这里使用的是scatter,并显示annotation,,<><>hovering,上下页。

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()

Because people also want to use this solution for a line plot instead of a scatter, the following would be the same solution for plot (which works slightly differently).

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()

In case someone is looking for a solution for lines in twin axes, refer to How to make labels appear when hovering over a point in multiple axis?

如果有人正在寻找易货地的解决办法,请参见。

• Using the mplcursors package is probably the easiest option.
  • mplcursors: read the docs
  • mplcursors: github
  • If using Anaconda, install with these instructions, otherwise use these instructions for pip.
• This must be plotted in an interactive window, not inline.
  • For jupyter, executing something like %matplotlib qt in a cell will turn on interactive plotting. See How can I open the interactive matplotlib window in IPython notebook?
• Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2

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()

Pandas

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

Seaborn

• Works with axes-level plots like sns.lineplot, and figure-level plots like sns.relplot.

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()

http://matplotlib.sourceforge.net/examples/event_handling/pick_event_demo.html”rel=“nofollow noreferer” http://matplotlib.sourceforge.net/examples/event_handling/pick_event_demo.html:

from matplotlib.pyplot import figure, show
import numpy as npy
from numpy.random import rand


if 1: # picking on a scatter plot (matplotlib.collections.RegularPolyCollection)

    x, y, c, s = rand(4, 100)
    def onpick3(event):
        ind = event.ind
        print( onpick3 scatter: , ind, npy.take(x, ind), npy.take(y, ind))

    fig = figure()
    ax1 = fig.add_subplot(111)
    col = ax1.scatter(x, y, 100*s, c, picker=True)
    #fig.savefig( pscoll.eps )
    fig.canvas.mpl_connect( pick_event , onpick3)

show()

• This recipe draws an annotation on picking a data point: http://scipy-cookbook.readthedocs.io/items/Matplotlib_Interactive_Plotting.html .
• This recipe draws a tooltip, but it requires wxPython: Point and line tooltips in matplotlib?

其他答复没有满足我在最近版的Jupyter线校准数字中适当展示工具提示的需要。 这一工作是:

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()

Leading to something like the following picture when going over a point with mouse:

http://matplotlib.org/users/shell.html

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()

正如索伊卜要求的那样,这块地块是直线的。

供应商为我工作。 mplcursors为校正提供可点击的信号。 大量来自小型数据交换器() ,内容简化

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.
Right-click to deselect.
"
             "Annotations can be dragged.")

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

plt.show()

I have made a multi-line annotation system to add to: https://stackoverflow.com/a/47166787/10302020. for the most up to date version: https://github.com/AidenBurgess/MultiAnnotationLineGraph

简单地改动了底部的数据。

import matplotlib.pyplot as plt


def update_annot(ind, line, annot, ydata):
    x, y = line.get_data()
    annot.xy = (x[ind["ind"][0]], y[ind["ind"][0]])
    # Get x and y values, then format them to be displayed
    x_values = " ".join(list(map(str, ind["ind"])))
    y_values = " ".join(str(ydata[n]) for n in ind["ind"])
    text = "{}, {}".format(x_values, y_values)
    annot.set_text(text)
    annot.get_bbox_patch().set_alpha(0.4)


def hover(event, line_info):
    line, annot, ydata = line_info
    vis = annot.get_visible()
    if event.inaxes == ax:
        # Draw annotations if cursor in right position
        cont, ind = line.contains(event)
        if cont:
            update_annot(ind, line, annot, ydata)
            annot.set_visible(True)
            fig.canvas.draw_idle()
        else:
            # Don t draw annotations
            if vis:
                annot.set_visible(False)
                fig.canvas.draw_idle()


def plot_line(x, y):
    line, = plt.plot(x, y, marker="o")
    # Annotation style may be changed here
    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)
    line_info = [line, annot, y]
    fig.canvas.mpl_connect("motion_notify_event",
                           lambda event: hover(event, line_info))


# Your data values to plot
x1 = range(21)
y1 = range(0, 21)
x2 = range(21)
y2 = range(0, 42, 2)
# Plot line graphs
fig, ax = plt.subplots()
plot_line(x1, y1)
plot_line(x2, y2)
plt.show()

showing object information in matplotlib statusbar

### imports
import matplotlib as mpl
import matplotlib.pylab as plt
import numpy as np


# https://stackoverflow.com/a/47166787/7128154
# https://matplotlib.org/3.3.3/api/collections_api.html#matplotlib.collections.PathCollection
# https://matplotlib.org/3.3.3/api/path_api.html#matplotlib.path.Path
# https://stackoverflow.com/questions/15876011/add-information-to-matplotlib-navigation-toolbar-status-bar
# https://stackoverflow.com/questions/36730261/matplotlib-path-contains-point
# https://stackoverflow.com/a/36335048/7128154
class StatusbarHoverManager:
    """
    Manage hover information for mpl.axes.Axes object based on appearing
    artists.

