TypeError: float() argument must be a string or a number, not 'Period'

Question

I have a pandas dataframe with columns like this:

df.columns = pd.to_datetime(list(df)) #list(df) = ["2017-01", "2016-01", ...]

Then I performed an interpolation in each row of the dataset because I have some NaNs that I want to get rid off. Here is the result printed:

ORIGINAL  
2007-12-01     NaN 
2008-12-01     NaN 
2009-12-01     NaN 
2010-12-01   -0.35 
2011-12-01    0.67 
2012-12-01     NaN 
2013-12-01     NaN 
2014-12-01    1.03 
2015-12-01    0.37 
2016-12-01     NaN 
2017-12-01     NaN 
Name: row1, dtype: float64 

INTERPOLATION  
2007-12-01   -0.350000 
2008-12-01   -0.350000 
2009-12-01   -0.350000 
2010-12-01   -0.350000 
2011-12-01    0.670000 
2012-12-01    0.790219 
2013-12-01    0.910109 
2014-12-01    1.030000 
2015-12-01    0.370000 
2016-12-01    0.370000 
2017-12-01    0.370000 
Name: row1, dtype: float64

Then I try to plot the interpolated row and get:

TypeError: float() argument must be a string or a number, not 'Period' 

The whole code:

print("ORIGINAL\n", series)
interpolation = series.interpolate(method=func, limit=10, limit_direction='both')
interpolation.plot()
print("INTERPOLATION\n",interpolation)

It seems to me that the error is in the time values in the series, but I think matplotlib should be hable to handle it, so I'm doing something wrong for sure. Thanks in advance.

Answer 1

Here is the simplest answer,No need to upgrade or downgrade the pandas.

pd.plotting.register_matplotlib_converters()

sometime registering causing another error which is like compute.use_bottleneck, use_numexpr error for getting rid of that call deregister :P

Like: pd.plotting.deregister_matplotlib_converters()

source:Link

Answer 2

This is a bug in Pandas, and will be fixed by the next major release by August 31, 2018 if everything goes swimmingly.

For now, @J63's workaround have to do. That, or install an earlier version of pandas, such as 0.20.2.

Answer 3

It works if I do:

plt.plot(row.index, row.values)
plt.show()

I don't know why though...

Answer 4

Copied your Interpolation results

df = pd.read_clipboard(header=None)
df.columns = ['Period','Value']
df['Period'] = pd.to_datetime(df['Period'])
df  = df.set_index('Period')
print(df)

               Value
Period              
2007-12-01 -0.350000
2008-12-01 -0.350000
2009-12-01 -0.350000
2010-12-01 -0.350000
2011-12-01  0.670000
2012-12-01  0.790219
2013-12-01  0.910109
2014-12-01  1.030000
2015-12-01  0.370000
2016-12-01  0.370000
2017-12-01  0.370000


df.plot()