BrokenBarHCollection with pandas timeseries object.
>
> Here's a minimal example: (python 3.3, pandas 0.15.1, matplotlib 1.4.2)
>
> #-----------------------------------------------------
> import pandas as pd
> import numpy as np
> from datetime import datetime as dt
> import matplotlib.pyplot as plt
> import matplotlib.collections as collections
> span_where = collections.BrokenBarHCollection.span_where
>
> # init the dataframe
> time = pd.date_range(pd.datetime(1950,1,1), periods=5, freq='MS')
> df = pd.DataFrame(np.arange(5), index=time, columns=['data'])
> df['cond'] = df['data'] == 3
>
> # Make the plot
> fig = plt.figure()
> ax = fig.add_subplot(111)
> df['data'].plot(ax=ax, c='black')
> c = span_where(df.index, ymin=0, ymax=4, where=df['cond'],
> facecolor='green', alpha=0.5)
> #-----------------------------------------------------
>
> I get the error:
> "TypeError: float() argument must be a string or a number"
>
> Basically, span_where() is not happy with my x values which are a panda
> timeserie. I tried several stuffs (df.index.to_*) but there is something
> I still don't get in the internal representation of dates in matplolib.
>
> Any hint? Thanks a lot!
>
> Fabien
>
>
>
>
>
>
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