Matplotlib Library Functions in Python

Matplotlib is a low level graph plotting library in python. It is created by John D. Hunter. Matplotlib is open source and Free. Matplotlib is mostly written in python. python library use to create 2D graphs.  it is numerical mathematical extension for NumPy library.

Pyplot Function

The pyplot functions are used to make some changes to figure. The matplotlib.pyplot is the collection command style function. There are usually imported under the plt alias.

Syntax

import  matplotlib.pyplot as plt.

Example

Draw a line in a diagram from position (0,0) to position (6.5,248).

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 6.5])
ypoints = np.array([0, 248])

plt.plot(xpoints, ypoints)
plt.show()

Output of pyplot functions

Matplotlip Plotting

This function is use to draw points in a diagram. By default, the Matplotlip Plotting function draws a line from point to point is knows as Matplotlip Function

The function takes two parameters for specific points in the diagram.

• Parameter 1 is an array contain the points on the x-axis.
• Parameter 2 is an array contain the points on the y-axis.

Example

Draw a line in a diagram from position (4, 9) to position (16, 18):

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([4, 16])
ypoints = np.array([9, 18])

plt.plot(xpoints, ypoints)
plt.show()

Matplotlip Markers

Matplotlip Markers is use both by the marker functionality of the plot and scatter is knows as Matplotip Markers.

Example

Mark each point with a circle:

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.array([4, 8, 10, 7])

plt.plot(ypoints, marker = ‘o’)
plt.show()

Marker	Description
*	Star
o	Circle
.	Point
,	Pixel
x	X
+	Plus
s	Square
D	Diamond
p	Pentagon
H	Hexagon
v	Triangle Down
^	Triangle Up
>	Triangle Right
<	Triangle Left
1	Tri Down
2	Tri Up
3	Tri Left
4	Tri Right
_	Hline
|	Vline

Matplotlip Line

Line style to change the style of the plotted line. ls means Line Style.

Line Styles

Style	Symbol
solid	–
dotted	:
dashed	—
dashdot	-.
None	or

Example

Use a dashed line:

import matplotlib.pyplot as plt
import numpy as np

ypoints = np.array([4, 8, 12, 7])

plt.plot(ypoints, linestyle = ‘dashed’)
plt.show()

Matplotlip label

The xlabel and ylabel functions to set a label for the x- and y-axis

Example

Add labels to the x- and y-axis:

import numpy as np
import matplotlib.pyplot as plt

x = np.array([81, 86, 91, 96, 101, 106, 111, 116, 121, 126])
y = np.array([241, 251, 261, 271, 281, 291, 301, 311, 321, 331])

plt.plot(x, y)

plt.xlabel(“Average book)
plt.ylabel(“Average copy”)

plt.show()

Matplotlib Grid

The grid function to add grid lines to the plot.

Example

Add grid lines to the plot

import numpy as np
import matplotlib.pyplot as plt

x = np.array([81, 86, 91, 96, 101, 106, 111, 116, 121, 126])
y = np.array([241, 251, 261, 271, 281, 291, 301, 311, 321, 331])

plt.title(“Student”)
plt.xlabel(“Average book”)
plt.ylabel(“Average copy”)

plt.plot(x, y)

plt.grid()

plt.show()

Matplotlib Subplot

This function you can draw multiple plots in one figure.

Example

Draw 3 plots

import matplotlib.pyplot as plt
import numpy as np

#plot 1:
x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])

plt.subplot(1, 2, 1)
plt.plot(x,y)

#plot 2:
x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])

plt.subplot(1, 2, 2)
plt.plot(x,y)

#plot 3:
x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])

plt.subplot(1, 2, 3)
plt.plot(x,y)

plt.show()

Matplotlib Scatter

This function to draw a scatter plot.

Example

A simple scatter plot

import matplotlib.pyplot as plt
import numpy as np

x = np.array([4,6,5,7,2,18,12,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,84,103,87,94,79,75,85,86])

plt.scatter(x, y)
plt.show()

Matplotlib Bars

This function to draw bar graphs.

Example

Draw 5 bars

import matplotlib.pyplot as plt
import numpy as np

x = np.array([“A”, “B”, “C”, “D” ,”E”])
y = np.array([3, 8, 1, 10, 12])

plt.bar(x,y)
plt.show()

Matplotlib Histograms

This graph showing frequency distributions.

Example

A simple histogram:

import matplotlib.pyplot as plt
import numpy as np

x = np.random.normal(150, 110, 25)

plt.hist(x)
plt.show() 

Matplotlib Pie Charts

This function to draw pie charts

Example

A simple pie chart:

mport matplotlib.pyplot as plt
import numpy as np

y = np.array([35, 20, 20, 25])

plt.pie(y)
plt.show()

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To know more about Matplotlib please Wikipedia click here

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