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Annotate Matplotlib Chart

You can annotate any point in your chart with text using the annotate() function. The function parameters used in the example below are:

• text : The text of the annotation
• xy : The point (x,y) to annotate
• xytext : The position (x,y) to place the text at (If None, defaults to xy)
• arrowprops : The properties used to draw an arrow between the positions xy and xytext

# Libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
#Data
df=pd.DataFrame({'x_pos': range(1,101), 'y_pos': np.random.randn(100)*15+range(1,101) })

# Basic chart
plt.plot('x_pos', 'y_pos', data=df,  linestyle='none', marker='o')
 
# Annotate with text + Arrow
plt.annotate(
# Label and coordinate
'This point is interesting!', xy=(25, 50), xytext=(0, 80),
 
# Custom arrow
arrowprops=dict(facecolor='black', shrink=0.05))

# Show the graph
plt.show()

You can add a text of mathematical expression to your plot with text() function:

# Library
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# plot
df=pd.DataFrame({'x_pos': range(1,101), 'y_pos': np.random.randn(100)*15+range(1,101) })
plt.plot( 'x_pos', 'y_pos', data=df, linestyle='none', marker='o')
 
# Annotation
plt.text(40, 0, r'equation: $\sum_{i=0}^\infty x_i$', fontsize=20)

# Show the graph
plt.show()

You can use the add_patch() function to add a matplotlib patch to the axes. In the example below, you will see how to add a rectangle. You can see the list of patches here.

# libraries
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# Data
df=pd.DataFrame({'x_pos': range(1,101), 'y_pos': np.random.randn(100)*15+range(1,101) })
 
# Plot
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot( 'x_pos', 'y_pos', data=df, linestyle='none', marker='o')
 
# Add rectangle
ax1.add_patch(
patches.Rectangle(
(20, 25), # (x,y)
50, # width
50, # height
# You can add rotation as well with 'angle'
alpha=0.3, facecolor="red", edgecolor="black", linewidth=3, linestyle='solid')
)

# Show the graph
plt.show()

# Libraries
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# Data
df=pd.DataFrame({'x_pos': range(1,101), 'y_pos': np.random.randn(100)*15+range(1,101) })
 
# Plot
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot( 'x_pos', 'y_pos', data=df, linestyle='none', marker='o')
 
# Annotation
ax1.add_patch(
patches.Circle(
(40, 35),           # (x,y)
30,                    # radius
alpha=0.3, facecolor="green", edgecolor="black", linewidth=1, linestyle='solid'
)
)

# Show the graph
plt.show()

# libraries
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# Data
df=pd.DataFrame({'x_pos': range(1,101), 'y_pos': np.random.randn(100)*15+range(1,101) })
 
# Plot
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot( 'x_pos', 'y_pos', data=df, linestyle='none', marker='o')
ax1.add_patch(
patches.Ellipse(
(40, 35), # (x,y)
30, # width
100, # height
45, # radius
alpha=0.3, facecolor="green", edgecolor="black", linewidth=1, linestyle='solid'
)
)

# Show the graph
plt.show()

In the example below, a line segment is added to the scatterplot by using plot() function. The first function draws the scatterplot and the second one draws a line segment by passing 'solid' as a linestyle parameter.

# Libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# Data
df=pd.DataFrame({'x_pos': range(1,101), 'y_pos': np.random.randn(100)*15+range(1,101) })
    
# Basic chart
plt.plot( 'x_pos', 'y_pos', data=df, linestyle='none', marker='o')

# Annotation
plt.plot([80, 40], [30, 90], color="skyblue", lw=5, linestyle='solid', label="_not in legend")

# Show the graph
plt.show()

It is possible to add a vertical and horizontal lines to a basic matplotlib chart using the axvline() and the axhline() functions:

# Libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
# Data   
df=pd.DataFrame({'x_pos': range(1,101), 'y_pos': np.random.randn(100)*15+range(1,101) })
    
# Plot
plt.plot( 'x_pos', 'y_pos', data=df, linestyle='none', marker='o')
 
# Annotation
plt.axvline(40, color='r')
plt.axhline(40, color='green')

# Show the graph
plt.show()

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