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Matplotlib change colour of every second element in yaxis

So I have this plot here:
What I want to do is to have every second element of yaxis to be coloured for example in blue and the rest in red.
Here is the result I want to get:
and here is the code I got:
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
mpl.rcParams['toolbar'] = 'None'
plt.style.use('fivethirtyeight')
result_7_s = amount * s_7_days
result_14_s = amount * s_14_days
result_21_s = amount * s_21_days
result_7_fc = amount * fc_7_days
result_14_fc = amount * fc_14_days
result_21_fc = amount * fc_21_days
final_y = np.array([int(result_7_s), int(result_14_s),
int(result_21_s), int(result_7_fc),
int(result_14_fc), int(result_21_fc)])
fig, ax = plt.subplots(num = 'Test')
x = np.array([7, 14, 21])
plt.xticks(ticks = x, labels = x)
plt.yticks(ticks = final_y, labels = final_y)
plt.title(f'Prices for {amount} people')
plt.xlabel('Days')
plt.ylabel('Price')
plt.tight_layout()
ax.bar(x - 0.5, final_y[:3], width=1, color='#444444', label='Standard')
ax.bar(x + 0.5, final_y[3:], width=1, color='#e5ae38', label='First Class')
ax.tick_params(axis='y', colors = 'blue') # <-------
ax.yaxis.set_major_formatter('{x}$')
plt.legend()
plt.savefig('result.png')
plt.show()

Iterate over the tick labels to apply the desired color to each one of them:
for n, tick_label in enumerate(ax.yaxis.get_ticklabels()):
tick_label.set_color("red" if n%2 else "blue")

Here is the solution I came with:
for i in range(0, 3):
plt.gca().get_yticklabels()[i].set_color('blue')
for i in range(3, 6):
plt.gca().get_yticklabels()[i].set_color('red')

How to change background color of inset figure

I'm trying to create an inset figure that has a different projection from the parent. The only issue I have at this point is the inset figures's tick labels are not legible because they are black and blend in with the plot behind it. I could change the color of the ticks and labels to white, but that does not help when the data in ax0 yields lighter colors. Here is the MWE:
import calipsoFunctions as cf
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import numpy as np
import pylab as pl
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from mpl_toolkits.axes_grid1.inset_locator import inset_axes, mark_inset, InsetPosition
x, y = np.arange(100), np.arange(200)
X, Y = np.meshgrid(x, y)
C = np.random.randint(0, 100, (200, 100))
fig = pl.figure(figsize=(6.5, 5.25))
gs0 = pl.GridSpec(3, 1)
gs0.update(left=0.08, right=0.925,
top=0.95, bottom=0.33,
hspace=0.10, wspace=0.0)
gs1 = pl.GridSpec(1, 2)
gs1.update(left=0.08, right=0.925,
top=0.225, bottom=0.05,
hspace=0.0, wspace=0.025)
# create primary axes
ax0 = pl.subplot(gs0[0])
ax1 = pl.subplot(gs0[1])
ax0.pcolormesh(X, Y, C, vmin=0, vmax=75)
ax1.pcolormesh(X, Y, C, vmin=0, vmax=75)
# add map plot (inset axis)
loc_box = [0.8, 0.55, 0.20, 0.45]
ax0_inset = fig.add_axes(loc_box,
projection=ccrs.PlateCarree(),
aspect="auto",
facecolor="w",
frameon=True)
lat_array = np.arange(-20, 20)
lon_array = np.arange(-10, 10, 0.5)
ax0_inset.plot(lat_array, lon_array, "k-", lw=1)
ip = InsetPosition(ax0, loc_box)
ax0_inset.set_axes_locator(ip)
ax0_inset.coastlines(resolution="10m", linewidth=0.25, color="k")
ax0_inset.add_feature(cfeature.LAND)
llat, ulat = lat_array.min(), lat_array.max()
llon, ulon = lon_array.min(), lon_array.max()
llat = np.round(llat / 10) * 10
ulat = np.round(ulat / 10) * 10
llon = np.round(llon / 5) * 5
ulon = np.round(ulon / 5) * 5
ax0_inset.set_yticks(np.arange(llat, ulat, 20), minor=False)
ax0_inset.set_yticks(np.arange(llat, ulat, 10), minor=True)
ax0_inset.set_yticklabels(np.arange(llat, ulat, 20),
fontsize=8)
ax0_inset.yaxis.set_major_formatter(LatitudeFormatter())
ax0_inset.set_xticks(np.arange(llon, ulon, 5), minor=False)
ax0_inset.set_xticks(np.arange(llon, ulon, 1), minor=True)
ax0_inset.set_xticklabels(np.arange(llon, ulon, 5),
fontsize=8,
rotation=45)
ax0_inset.xaxis.set_major_formatter(LongitudeFormatter())
ax0_inset.grid()
ax0_inset.tick_params(which="both",
axis="both",
direction="in",
labelsize=8)
fig.show()
Is there a way to change the background color of ax0_inset so that these tick labels are legible? I tried changing the face_color to "w", but that did not work. Ideally, I want the same behavior as ax0.figure.set_facecolor("w"), but for the ax0_inset axis. Is this doable?

