如何强制整数刻度标签

Based on an answer for modifying tick labels I came up with a solution, don't know whether it will work in your case as your code snippet can't be executed in itself.

The idea is to force the tick labels to a .5 spacing, then replace every .5 tick with its integer counterpart, and others with an empty string.

import numpy
import matplotlib.pyplot as plt


fig, (ax1, ax2) = plt.subplots(1,2)


x1, x2 = 1, 5
y1, y2 = 3, 7


# first axis: ticks spaced at 0.5
ax1.plot([x1, x2], [y1, y2])
ax1.set_xticks(numpy.arange(x1-1, x2+1, 0.5))
ax1.set_yticks(numpy.arange(y1-1, y2+1, 0.5))


# second axis: tick labels will be replaced
ax2.plot([x1, x2], [y1, y2])
ax2.set_xticks(numpy.arange(x1-1, x2+1, 0.5))
ax2.set_yticks(numpy.arange(y1-1, y2+1, 0.5))


# We need to draw the canvas, otherwise the labels won't be positioned and
# won't have values yet.
fig.canvas.draw()


# new x ticks  '1'->'', '1.5'->'1', '2'->'', '2.5'->'2' etc.
labels = [item.get_text() for item in ax2.get_xticklabels()]
new_labels = [ "%d" % int(float(l)) if '.5' in l else '' for l in labels]
ax2.set_xticklabels(new_labels)


# new y ticks
labels = [item.get_text() for item in ax2.get_yticklabels()]
new_labels = [ "%d" % int(float(l)) if '.5' in l else '' for l in labels]
ax2.set_yticklabels(new_labels)


fig.canvas.draw()
plt.show()

If you want to zoom out a lot, that will need some extra care, as this one produces a very dense set of tick labels then.

This should be simpler:

(from https://scivision.co/matplotlib-force-integer-labeling-of-axis/)

import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
#...
ax = plt.figure().gca()
#...
ax.xaxis.set_major_locator(MaxNLocator(integer=True))

The following solution by simply casting the index i to string worked for me:

    import matplotlib.pyplot as plt
import time


datay = [1,6,8,4] # Just an example
datax = []
    

# In the following for loop datax in the end will have the same size of datay,
# can be changed by replacing the range with wathever you need
for i in range(len(datay)):
# In the following assignment statement every value in the datax
# list will be set as a string, this solves the floating point issue
datax += [str(1 + i)]


a = plt


# The plot function sets the datax content as the x ticks, the datay values
# are used as the actual values to plot
a.plot(datax, datay)


a.show()

ax.set_xticks([2,3])
ax.set_yticks([2,3])