要做到这一点，你需要一个histogram，这可以很容易地通过你的panda DataFrame的hist方法来实现（这个方法本身，只需要使用Matplotlib的hist绘图方法，它使用NumPy的histogram函数），例如：

import pandas as pd
from matplotlib import pyplot as plt

# create a version of the data for the example
data = pd.DataFrame({"DIAMETER": [3.039085, 2.763617, 2.052176, 9.498093, 2.680360]})

fig, ax = plt.subplots()  # create a figure to plot onto

bins = [2, 3, 4, 5, 6, 7, 8, 9, 10]  # set the histogram bin edges
data.hist(
    column="DIAMETER",  # the column to histogram
    bins=bins,  # set the bin edges
    density=True,   # this normalises the histogram  
    ax=ax,  # the Matplotlib axis onto which to plot
)

fig.show()  # show the plot

这给出：

其中Pandas hist函数将根据列名自动添加图标题。
如果您不指定bins关键字，那么它将自动生成10个以数据范围为边界的区间，但这些区间不一定是整数间隔的。如果您想确保任意数据都是整数间隔的区间，那么您可以使用以下命令设置区间：

import numpy as np

bins = np.arange(
    np.floor(data["DIAMETER"].min()),
    np.ceil(data["DIAMETER"].max()) + 1,
)

如果你想要一个像other post中建议的那样的图，那么纯粹使用NumPy和Matplotlib你可以：

import numpy as np
from matplotlib import pyplot as plt

# set bins
bins = np.arange(
    np.floor(data["DIAMETER"].min()) - 1,  # subtract 1, so we have a zero count bin at the start
    np.ceil(data["DIAMETER"].max()) + 2,  # add 2, so we have a zero count bin at the end
)

# generate histogram
counts, _ = np.histogram(
    data["DIAMETER"],
    bins=bins,
    density=True,  # normalise histogram
)

dx_2 = 0.5 * (bins[1] - bins[0])  # bin half spacing (we know this is 1/2, but lets calculate it in case you change it!)

# plot
fig, ax = plt.subplots()
ax.plot(
    bins[:-1] + dx_2,
    counts,
    marker="s",  # square marker like the plot from your other question
)

ax.set_xlabel("Diameter")
ax.set_ylabel("Probability density")

fig.show()

其给出：