在matplotlib中将x轴移动到绘图的顶部

基于关于matplotlib中的热图的问题，我想将x轴标题移动到图的顶部。

import matplotlib.pyplot as plt
import numpy as np
column_labels = list('ABCD')
row_labels = list('WXYZ')
data = np.random.rand(4,4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=plt.cm.Blues)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[0])+0.5, minor=False)
ax.set_yticks(np.arange(data.shape[1])+0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.set_label_position('top') # <-- This doesn't work!

ax.set_xticklabels(row_labels, minor=False)
ax.set_yticklabels(column_labels, minor=False)
plt.show()

但是，调用matplotlib的set_label_position（如上所述）似乎没有达到预期的效果。这是我的输出：

我究竟做错了什么？

用

ax.xaxis.tick_top()

将刻度线放在图像的顶部。命令

ax.set_xlabel('X LABEL')    
ax.xaxis.set_label_position('top') 

影响标签，而不影响刻度线。

import matplotlib.pyplot as plt
import numpy as np
column_labels = list('ABCD')
row_labels = list('WXYZ')
data = np.random.rand(4, 4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=plt.cm.Blues)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()

ax.set_xticklabels(column_labels, minor=False)
ax.set_yticklabels(row_labels, minor=False)
plt.show()

您想要set_ticks_position而不是set_label_position：

ax.xaxis.set_ticks_position('top') # the rest is the same

这给了我：

tick_params对于设置刻度属性非常有用。可以使用以下命令将标签移到顶部：

    ax.tick_params(labelbottom=False,labeltop=True)

如果要让刻度（而不是标签）显示在顶部和底部（而不仅仅是顶部），则必须做一些额外的按摩。我可以做到的唯一方法是对unutbu的代码进行较小的更改：

import matplotlib.pyplot as plt
import numpy as np
column_labels = list('ABCD')
row_labels = list('WXYZ')
data = np.random.rand(4, 4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=plt.cm.Blues)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()
ax.xaxis.set_ticks_position('both') # THIS IS THE ONLY CHANGE

ax.set_xticklabels(column_labels, minor=False)
ax.set_yticklabels(row_labels, minor=False)
plt.show()

输出：