Matplotlib is a Python library that helps in visualizing and analyzing the data and helps in better understanding of the data with the help of graphical, pictorial visualizations that can be simulated using the matplotlib library. Matplotlib is a comprehensive library for static, animated and interactive visualizations.
Installation of matplotlib library
Step 1: Open command manager (just type “cmd” in your windows start search bar)
Step 2: Type the below command in the terminal.
cd Desktop
Step 3: Then type the following command.
pip install matplotlib
Creating a Simple Plot
Python3
# importing the required module import matplotlib.pyplot as plt # x axis values x = [1,2,3] # corresponding y axis values y = [2,4,1] # plotting the points plt.plot(x, y) # naming the x axis plt.xlabel('x - axis') # naming the y axis plt.ylabel('y - axis') # giving a title to my graph plt.title('My first graph!') # function to show the plot plt.show() |
Output:
The code seems self-explanatory. Following steps were followed:
- Define the x-axis and corresponding y-axis values as lists.
- Plot them on canvas using .plot() function.
- Give a name to x-axis and y-axis using .xlabel() and .ylabel() functions.
- Give a title to your plot using .title() function.
- Finally, to view your plot, we use .show() function.
Let’s have a look at some of the basic functions that are often used in matplotlib.
| Method | Description |
|---|---|
| plot() | it creates the plot at the background of computer, it doesn’t displays it. We can also add a label as it’s argument that by what name we will call this plot – utilized in legend() |
| show() | it displays the created plots |
| xlabel() | it labels the x-axis |
| ylabel() | it labels the y-axis |
| title() | it gives the title to the graph |
| gca() | it helps to get access over the all the four axes of the graph |
| gca().spines[‘right/left/top/bottom’].set_visible(True/False) | it access the individual spines or the individual boundaries and helps to change theoir visibility |
| xticks() | it decides how the markings are to be made on the x-axis |
| yticks() | it decides how the markings are to be made on the y-axis |
| gca().legend() | pass a list as it’s arguments of all the plots made, if labels are not explicitly specified then add the values in the list in the same order as the plots are made |
| annotate() | it is use to write comments on the graph at the specified position |
| figure(figsize = (x, y)) | whenever we want the result to be displayed in a separate window we use this command, and figsize argument decides what will be the initial size of the window that will be displayed after the run |
| subplot(r, c, i) | it is used to create multiple plots in the same figure with r signifies the no of rows in the figure, c signifies no of columns in a figure and i specifies the positioning of the particular plot |
| set_xticks | it is used to set the range and the step size of the markings on x – axis in a subplot |
| set_yticks | it is used to set the range and the step size of the markings on y – axis in a subplot |
Note: Try removing the features added one by one and understand how does the output result changes
Example 1:
Python3
import matplotlib.pyplot as plt a = [1, 2, 3, 4, 5]b = [0, 0.6, 0.2, 15, 10, 8, 16, 21]plt.plot(a) # o is for circles and r is # for redplt.plot(b, "or") plt.plot(list(range(0, 22, 3))) # naming the x-axisplt.xlabel('Day ->') # naming the y-axisplt.ylabel('Temp ->') c = [4, 2, 6, 8, 3, 20, 13, 15]plt.plot(c, label = '4th Rep') # get current axes commandax = plt.gca() # get command over the individual# boundary line of the graph bodyax.spines['right'].set_visible(False)ax.spines['top'].set_visible(False) # set the range or the bounds of # the left boundary line to fixed rangeax.spines['left'].set_bounds(-3, 40) # set the interval by which # the x-axis set the marksplt.xticks(list(range(-3, 10))) # set the intervals by which y-axis# set the marksplt.yticks(list(range(-3, 20, 3))) # legend denotes that what color # signifies whatax.legend(['1st Rep', '2nd Rep', '3rd Rep', '4th Rep']) # annotate command helps to write# ON THE GRAPH any text xy denotes # the position on the graphplt.annotate('Temperature V / s Days', xy = (1.01, -2.15)) # gives a title to the Graphplt.title('All Features Discussed') plt.show() |
Output:
Example 2:
Python3
import matplotlib.pyplot as plt a = [1, 2, 3, 4, 5]b = [0, 0.6, 0.2, 15, 10, 8, 16, 21]c = [4, 2, 6, 8, 3, 20, 13, 15] # use fig whenever u want the # output in a new window also # specify the window size you# want ans to be displayedfig = plt.figure(figsize =(10, 10)) # creating multiple plots in a # single plotsub1 = plt.subplot(2, 2, 1)sub2 = plt.subplot(2, 2, 2)sub3 = plt.subplot(2, 2, 3)sub4 = plt.subplot(2, 2, 4) sub1.plot(a, 'sb') # sets how the display subplot # x axis values advances by 1# within the specified rangesub1.set_xticks(list(range(0, 10, 1)))sub1.set_title('1st Rep') sub2.plot(b, 'or') # sets how the display subplot x axis# values advances by 2 within the# specified rangesub2.set_xticks(list(range(0, 10, 2)))sub2.set_title('2nd Rep') # can directly pass a list in the plot# function instead adding the referencesub3.plot(list(range(0, 22, 3)), 'vg')sub3.set_xticks(list(range(0, 10, 1)))sub3.set_title('3rd Rep') sub4.plot(c, 'Dm') # similarly we can set the ticks for # the y-axis range(start(inclusive),# end(exclusive), step)sub4.set_yticks(list(range(0, 24, 2)))sub4.set_title('4th Rep') # without writing plt.show() no plot# will be visibleplt.show() |
Output:
