如果你想要一个直方图，你不需要为 x 值附加任何名称"，因为在 x 轴上你会有数据箱:

将 matplotlib.pyplot 导入为 plt将 numpy 导入为 np%matplotlib 内联np.random.seed(42)x = np.random.normal(大小=1000)plt.hist(x, density=True, bins=30) # density=False 进行计数plt.ylabel('概率')plt.xlabel('数据');

注意，bins=30的数量是任意选择的，还有 ，其中 IQR 是

最后，您可以使用 PDF 行、标题和图例使您的直方图更漂亮:

import scipy.stats as stplt.hist(x，密度=真，bins=82，标签=数据")mn, mx = plt.xlim()plt.xlim(mn, mx)kde_xs = np.linspace(mn, mx, 300)kde = st.gaussian_kde(x)plt.plot(kde_xs, kde.pdf(kde_xs), label="PDF")plt.legend(loc=左上")plt.ylabel(概率")plt.xlabel(数据")plt.title(直方图");

如果您愿意探索其他机会，seaborn 有一个捷径:

# !pip install seaborn将 seaborn 作为 sns 导入sns.displot(x, bins=82, kde=True);

现在回到 OP.

如果您的数据点数量有限，则使用条形图来表示您的数据会更有意义.然后您可以将标签附加到 x 轴:

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

I am trying to plot a histogram using the matplotlib.hist() function but I am not sure how to do it.

I have a list

probability = [0.3602150537634409, 0.42028985507246375, 
  0.373117033603708, 0.36813186813186816, 0.32517482517482516, 
  0.4175257731958763, 0.41025641025641024, 0.39408866995073893, 
  0.4143222506393862, 0.34, 0.391025641025641, 0.3130841121495327, 
  0.35398230088495575]

and a list of names(strings).

How do I make the probability as my y-value of each bar and names as x-values?

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');

Note, the number of bins=30 was chosen arbitrarily, and there is Freedman–Diaconis rule to be more scientific in choosing the "right" bin width:

, where IQR is Interquartile range and n is total number of datapoints to plot

So, according to this rule one may calculate number of bins as:

q25, q75 = np.percentile(x, [25, 75])
bin_width = 2 * (q75 - q25) * len(x) ** (-1/3)
bins = round((x.max() - x.min()) / bin_width)
print("Freedman–Diaconis number of bins:", bins)
plt.hist(x, bins=bins);

---

Freedman–Diaconis number of bins: 82

And finally 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=82, label="Data")
mn, mx = plt.xlim()
plt.xlim(mn, mx)
kde_xs = np.linspace(mn, mx, 300)
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");

If you're willing to explore other opportunities, there is a shortcut with seaborn:

# !pip install seaborn
import seaborn as sns
sns.displot(x, bins=82, kde=True);

Now back to the OP.

If you have limited number of data points, 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']);