The cause:

After researching your problem for hours, I found that when you utilized an AutoLocator while changing the view or zooming, the labels frequently finished applying to the incorrect ticks. Therefore, this error happened.

Solution:

Let’s use FixedLocator to get rid of the warning (that is part of matplotlib.ticker). We have provided a code to plot three charts below. We use many formats for their axes. Although “set ticks” silences the warning, it alters the locations and labels of the real ticks (we spend a lot of time on finding that FixedLocator uses the same information but it still holds the original locations of ticks). To explore how each answer might impact the result, experiment with the x/ y’s.

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as mticker

mpl.rcParams['font.size'] = 6.5

x = np.array(range(1000, 5000, 500))
y = 37*x

fig, [ax1, ax2, ax3] = plt.subplots(1,3)

ax1.plot(x,y, linewidth=5, color='green')
ax2.plot(x,y, linewidth=5, color='red')
ax3.plot(x,y, linewidth=5, color='blue')

label_format = '{:,.0f}'

# there is not anything done to ax1 because it is a "control chart."

# fix yticks with "set_yticks"
ticks_loc = ax2.get_yticks().tolist()
ax2.set_yticks(ax1.get_yticks().tolist())
ax2.set_yticklabels([label_format.format(x) for x in ticks_loc])

# fix yticks with matplotlib.ticker "FixedLocator"
ticks_loc = ax3.get_yticks().tolist()
ax3.yaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
ax3.set_yticklabels([label_format.format(x) for x in ticks_loc])

# fix xticks with FixedLocator but continues using MaxNLocator to prevent cramped x-labels
ax3.xaxis.set_major_locator(mticker.MaxNLocator(3))
ticks_loc = ax3.get_xticks().tolist()
ax3.xaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
ax3.set_xticklabels([label_format.format(x) for x in ticks_loc])

fig.tight_layout()
plt.show()

THE FOLLOWING ARE THE OUTPUT CHARTS. There is a few unused lines of code as the one above (we simply collect the xticks or yticks and set them again), only makes our application noisier. The warning should be removed, in our opinion. The contributors who administer matplotlib have their reasons for maintaining the warning, though, if you check into some of the “bug reports” (from links on the comments above/below; the problem is not truly an error: it is an update that is causing some issues). MATPLOTLIB ANCIENT VERSION: The warning messages could be troublesome if you utilize your console to manage negative code outputs, like we do. Therefore, downgrading matplotlib to version 3.2.2 is a way to put off having to deal with the problem. Here is the command used to downgrade matplotlib in Anaconda, which we use to manage our Python packages:

conda install matplotlib=3.2.2

Howerver, some of the listed versions might not be accessible. For example, in spite of appearance on matplotlib’s releases page, we were unable to install matplotlib 3.3.0: https://github.com/matplotlib/matplotlib/releases