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Matplotlib - PyPlot



matplotlib.pyplot is a state-based interface and contains collection of functions that provides a MATLAB-like way of plotting in matplotlib. Each pyplot function makes some change to a figure. For example: creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc.

The object-oriented API is recommended for more complex plots.

The most frequently used functions of Pyplot are listed below:

Types of Plots

FunctionsDescription
bar() Makes a bar plot.
barh() Makes a horizontal bar plot.
boxplot() Makes a box and whisker plot.
contour() Plots contour lines.
contourf() Plots filled contours.
hist() Plots a histogram.
hist2d() Makes a 2D histogram plot.
pie() Plots a pie chart.
plot() Plots y versus x as lines and/or markers.
quiver() Plots a 2D field of arrows.
scatter() Plots a scatter plot of x vs y.
step() Makes a step plot.
stackplot() Draws a stacked area plot.
streamplot() Draws streamlines of a vector flow.
triplot() Draws a unstructured triangular grid as lines and/or markers.
violinplot() Makes a violin plot.

Axes Functions

FunctionsDescription
axes() Adds an axes to the current figure and make it the current axes.
text() Adds text to the Axes.
title() Sets a title for the Axes.
xlabel() Sets the label for the x-axis.
xlim() Gets or sets the x limits of the current axes.
xscale() Sets the x-axis scale.
xticks() Gets or sets the current tick locations and labels of the x-axis.
ylabel() Sets the label for the y-axis.
ylim() Gets or sets the y-limits of the current axes.
yscale() Sets the y-axis scale.
yticks() Gets or sets the current tick locations and labels of the y-axis.
legend() Places a legend on the Axes.

Figure Functions

FunctionsDescription
figlegend() Place a legend on the figure.
figtext() Add text to figure.
figure() Create a new figure, or activate an existing figure.
show() Display all open figures.
savefig() Save the current figure.
close() Close a figure window.
clf() Clear the current figure.

Image Functions

FunctionsDescription
imread() Read an image from a file into an array.
imsave() Save an array as an image file.
imshow() Display data as an image, i.e., on a 2D regular raster.

Creating plot using Pyplot

Before using any matplotlib.pyplot function, first of all the Pyplot module is imported from Matplotlib as shown below:

import matplotlib.pyplot as plt

After importing the module, all functions inside Pyplot module can be used in the current script.

Example: Simple plot

Consider the example below where plot() function is used to plot y = sin(x). After that, the axes functions are used to format the axes. At last, show() function is used to display the figure.

import matplotlib.pyplot as plt
import numpy as np

#creating a array of values between
#0 to 10 with a difference of 0.1
x = np.arange(0, 10, 0.1)
y = np.sin(x)

#plotting the curve
plt.plot(x, y) 

#formatting axes
plt.xlabel("x")
plt.ylabel("y = sin(x)")
plt.title("Sine wave") 

#displaying the figure
plt.show()

The output of the above code will be:

Pyplot

Example: Adding legend

The example below shows how to add two plots in a single figure.

import matplotlib.pyplot as plt
import numpy as np

#creating a array of values between
#0 to 10 with a difference of 0.1
x = np.arange(0, 10, 0.1)
y1 = np.sin(x)
y2 = np.cos(x)

#plotting curves
plt.plot(x, y1) 
plt.plot(x, y2) 

#formatting axes
plt.xlabel("x")
plt.ylabel("y")
plt.title("Sine vs Cosine") 

#adding legend
plt.legend(['sin(x)', 'cos(x)']) 

#displaying the figure
plt.show()

The output of the above code will be:

Pyplot

Format String

A format string consists of a part for color, marker and line:

fmt = '[marker][line][color]'

Each of them is optional. If not provided, the value from the style cycle is used. Other combinations such as [color][marker][line] are also supported, but note that their parsing may be ambiguous.

A format string can be added to a plot to add more styles in it.

Markers

CharacterDescription
'.' point marker
',' pixel marker
'o' circle marker
'v' triangle_down marker
'^' triangle_up marker
'<' triangle_left marker
'>' triangle_right marker
'1' tri_down marker
'2' tri_up marker
'3' tri_left marker
'4' tri_right marker
'8' octagon marker
's' square marker
'p' pentagon marker
'P' plus (filled) marker
'*' star marker
'h' hexagon1 marker
'H' hexagon2 marker
'+' plus marker
'x' x marker
'X' x (filled) marker
'D' diamond marker
'd' thin_diamond marker
'|' vline marker
'_' hline marker

Line styles

CharacterDescription
'-' solid line style
'--' dashed line style
'-.' dash-dot line style
':' dotted line style

Colors

CharacterDescription
'b' blue
'g' green
'r' red
'c' cyan
'm' magenta
'y' yellow
'k' black
'w' white

Example: Using format string

In the example below, format string is used to add more styles in the plot.

import matplotlib.pyplot as plt
import numpy as np

#creating a array of values between
#0 to 10 with a difference of 0.5
x = np.arange(0, 10, 0.5)
y1 = np.sin(x)
y2 = np.cos(x)

#plotting curves
plt.plot(x, y1, 'o-r') 
plt.plot(x, y2, 'v--b') 

#formatting axes
plt.xlabel("x")
plt.ylabel("y")
plt.title("Sine vs Cosine") 

#adding legend
plt.legend(['sin(x)', 'cos(x)']) 

#displaying the figure
plt.show()

The output of the above code will be:

Pyplot

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