Matplotlib bar plot with pandas Timestamp

Question

I am having trouble making this very simple example work:

from numpy import datetime64

from pandas import Series

import matplotlib.pyplot as plt

import datetime



x = Series ([datetime64("2016-01-01"),datetime64("2016-02-01")]).astype(datetime)

y = Series ([0.1 , 0.2])



ax = plt.subplot(111)

ax.bar(x, y, width=10)

ax.xaxis_date()



plt.show()

The error I get is:

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

Note the

astype(datetime)

piece - that is something I tried after reading this other SO post. Without that piece, I get the same error.

On the other hand, the example works well enough with plain

datetime64

types - that is, changing these two lines:

x = [datetime64("2016-01-01"),datetime64("2016-02-01")]

y = [0.1 , 0.2]

So the issue must be the

Timestamp

type that pandas converts the

datetime64

objects into. Is there a way to make this work with

Timestamp

directly, and not revert to

datetime64

? I'm using

Series

/

Timestamp

here because my real objective is plotting series from a

DataFrame

. (Note: I cannot use the

DataFrame

plotting methods because my real example is inside a seaborn

FacetGrid

and I have to use matplotlib directly.)

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Answer 1

Use:

ax.bar(x.values, y, width=10)

when using the Series objects. The issue is that you are not sending an object that is similar to an array, it is an indexed array that matplotlib does not know how to handle. values returns only the array

Matplotlib bar plot with pandas Timestamp

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