# [python] Matplotlib scatter plot with different text at each data point

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]
z = [0.15, 0.3, 0.45, 0.6, 0.75]
n = [58, 651, 393, 203, 123]
ax1.scatter(z, y, fmt='o')
``````

Any ideas?

This question is related to `python` `matplotlib` `text` `scatter-plot` `annotate`

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.

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.

In case anyone is trying to apply the above solutions to a .scatter() instead of a .subplot(),

I tried running the following code

``````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.scatter(z, y)

for i, txt in enumerate(n):
ax.annotate(txt, (z[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(z, y)

I eventually solved the error using the following code

``````plt.scatter(z, y)

for i, txt in enumerate(n):
plt.annotate(txt, (z[i], y[i]))
``````

I didn't expect there to be a difference between .scatter() and .subplot() I should have known better.

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)
``````

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.

``````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.

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:

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]))
``````

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
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()
``````