I am trying to make a scatter plot and annotate data points with different numbers from a list.
So, for example, I want to plot y vs x and annotate with corresponding numbers from n.
y = [2.56422, 3.77284, 3.52623, 3.51468, 3.02199]
x = [0.15, 0.3, 0.45, 0.6, 0.75]
n = [58, 651, 393, 203, 123]
ax = fig.add_subplot(111)
ax1.scatter(z, y, fmt='o')
Any ideas?
I'm not aware of any plotting method which takes arrays or lists but you could use annotate() while iterating over the values in n.
import matplotlib.pyplot as plt
x = [0.15, 0.3, 0.45, 0.6, 0.75]
y = [2.56422, 3.77284, 3.52623, 3.51468, 3.02199]
n = [58, 651, 393, 203, 123]
fig, ax = plt.subplots()
ax.scatter(x, y)
for i, txt in enumerate(n):
ax.annotate(txt, (x[i], y[i]))
There are a lot of formatting options for annotate(), see the matplotlib website:
regplots without too much disruption, too.
KeyError- so I guess a dict() object is expected? Is there any other way to label the data using enumerate, annotate and a pandas data frame?
xytext=(x,y) keyword that allows specifying the location of the text label. The default is the same as the point xy=(x,y). For example: ax.annotate(txt, xy=(z[i], y[i]), xytext=(z[i]+0.1, y[i]+0.1)) That will also allow drawing lines arrows between the two locations. More info at: matplotlib.org/3.5.0/tutorials/text/annotations.htmlIn case anyone is trying to apply the above solutions to a .scatter() instead of a .subplot(),
I tried running the following code
import matplotlib.pyplot as plt
x = [0.15, 0.3, 0.45, 0.6, 0.75]
y = [2.56422, 3.77284, 3.52623, 3.51468, 3.02199]
n = [58, 651, 393, 203, 123]
fig, ax = plt.scatter(x, y)
for i, txt in enumerate(n):
ax.annotate(txt, (x[i], y[i]))
But ran into errors stating "cannot unpack non-iterable PathCollection object", with the error specifically pointing at codeline fig, ax = plt.scatter(x, y)
I eventually solved the error using the following code
import matplotlib.pyplot as plt
plt.scatter(x, y)
for i, txt in enumerate(n):
plt.annotate(txt, (x[i], y[i]))
I didn't expect there to be a difference between .scatter() and .subplot()
I should have known better.
plt.scatter is not meant to create a Figure and an Axes like plt.subplots() does, but a PathCollection containing the scatter points. You are supposed to create the figure and axes beforehand.In versions earlier than matplotlib 2.0, ax.scatter is not necessary to plot text without markers. In version 2.0 you'll need ax.scatter to set the proper range and markers for text.
import matplotlib.pyplot as plt
y = [2.56422, 3.77284, 3.52623, 3.51468, 3.02199]
z = [0.15, 0.3, 0.45, 0.6, 0.75]
n = [58, 651, 393, 203, 123]
fig, ax = plt.subplots()
for i, txt in enumerate(n):
ax.annotate(txt, (z[i], y[i]))
And in this link you can find an example in 3d.
plt.figure(figsize=(20,10)) aren't working as expected, in that that invoking this code doesn't actually change the size of the image. Looking forward to your assistance. Thanks!
You may also use pyplot.text (see here).
def plot_embeddings(M_reduced, word2Ind, words):
"""
Plot in a scatterplot the embeddings of the words specified in the list "words".
Include a label next to each point.
"""
for word in words:
x, y = M_reduced[word2Ind[word]]
plt.scatter(x, y, marker='x', color='red')
plt.text(x+.03, y+.03, word, fontsize=9)
plt.show()
M_reduced_plot_test = np.array([[1, 1], [-1, -1], [1, -1], [-1, 1], [0, 0]])
word2Ind_plot_test = {'test1': 0, 'test2': 1, 'test3': 2, 'test4': 3, 'test5': 4}
words = ['test1', 'test2', 'test3', 'test4', 'test5']
plot_embeddings(M_reduced_plot_test, word2Ind_plot_test, words)
I would love to add that you can even use arrows /text boxes to annotate the labels. Here is what I mean:
import random
import matplotlib.pyplot as plt
y = [2.56422, 3.77284, 3.52623, 3.51468, 3.02199]
z = [0.15, 0.3, 0.45, 0.6, 0.75]
n = [58, 651, 393, 203, 123]
fig, ax = plt.subplots()
ax.scatter(z, y)
ax.annotate(n[0], (z[0], y[0]), xytext=(z[0]+0.05, y[0]+0.3),
arrowprops=dict(facecolor='red', shrink=0.05))
ax.annotate(n[1], (z[1], y[1]), xytext=(z[1]-0.05, y[1]-0.3),
arrowprops = dict( arrowstyle="->",
connectionstyle="angle3,angleA=0,angleB=-90"))
ax.annotate(n[2], (z[2], y[2]), xytext=(z[2]-0.05, y[2]-0.3),
arrowprops = dict(arrowstyle="wedge,tail_width=0.5", alpha=0.1))
ax.annotate(n[3], (z[3], y[3]), xytext=(z[3]+0.05, y[3]-0.2),
arrowprops = dict(arrowstyle="fancy"))
ax.annotate(n[4], (z[4], y[4]), xytext=(z[4]-0.1, y[4]-0.2),
bbox=dict(boxstyle="round", alpha=0.1),
arrowprops = dict(arrowstyle="simple"))
plt.show()
Which will generate the following graph:
For limited set of values matplotlib is fine. But when you have lots of values the tooltip starts to overlap over other data points. But with limited space you can't ignore the values. Hence it's better to zoom out or zoom in.
Using plotly
import plotly.express as px
import pandas as pd
df = px.data.tips()
df = px.data.gapminder().query("year==2007 and continent=='Americas'")
fig = px.scatter(df, x="gdpPercap", y="lifeExp", text="country", log_x=True, size_max=100, color="lifeExp")
fig.update_traces(textposition='top center')
fig.update_layout(title_text='Life Expectency', title_x=0.5)
fig.show()
Python 3.6+:
coordinates = [('a',1,2), ('b',3,4), ('c',5,6)]
for x in coordinates: plt.annotate(x[0], (x[1], x[2]))
coordinates = [('a',(1,2)), ('b',(3,4)), ('c',(5,6))] and plt.annotate(*x)?This might be useful when you need individually annotate in different time (I mean, not in a single for loop)
ax = plt.gca()
ax.annotate('your_lable', (x,y))
where x and y are the your target coordinate and type is float/int.
As a one liner using list comprehension and numpy:
[ax.annotate(x[0], (x[1], x[2])) for x in np.array([n,z,y]).T]
setup is ditto to Rutger's answer.
deque(..., maxlen=0).