Following #Mr. T's comment suggestion, a work-around solution could be:
# insert transparent (or opaque) rectangle around inset_axes plot
# to make axes labels more visible
# make buffer variable to control amount of buffer around inset_axes
buffer = 0.1 # fractional axes coordinates
# use ax inset tuple coords in loc_box to add rectangle patch
# [left, bottom, width, height] (fractional axes coordinates)
fig.add_patch(plt.Rectangle((
loc_box[0]-buffer, loc_box[1]-buffer),
loc_box[2]+buffer, loc_box[3]+buffer,
linestyle="-", edgecolor="k", facecolor="w",
linewidth=1, alpha=0.75, zorder=5,
transform=ax0.transAxes))

ax.annotate text partially appearing outside the figure box

Apologies, rather unskilled with programming and stackoverflow too. I am drawing bar plots on some data and have managed to add percentages beside the bars, using ax.annotate. However for the bar with highest responses I always get part of the percentage number outside the figure box, as per image below. Have tried different ideas but none worked to fix this. Looking for some suggestions on how to fix this.
Here is my code
from matplotlib import pyplot as plt
import seaborn as sns
def plot_barplot(df):
plt.rcParams.update({'font.size': 18})
sns.set(font_scale=2)
if (len(df) > 1):
fig = plt.figure(figsize=(12,10))
ax = sns.barplot(x='count', y=df.columns[0], data=df, color='blue')
else:
fig = plt.figure(figsize=(5,7))
ax = sns.barplot(x=df.columns[0], y='count', data=df, color='blue')
fig.set_tight_layout(True)
plt.rcParams.update({'font.size': 14})
total = df['count'].sum()
for p in ax.patches:
percentage ='{:.2f}%'.format(100 * p.get_width()/total)
print(percentage)
x = p.get_x() + p.get_width() + 0.02
y = p.get_y() + p.get_height()/2
ax.annotate(percentage, (x, y))
Dataframe looks like this

I would suggest you increase the axes' margins (in the x direction in that case). That is the space there is between the maximum of your data and the maximum scale on the axis. You will have to play around with the value depending on your needs, but it looks like a value of 0.1 or 0.2 should be enough.
add:
plt.rcParams.update({'axes.xmargin': 0.2})
to the top of your function
full code:
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
def plot_barplot(df):
plt.rcParams.update({'font.size': 18})
plt.rcParams.update({'axes.xmargin': 0.1})
sns.set(font_scale=2)
if (len(df) > 1):
fig = plt.figure(figsize=(12, 10))
ax = sns.barplot(x='count', y=df.columns[0], data=df, color='blue')
else:
fig = plt.figure(figsize=(5, 7))
ax = sns.barplot(x=df.columns[0], y='count', data=df, color='blue')
fig.set_tight_layout(True)
plt.rcParams.update({'font.size': 14})
total = df['count'].sum()
for p in ax.patches:
percentage = '{:.2f}%'.format(100 * p.get_width() / total)
print(percentage)
x = p.get_x() + p.get_width() + 0.02
y = p.get_y() + p.get_height() / 2
ax.annotate(percentage, (x, y))
df = pd.DataFrame({'question': ['Agree', 'Strongly agree'], 'count': [200, 400]})
plot_barplot(df)
plt.show()