    Attributes
    ----------
    ax : mpl.axes.Axes
        subplot to show status information
    artists : list of mpl.artist.Artist
        elements on the subplot, which react to mouse over
    labels : list (list of strings) or strings
        each element on the top level corresponds to an artist.
        if the artist has items
        (i.e. second return value of contains() has key  ind ),
        the element has to be of type list.
        otherwise the element if of type string
    cid : to reconnect motion_notify_event
    """
    def __init__(self, ax):
        assert isinstance(ax, mpl.axes.Axes)


        def hover(event):
            if event.inaxes != ax:
                return
            info =  x={:.2f}, y={:.2f} .format(event.xdata, event.ydata)
            ax.format_coord = lambda x, y: info
        cid = ax.figure.canvas.mpl_connect("motion_notify_event", hover)

        self.ax = ax
        self.cid = cid
        self.artists = []
        self.labels = []

    def add_artist_labels(self, artist, label):
        if isinstance(artist, list):
            assert len(artist) == 1
            artist = artist[0]

        self.artists += [artist]
        self.labels += [label]

        def hover(event):
            if event.inaxes != self.ax:
                return
            info =  x={:.2f}, y={:.2f} .format(event.xdata, event.ydata)
            for aa, artist in enumerate(self.artists):
                cont, dct = artist.contains(event)
                if not cont:
                    continue
                inds = dct.get( ind )
                if inds is not None:  # artist contains items
                    for ii in inds:
                        lbl = self.labels[aa][ii]
                        info +=  ;   artist [{:d}, {:d}]: {:} .format(
                            aa, ii, lbl)
                else:
                    lbl = self.labels[aa]
                    info +=  ;   artist [{:d}]: {:} .format(aa, lbl)
            self.ax.format_coord = lambda x, y: info

        self.ax.figure.canvas.mpl_disconnect(self.cid)
        self.cid = self.ax.figure.canvas.mpl_connect(
            "motion_notify_event", hover)



def demo_StatusbarHoverManager():
    fig, ax = plt.subplots()
    shm = StatusbarHoverManager(ax)

    poly = mpl.patches.Polygon(
        [[0,0], [3, 5], [5, 4], [6,1]], closed=True, color= green , zorder=0)
    artist = ax.add_patch(poly)
    shm.add_artist_labels(artist,  polygon )

    artist = ax.scatter([2.5, 1, 2, 3], [6, 1, 1, 7], c= blue , s=10**2)
    lbls = [ point   + str(ii) for ii in range(4)]
    shm.add_artist_labels(artist, lbls)

    artist = ax.plot(
        [0, 0, 1, 5, 3], [0, 1, 1, 0, 2], marker= o , color= red )
    lbls = [ segment   + str(ii) for ii in range(5)]
    shm.add_artist_labels(artist, lbls)

    plt.show()


# --- main
if __name__== "__main__":
    demo_StatusbarHoverManager()

import matplotlib.pylab as plt
import numpy as np

plt.close( all )
fh, ax = plt.subplots()

#Generate some data
y,x = np.histogram(np.random.randn(10000), bins=500)
x = x[:-1]
colors = [ #0000ff ,  #00ff00 , #ff0000 ]
x2, y2 = x,y/10
x3, y3 = x, np.random.randn(500)*10+40

#Plot
h1 = ax.plot(x, y, color=colors[0])
h2 = ax.plot(x2, y2, color=colors[1])
h3 = ax.scatter(x3, y3, color=colors[2], s=1)

artists = h1 + h2 + [h3] #concatenating lists
labels = [list( ABCDE *100),list( FGHIJ *100),list( klmno *100)] #define labels shown

