如果您想要直方图，则无需在x值上附加任何名称"，因为在x轴上您将具有数据仓:

If you want a histogram, you don't need to attach any 'names' to x-values, as on x-axis you would have data bins:

import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(42)
x = np.random.normal(size=1000)
plt.hist(x, density=True, bins=30)  # `density=False` would make counts
plt.ylabel('Probability')
plt.xlabel('Data');

您可以使用PDF线，标题和图例使直方图更加有趣:

You can make your histogram a bit fancier with PDF line, titles, and legend:

import scipy.stats as st
plt.hist(x, density=True, bins=30, label="Data")
mn, mx = plt.xlim()
plt.xlim(mn, mx)
kde_xs = np.linspace(mn, mx, 301)
kde = st.gaussian_kde(x)
plt.plot(kde_xs, kde.pdf(kde_xs), label="PDF")
plt.legend(loc="upper left")
plt.ylabel('Probability')
plt.xlabel('Data')
plt.title("Histogram");

但是，如果您的数据点数量有限(例如在OP中)，则条形图可以更好地表示您的数据(然后您可以在x轴上附加标签):

However, if you have limited number of data points, like in OP, a bar plot would make more sense to represent your data (then you may attach labels to x-axis):

x = np.arange(3)
plt.bar(x, height=[1,2,3])
plt.xticks(x, ['a','b','c'])