Matplotlib variable issues

I am almost brand-new to Python and matplotlib, and so have been working on adapting an example from the Python documentation for a graph that I need to complete. However, I get undefined name errors for the rect1 and rect2 calls and for ax in the ax.text. I have a feeling that it has something to do with the values not transferring across function definitions, but I can't figure out the proper syntax. Any ideas?
P.S. I can supply additional information if necessary; this is my first post of this sort.
from inventoryClass import stockItem
import numpy as np
import matplotlib.pyplot as plt
def plotInventory(itemRecords):
stockBegin = []
stockFinish = []
stockID = []
stockItems = []
for rec in itemRecords.values() :
stockBegin.append(rec.getStockStart)
stockFinish.append(rec.getStockOnHand)
stockID.append(rec.getID)
stockItems.append(rec.getName)
N = len(stockBegin)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(ind, stockBegin, width, color='r')
rects2 = ax.bar(ind + width, stockFinish, width, color='y')
# add some text for labels, title and axes ticks
ax.set_ylabel('Inventory')
ax.set_title('Stock start and end inventory, by item')
ax.set_xticks(ind + width)
ax.set_xticklabels((str(stockID[0]), str(stockID[1]), str(stockID[1])))
ax.legend((rects1[0], rects2[0]), ('Start', 'End'))
def autolabel(rects) :
for rect in rects :
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,
'%d' % int(height),
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
plt.show()

The variables rects1 and rects2 only exist in the scope of plotInventory and so python doesn't know what you're referring to by 'rects1'. There are two possible ways to fix this:
You can return the values so they are available in the global scope:
def plotInventory(itemRecords):
# ... code ... #
return rects1, rects2
rects1, rects2 = plotInventory(records)
autolabel(rects1)
autolabel(rects2)
plt.show()
You can just call autolabel from inside plotInventory:
def plotInventory(itemRecords):
# ... code ... #
autolabel(rects1)
autolabel(rects2)
As for ax, you have the same problem and the same solutions, except that you need to pass ax into autolabel, eg:
def autolabel(ax, rects):
# ... code ... #
ax, rects1, rects2 = plotInventory(records)
autolabel(ax, rects1)
autolabel(ax, rects2)
Remember to return ax from plotInventory as well!

How to add hovering annotations to a plot

I am using matplotlib to make scatter plots. Each point on the scatter plot is associated with a named object. I would like to be able to see the name of an object when I hover my cursor over the point on the scatter plot associated with that object. In particular, it would be nice to be able to quickly see the names of the points that are outliers. The closest thing I have been able to find while searching here is the annotate command, but that appears to create a fixed label on the plot. Unfortunately, with the number of points that I have, the scatter plot would be unreadable if I labeled each point. Does anyone know of a way to create labels that only appear when the cursor hovers in the vicinity of that point?

It seems none of the other answers here actually answer the question. So here is a code that uses a scatter and shows an annotation upon hovering over the scatter points.
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?
In case someone is looking for a solution for bar plots, please refer to e.g. this answer.

This solution works when hovering a line without the need to click it:
import matplotlib.pyplot as plt
# Need to create as global variable so our callback(on_plot_hover) can access
fig = plt.figure()
plot = fig.add_subplot(111)
# create some curves
for i in range(4):
# Giving unique ids to each data member
plot.plot(
[i*1,i*2,i*3,i*4],
gid=i)
def on_plot_hover(event):
# Iterating over each data member plotted
for curve in plot.get_lines():
# Searching which data member corresponds to current mouse position
if curve.contains(event)[0]:
print("over %s" % curve.get_gid())
fig.canvas.mpl_connect('motion_notify_event', on_plot_hover)
plt.show()

From 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?

The easiest option is to use the mplcursors package.
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()

The other answers did not address my need for properly showing tooltips in a recent version of Jupyter inline matplotlib figure. This one works though:
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:

A slight edit on an example provided in 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()
This plots a straight line plot, as Sohaib was asking

mpld3 solve it for me.
EDIT (CODE ADDED):
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()
You can check this example

mplcursors worked for me. mplcursors provides clickable annotation for matplotlib. It is heavily inspired from mpldatacursor (https://github.com/joferkington/mpldatacursor), with a much simplified 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()

showing object information in matplotlib statusbar
Features
no extra libraries needed
clean plot
no overlap of labels and artists
supports multi artist labeling
can handle artists from different plotting calls (like scatter, plot, add_patch)
code in library style
Code
### 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()

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
Simply change the data in the bottom section.
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()

Based off Markus Dutschke" and "ImportanceOfBeingErnest", I (imo) simplified the code and made it more modular.
Also this doesn't require additional packages to be installed.
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:

Maybe this helps anybody, but I have adapted the #ImportanceOfBeingErnest's answer to work with patches and classes. Features:
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
Note: For my applications, the overlap is not relevant, thus only one object's name is displayed at a time. Feel free to extend to multiple objects if you wish, it is not too hard.
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()
Implementation
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()