Mplcursors对我很有用。Mplcursors为matplotlib提供了可单击的注释。它很大程度上受到mpldatacursor (https://github.com/joferkington/mpldatacursor)的启发，具有非常简化的API

import matplotlib.pyplot as plt
import numpy as np
import mplcursors

data = np.outer(range(10), range(1, 5))

fig, ax = plt.subplots()
lines = ax.plot(data)
ax.set_title("Click somewhere on a line.\nRight-click to deselect.\n"
             "Annotations can be dragged.")

mplcursors.cursor(lines) # or just mplcursors.cursor()

plt.show()

似乎这里的其他答案都不能回答这个问题。这是一个代码，它使用散点并在悬停在散点上时显示注释。

import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)

x = np.random.rand(15)
y = np.random.rand(15)
names = np.array(list("ABCDEFGHIJKLMNO"))
c = np.random.randint(1,5,size=15)

norm = plt.Normalize(1,4)
cmap = plt.cm.RdYlGn

fig,ax = plt.subplots()
sc = plt.scatter(x,y,c=c, s=100, cmap=cmap, norm=norm)

annot = ax.annotate("", xy=(0,0), xytext=(20,20),textcoords="offset points",
                    bbox=dict(boxstyle="round", fc="w"),
                    arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)

def update_annot(ind):

    pos = sc.get_offsets()[ind["ind"][0]]
    annot.xy = pos
    text = "{}, {}".format(" ".join(list(map(str,ind["ind"]))), 
                           " ".join([names[n] for n in ind["ind"]]))
    annot.set_text(text)
    annot.get_bbox_patch().set_facecolor(cmap(norm(c[ind["ind"][0]])))
    annot.get_bbox_patch().set_alpha(0.4)


def hover(event):
    vis = annot.get_visible()
    if event.inaxes == ax:
        cont, ind = sc.contains(event)
        if cont:
            update_annot(ind)
            annot.set_visible(True)
            fig.canvas.draw_idle()
        else:
            if vis:
                annot.set_visible(False)
                fig.canvas.draw_idle()

fig.canvas.mpl_connect("motion_notify_event", hover)

plt.show()

因为人们还想将这个解决方案用于线状图而不是散点图，所以下面的解决方案将用于图形(其工作方式略有不同)。

import matplotlib.pyplot as plt import numpy as np; np.random.seed(1) x = np.sort(np.random.rand(15)) y = np.sort(np.random.rand(15)) names = np.array(list("ABCDEFGHIJKLMNO")) norm = plt.Normalize(1,4) cmap = plt.cm.RdYlGn fig,ax = plt.subplots() line, = plt.plot(x,y, marker="o") annot = ax.annotate("", xy=(0,0), xytext=(-20,20),textcoords="offset points", bbox=dict(boxstyle="round", fc="w"), arrowprops=dict(arrowstyle="->")) annot.set_visible(False) def update_annot(ind): x,y = line.get_data() annot.xy = (x[ind["ind"][0]], y[ind["ind"][0]]) text = "{}, {}".format(" ".join(list(map(str,ind["ind"]))), " ".join([names[n] for n in ind["ind"]])) annot.set_text(text) annot.get_bbox_patch().set_alpha(0.4) def hover(event): vis = annot.get_visible() if event.inaxes == ax: cont, ind = line.contains(event) if cont: update_annot(ind) annot.set_visible(True) fig.canvas.draw_idle() else: if vis: annot.set_visible(False) fig.canvas.draw_idle() fig.canvas.mpl_connect("motion_notify_event", hover) plt.show()

如果有人正在寻找双轴线的解决方案，请参阅如何使标签出现时悬停在多个轴上的点?

如果有人正在寻找条形图的解决方案，请参考例如这个答案。

Mpld3为我解决它。 编辑(新增代码):

import matplotlib.pyplot as plt
import numpy as np
import mpld3

fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
N = 100

scatter = ax.scatter(np.random.normal(size=N),
                 np.random.normal(size=N),
                 c=np.random.random(size=N),
                 s=1000 * np.random.random(size=N),
                 alpha=0.3,
                 cmap=plt.cm.jet)
ax.grid(color='white', linestyle='solid')

ax.set_title("Scatter Plot (with tooltips!)", size=20)

labels = ['point {0}'.format(i + 1) for i in range(N)]
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)
mpld3.plugins.connect(fig, tooltip)

mpld3.show()

你可以检查这个例子

其他答案没有解决我在最新版本的Jupyter内联matplotlib图中正确显示工具提示的需求。这条是可行的:

import matplotlib.pyplot as plt
import numpy as np
import mplcursors
np.random.seed(42)

fig, ax = plt.subplots()
ax.scatter(*np.random.random((2, 26)))
ax.set_title("Mouse over a point")
crs = mplcursors.cursor(ax,hover=True)

crs.connect("add", lambda sel: sel.annotation.set_text(
    'Point {},{}'.format(sel.target[0], sel.target[1])))
plt.show()

当用鼠标浏览一个点时，会导致如下图所示:

对http://matplotlib.org/users/shell.html:中提供的示例进行了轻微编辑

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title('click on points')

line, = ax.plot(np.random.rand(100), '-', picker=5)  # 5 points tolerance


def onpick(event):
    thisline = event.artist
    xdata = thisline.get_xdata()
    ydata = thisline.get_ydata()
    ind = event.ind
    print('onpick points:', *zip(xdata[ind], ydata[ind]))


fig.canvas.mpl_connect('pick_event', onpick)

plt.show()

就像Sohaib问的，这是一条直线

最简单的选择是使用mplcursors包。 Mplcursors:读取文档 mplcursors: github 如果使用Anaconda，请按照这些说明安装，否则使用这些说明安装pip。 这必须在交互式窗口中绘制，而不是内联。 对于jupyter，在单元格中执行%matplotlib qt之类的代码将启用交互式绘图。参见如何在IPython笔记本中打开交互式matplotlib窗口? 在python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2中测试

import matplotlib.pyplot as plt
import pandas_datareader as web  # only for test data; must be installed with conda or pip
from mplcursors import cursor  # separate package must be installed

# reproducible sample data as a pandas dataframe
df = web.DataReader('aapl', data_source='yahoo', start='2021-03-09', end='2022-06-13')

plt.figure(figsize=(12, 7))
plt.plot(df.index, df.Close)
cursor(hover=True)
plt.show()

熊猫

ax = df.plot(y='Close', figsize=(10, 7))
cursor(hover=True)
plt.show()

Seaborn

工作与轴级别的情节，如sns。Lineplot和像sns.relplot这样的数字级plot。

import seaborn as sns

# load sample data
tips = sns.load_dataset('tips')

sns.relplot(data=tips, x="total_bill", y="tip", hue="day", col="time")
cursor(hover=True)
plt.show()