[Bug]: Plotting of Pandas DataFrame data with nanosecond timestamps

[wolfig]

Bug summary

When plotting time-series data from a high-frequency data source, the plot looks distorted when the values are plotted against a nanosecond timestamp.

I am working in PyCharm 2021.2.2 Community Edition, without Jupiter notebook

Code for reproduction

import pandas as pd
from pandas import DataFrame
from matplotlib import pyplot as plt


if __name__ == '__main__':
    data = DataFrame(pd.read_hdf('YRT1DT2F_rawdata_series_2022-05-10_0h-6h.h5', 'YRT1DT2F'))
    data = data[pd.to_datetime('2022-05-10 04:28:32.573995', utc=False, unit='ns'):pd.to_datetime('2022-05-10 04:28:32.574', utc=False, unit='ns')]

    print(data)

    plt.plot(data['value']) # produces bad output
    plt.show()
    plt.plot(data['value'].values) # produces good output
    plt.show()

Actual outcome

Plot result of data['values'].values

Plot result of data['values']

Expected outcome

I would expect the plot result to lookalike "good plot", but with time-scaled x-Axis

Additional information

No response

Operating system

MacOS 12.4 (Monterey)

Matplotlib Version

3.5.2

Matplotlib Backend

MacOSX

Python version

3.9.13

Jupyter version

No response

Installation

pip

[wolfig]

As it seems, matplotlib cannot deal with nanosecond timestamps

[jklymak]

Can you provide a self contained example that doesn't involve pandas, otherwise please open an issue with them. Thanks.

[jklymak]

However, I agree that just using Matplotlib does not give optimal results either:

from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
import numpy as np

times = (np.array([5, 7, 15, 23, 68], dtype='timedelta64[ns]') +
         np.datetime64('2022-07-26T04:50:20', 'ns'))

fig, ax = plt.subplots()
ax.plot(times, np.array([5, 7, 15, 23, 68]))
plt.show()

I think there have been attempts to fix this in the past, but it usually bumps up against the fact that nano-seconds are probably best plotted as deltas from a starting time.