如果x标签具有数字值，则熊猫处理条形图的x标记的方式可能会令人困惑.让我们举个例子:

The way that pandas handles x-ticks for bar plots can be quite confusing if your x-labels have numeric values. Let's take this example:

import pandas as pd
import numpy as np

x = np.linspace(0, 1, 21)
y = np.random.rand(21)
s = pd.Series(y, index=x)

ax = s.plot(kind='bar', figsize=(10, 3))
ax.figure.tight_layout()

您可能希望刻度位置直接与x中的值相对应，即0、0.05、0.1，...，1.0.但是，事实并非如此:

You might expect the tick locations to correspond directly to the values in x, i.e. 0, 0.05, 0.1, ..., 1.0. However, this isn't the case:

print(ax.get_xticks())
# [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20]

pandas会根据x中每个元素的 indices 设置刻度位置，然后根据x中的值:

Instead pandas sets the tick locations according to the indices of each element in x, but then sets the tick labels according to the values in x:

print(' '.join(label.get_text() for label in ax.get_xticklabels()))
# 0.0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1.0

因此，直接设置刻度位置(通过使用ax.set_xticks)或将xticks=参数传递给pd.Series.plot()都不会给您带来预期的效果:

Because of this, setting the tick positions directly (either by using ax.set_xticks) or passing the xticks= argument to pd.Series.plot() will not give you the effect you are expecting:

new_ticks = np.linspace(0, 1, 11)       # 0.0, 0.1, 0.2, ..., 1.0
ax.set_xticks(new_ticks)

相反，您需要分别更新x标记的位置和标签:

Instead you would need to update the positions and the labels of your x-ticks separately:

# positions of each tick, relative to the indices of the x-values
ax.set_xticks(np.interp(new_ticks, s.index, np.arange(s.size)))

# labels
ax.set_xticklabels(new_ticks)

在大多数情况下，这种行为实际上很有意义.对于条形图，x标签通常是非数字的(例如，与类别对应的字符串)，在这种情况下，将无法使用x中的值来设置刻度位置.在不引入其他参数来指定其位置的情况下，最合乎逻辑的选择是改为使用其索引.

This behavior actually makes a lot of sense in most cases. For bar plots it is common for the x-labels to be non-numeric (e.g. strings corresponding to categories), in which case it wouldn't be possible to use the values in x to set the tick locations. Without introducing another argument to specify their locations, the most logical choice would be to use their indices instead.