#___ Initialize annotation arrow
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 on_plot_hover(event):
    if event.inaxes != ax: #exit if mouse is not on figure
        return
    is_vis = annot.get_visible() #check if an annotation is visible
    # x,y = event.xdata,event.ydata #coordinates of mouse in graph
    for ii, artist in enumerate(artists):
        is_contained, dct = artist.contains(event)

        if(is_contained):
            if( get_data  in dir(artist)): #for plot
                data = list(zip(*artist.get_data()))
            elif( get_offsets  in dir(artist)): #for scatter
                data = artist.get_offsets().data

            inds = dct[ ind ] #get which data-index is under the mouse
            #___ Set Annotation settings
            xy = data[inds[0]] #get 1st position only
            annot.xy = xy
            annot.set_text(f pos={xy},text={labels[ii][inds[0]]} )
            annot.get_bbox_patch().set_edgecolor(colors[ii])
            annot.get_bbox_patch().set_alpha(0.7)
            annot.set_visible(True)
            fh.canvas.draw_idle()
        else:
             if is_vis:
                 annot.set_visible(False) #disable when not hovering
                 fh.canvas.draw_idle()

fh.canvas.mpl_connect( motion_notify_event , on_plot_hover)

Giving the following result:

我已经调整了进口税的答案,以便与配送和班级合作。 特征:

• The entire framework is contained inside of a single class, so all of the used variables are only available within their relevant scopes.
• Can create multiple distinct sets of patches
• Hovering over a patch prints patch collection name and patch subname
• Hovering over a patch highlights all patches of that collection by changing their edge color to black

注:对我的申请而言,重叠无关紧要,因此当时只显示一个物体的名称。 如果你愿意,可以自由延伸到多个物体,这并非太困难。

<>Usage

fig, ax = plt.subplots(tight_layout=True)

ap = annotated_patches(fig, ax)
ap.add_patches( Azure ,  circle ,  blue , np.random.uniform(0, 1, (4,2)),  ABCD , 0.1)
ap.add_patches( Lava ,  rect ,  red , np.random.uniform(0, 1, (3,2)),  EFG , 0.1, 0.05)
ap.add_patches( Emerald ,  rect ,  green , np.random.uniform(0, 1, (3,2)),  HIJ , 0.05, 0.1)

plt.axis( equal )
plt.axis( off )

plt.show()

<>执行>

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection

np.random.seed(1)


class annotated_patches:
    def __init__(self, fig, ax):
        self.fig = fig
        self.ax = ax

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

        self.motionCallbackID = self.fig.canvas.mpl_connect("motion_notify_event", self.hover)

    def add_patches(self, groupName, kind, color, xyCoords, names, *params):
        if kind== circle :
            circles = [mpatches.Circle(xy, *params, ec="none") for xy in xyCoords]
            thisCollection = PatchCollection(circles, facecolor=color, alpha=0.5, edgecolor=None)
            ax.add_collection(thisCollection)
        elif kind ==  rect :
            rectangles = [mpatches.Rectangle(xy, *params, ec="none") for xy in xyCoords] 
            thisCollection = PatchCollection(rectangles, facecolor=color, alpha=0.5, edgecolor=None)
            ax.add_collection(thisCollection)
        else:
            raise ValueError( Unexpected kind , kind)
            
        self.collectionsDict[groupName] = thisCollection
        self.coordsDict[groupName] = xyCoords
        self.namesDict[groupName] = names
        self.isActiveDict[groupName] = False
        
    def update_annot(self, groupName, patchIdxs):
        self.annot.xy = self.coordsDict[groupName][patchIdxs[0]]
        self.annot.set_text(groupName +  :   + self.namesDict[groupName][patchIdxs[0]])
        
        # Set edge color
        self.collectionsDict[groupName].set_edgecolor( black )
        self.isActiveDict[groupName] = True

    def hover(self, event):
        vis = self.annot.get_visible()
        updatedAny = False
        if event.inaxes == self.ax:            
            for groupName, collection in self.collectionsDict.items():
                cont, ind = collection.contains(event)
                if cont:
                    self.update_annot(groupName, ind["ind"])
                    self.annot.set_visible(True)
                    self.fig.canvas.draw_idle()
                    updatedAny = True
                else:
                    if self.isActiveDict[groupName]:
                        collection.set_edgecolor(None)
                        self.isActiveDict[groupName] = True
                    
            if (not updatedAny) and vis:
                self.annot.set_visible(False)
                self.fig.canvas.draw_idle()