I am seeing a slightly annoying issue when plotting some data with matplotlib.

As shown in the example output below, the x-axis has a slightly unexpected format.

The labels are (for example) 04 12:30. The 04 being the day of the month.

The code and data are provided to reproduce this figure, appended at the end of this question.

Why is the x-axis being formatted like this and what can I do to "fix" it? (I don’t want the day of the month to be shown, and I expecially don’t want it to be formatted as "04".)

Code

To run the code: python3 example.py

# example.py

import matplotlib.pyplot as plt
import pandas

df = pandas.read_json('data.json')

print(df.head())

fig, ax1 = plt.subplots(figsize=(12, 8))
ax1.plot(df['column_name'], label='My data')
ax1.set_xlabel('Timestamp')
ax1.set_ylabel('Value')
ax1.grid(True)
ax1.legend()

plt.title('Example')
plt.tight_layout()
plt.savefig('example.png')

Data

Save this file as data.json

{"column_name":{"1649073600000":174.79,"1649073660000":174.8,"1649073720000":174.79,"1649073780000":174.76,"1649073840000":174.69,"1649073900000":174.7,"1649073960000":174.69,"1649074020000":174.65,"1649074140000":174.67,"1649074200000":174.7,"1649074260000":174.74,"1649074320000":174.65,"1649074380000":174.69,"1649074440000":174.65,"1649074500000":174.7,"1649074560000":174.74,"1649074680000":174.72,"1649074740000":174.7,"1649074860000":174.7,"1649074920000":174.71,"1649074980000":174.75,"1649075040000":174.76,"1649075100000":174.73,"1649075160000":174.76,"1649075220000":174.7,"1649075280000":174.66,"1649075340000":174.61,"1649075400000":174.63,"1649075460000":174.65,"1649075520000":174.7,"1649075580000":174.69,"1649075640000":174.63,"1649075760000":174.66,"1649075820000":174.63,"1649075880000":174.58,"1649075940000":174.5,"1649076000000":174.52,"1649076060000":174.55,"1649076120000":174.55,"1649076180000":174.54,"1649076240000":174.48,"1649076300000":174.45,"1649076360000":174.38,"1649076420000":174.22,"1649076480000":174.21,"1649076540000":174.15,"1649076660000":174.25,"1649076720000":174.3,"1649076780000":174.28,"1649076840000":174.26,"1649076900000":174.25,"1649076960000":174.23,"1649077020000":174.21,"1649077080000":174.25,"1649077140000":174.26,"1649077200000":174.27}}