这应该更简单：
(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))

Read the official docs: https://matplotlib.org/stable/api/ticker_api.html#matplotlib.ticker.MaxNLocator

下面的解决方案通过简单地将索引i转换为string来实现：

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])

根据modifying tick labels的答案，我想出了一个解决方案，不知道它是否会在您的情况下工作，因为您的代码片段不能在自己执行。
其思想是将刻度标签强制为0.5的间距，然后将每个0.5刻度替换为整数刻度，将其他刻度替换为空字符串。

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()

如果你想缩小很多，这将需要一些额外的照顾，因为这一个产生了一个非常密集的一组刻度标签然后。