Incorrect timestamps are shown on the x-axis

I have the following bar plot being generated with the following code:

import matplotlib as mplt
from matplotlib import dates, pyplot
from matplotlib.transforms import ScaledTranslation
import numpy as np
import pandas as pd

ts = pd.date_range('2023/01/01', '2023/01/06', freq='3H', tz='utc')
xs = np.arange(len(ts))
df = pd.DataFrame({'date':ts,'value':np.ones(shape=len(ts)), 'intensity':np.random.uniform(0, 10, len(ts))})

colors = []
for i in df.intensity:
    if 0 <= i < 6:
        colors.append('#75FF71')
    elif 6 <= i < 8:
        colors.append('#FFC53D')
    else:
        colors.append('#FF5C5C')

# pyplot.box
fig, ax = pyplot.subplots(figsize = (24,1), constrained_layout=False)
ax.yaxis.set_ticklabels(labels=[])
ax.yaxis.set_visible(False)
ax.grid(False)
ax.set_frame_on(False)

hour_locs = dates.HourLocator(byhour=[6, 12, 18])
hour_locs_fmt = dates.DateFormatter('%H:%M')
ax.xaxis.set_minor_locator(hour_locs)
ax.xaxis.set_minor_formatter(hour_locs_fmt)

day_locs = dates.DayLocator(interval=1)
day_locs_fmt = dates.DateFormatter('%B %-d')
ax.xaxis.set_major_locator(day_locs)
ax.xaxis.set_major_formatter(day_locs_fmt)
ax.xaxis.set_tick_params(which='major', pad=-10, length=40)
ax.bar(df.date, df.value, color=colors)

offset = ScaledTranslation(1.6, 0, fig.dpi_scale_trans)
for label in ax.xaxis.get_majorticklabels():
    label.set_transform(label.get_transform() + offset)

The output:

The timestamps start from 2023/01/01 00:00:00+000 (UTC), however the plot shows that the data is starting at ~15:00 the day before. I’m assuming that matplotlib is ignoring the timezone in the data.

I did try specifying TZ in the locators and formatter in vain.

How do I get matplotlib to plot in UTC?

>Solution :

As you are plotting bars, it adds a little extra space on either ends. This is the reason you are seeing the extra space. At the end, add this line (ax.set_xlim(...)), forcing the plot to start and end at the times you want.

ax.set_xlim(df.date.min(), df.date.max())

You will get the below plot… hope this is what you are looking for…