Matplotlib text() / annotate() Function

Source: https://example.com/matplotlib/matplotlib-ref-text.html

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Matplotlib Reference Documentation

text() adds text at specified coordinates on the chart, annotate() adds annotations with arrows that can precisely point to data points.

Function Definition

text()

matplotlib.pyplot.text(x, y, s, fontdict=None, **kwargs)
Axes.text(x, y, s, fontdict=None, **kwargs)

annotate()

matplotlib.pyplot.annotate(text, xy, xytext=None, xycoords='data', textcoords=None, arrowprops=None, annotation_clip=None, **kwargs)
Axes.annotate(text, xy, xytext=None, xycoords='data', textcoords=None, arrowprops=None, annotation_clip=None, **kwargs)

Parameter Description

text() Parameters

Parameter	Description
x, y	Text position coordinates
s	Text string to display
fontsize	Font size
color	Text color
ha / horizontalalignment	Horizontal alignment: 'center', 'right', 'left'
va / verticalalignment	Vertical alignment: 'center', 'top', 'bottom', 'baseline'
rotation	Rotation angle (in degrees)
fontweight	Font weight: 'normal', 'bold', 'light'
bbox	Text background box properties, e.g., dict(facecolor='yellow', alpha=0.5)

annotate() Specific Parameters

Parameter	Description
text	Annotation text content
xy	Coordinates of the target point being pointed to (x, y) (arrow tip)
xytext	Coordinates where text is placed. Default None (text placed at xy position)
xycoords / textcoords	Coordinate system for xy / xytext: 'data', 'axes fraction', 'figure fraction', 'offset points', etc.
arrowprops	Arrow properties dictionary, e.g., dict(arrowstyle='->', color='gray', lw=1.5)

The core value of annotate is that xy and xytext can separately specify the coordinate point and text position, and connect them with an arrow, which text() cannot do.

Common arrowprops Arrow Styles

Style	Effect
->	Solid triangular arrow
-	No line
<->	Bidirectional arrow
-[widthB=...,lengthB=...]	Custom rectangular arrow
fancy	Elegant curved arrow
simple	Simple straight arrow

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Usage Examples

Example 1: Basic Text Addition (text)

Instance

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(0,10,100)

 y = np.sin(x)

fig, ax = plt.subplots(figsize=(8,4), layout='constrained')

ax.plot(x, y,'steelblue', linewidth=2)

# Add text marker at peak

 peak_idx = np.argmax(y)

 ax.text(x, y + 0.1,

 f'Peak: ({x:.2f}, {y:.2f})',

 fontsize=10, color='red',

 ha='center', va='bottom',

 fontweight='bold')

# Add text + background box at valley

 valley_idx = np.argmin(y)

 ax.text(x, y - 0.15,

 f'Valley: ({x:.2f}, {y:.2f})',

 fontsize=10, ha='center', va='top',

 bbox=dict(boxstyle='round,pad=0.3',

 facecolor='yellow', alpha=0.7))

ax.set_title('text() - Adding Text at Specific Coordinates')

 ax.set_xlabel('x')

 ax.set_ylabel('sin(x)')

 ax.grid(True, alpha=0.3)

 plt.show()

Example 2: Arrow Annotation (annotate)

Instance

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(0,10,50)

 y = np.exp(-x/3) * np.sin(2 * x)# Damped oscillation

fig, ax = plt.subplots(figsize=(8,5), layout='constrained')

 ax.plot(x, y,'steelblue', linewidth=2, marker='o', markersize=4)

# Annotate first peak (arrow from text points to data point)

 peak1_idx = np.argmax(y)

 ax.annotate('1st Peak',

 xy=(x, y),# Point being pointed to by arrow

 xytext=(x + 1.5, y + 0.2),# Text position

 arrowprops=dict(arrowstyle='->',

 color='red', lw=1.5),

 fontsize=11, color='red', fontweight='bold')

# Annotate decay region

 ax.annotate('Decay region',

 xy=(.7,.3), xycoords='axes fraction',# Use axes coordinates

 xytext=(0.5,0.8), textcoords='axes fraction',

 arrowprops=dict(arrowstyle='fancy',

 color='green',

 connectionstyle='arc3,rad=0.3'),

 fontsize=11, color='green',

 bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))

ax.set_title('annotate() - Annotations with Arrows')

 ax.set_xlabel('Time (s)')

 ax.set_ylabel('Amplitude')

 ax.grid(True, alpha=0.3)

 plt.show()

Example 3: Formula Annotation and Coordinate System

Instance

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(0,4*np.pi,200)

 y1 = np.sin(x)

 y2 = np.cos(x)

fig, ax = plt.subplots(figsize=(8,5), layout='constrained')

 ax.plot(x, y1,'blue',