Multiple Plots
We have learned about the basic components of a graph that can be added so that it can convey more information. One method can be by calling the plot function again and again with a different set of values as shown in the above example. Now let’s see how to plot multiple graphs using some functions and also how to plot subplots.
Method 1: Using the add_axes() method
The add_axes() method is used to add axes to the figure. This is a method of figure class
Syntax:
add_axes(self, *args, **kwargs)
Example:
Python3
# Python program to show pyplot moduleimport matplotlib.pyplot as pltfrom matplotlib.figure import Figure# initializing the datax = [10, 20, 30, 40]y = [20, 25, 35, 55]# Creating a new figure with width = 5 inches# and height = 4 inchesfig = plt.figure(figsize =(5, 4))# Creating first axes for the figureax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])# Creating second axes for the figureax2 = fig.add_axes([1, 0.1, 0.8, 0.8])# Adding the data to be plottedax1.plot(x, y)ax2.plot(y, x)plt.show() |
Output:
Method 2: Using subplot() method.
This method adds another plot at the specified grid position in the current figure.
Syntax:
subplot(nrows, ncols, index, **kwargs)
subplot(pos, **kwargs)
subplot(ax)
Example:
Python3
import matplotlib.pyplot as plt# initializing the datax = [10, 20, 30, 40]y = [20, 25, 35, 55]# Creating figure objectplt.figure()# adding first subplotplt.subplot(121)plt.plot(x, y)# adding second subplotplt.subplot(122)plt.plot(y, x) |
Output:
Method 3: Using subplots() method
This function is used to create figures and multiple subplots at the same time.
Syntax:
matplotlib.pyplot.subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)
Example:
Python3
import matplotlib.pyplot as plt# initializing the datax = [10, 20, 30, 40]y = [20, 25, 35, 55]# Creating the figure and subplots# according the argument passedfig, axes = plt.subplots(1, 2)# plotting the data in the# 1st subplotaxes[0].plot(x, y)# plotting the data in the 1st# subplot onlyaxes[0].plot(y, x)# plotting the data in the 2nd # subplot onlyaxes[1].plot(x, y) |
Output:
Method 4: Using subplot2grid() method
This function creates axes object at a specified location inside a grid and also helps in spanning the axes object across multiple rows or columns. In simpler words, this function is used to create multiple charts within the same figure.
Syntax:
Plt.subplot2grid(shape, location, rowspan, colspan)
Example:
Python3
import matplotlib.pyplot as plt# initializing the datax = [10, 20, 30, 40]y = [20, 25, 35, 55]# adding the subplotsaxes1 = plt.subplot2grid ((7, 1), (0, 0), rowspan = 2, colspan = 1)axes2 = plt.subplot2grid ((7, 1), (2, 0), rowspan = 2, colspan = 1)# plotting the dataaxes1.plot(x, y)axes2.plot(y, x) |
Output:
Data Visualization using Matplotlib
Data Visualization is the process of presenting data in the form of graphs or charts. It helps to understand large and complex amounts of data very easily. It allows the decision-makers to make decisions very efficiently and also allows them in identifying new trends and patterns very easily. It is also used in high-level data analysis for Machine Learning and Exploratory Data Analysis (EDA). Data visualization can be done with various tools like Tableau, Power BI, Python.
In this article, we will discuss how to visualize data with the help of the Matplotlib library of Python.