# Learn Matplotlib for Data Science (Course III)

July 9, 2020 2020-08-04 10:46## Learn Matplotlib for Data Science (Course III)

### Basic Plotting using Matplotlib

In this chapter, we will get to learn about all the basic terminologies/properties of a Matplotlib plot through visual examples and descriptive code.

By learning about the terminologies, we can start to create our own line plot using Matplotlib. We will also change the different properties of a line plot and explore everything that there is to plotting with Matplotlib.

**The terminologies of a Matplotlib plot**

The following diagram shows the different terminologies used to describe a Matplotlib plot. Learning these at the beginning will be helpful since most methods used for plotting Matplotlib plots are based on the terminologies.

**Title:**The title of a Matplotlib plot is the heading that appears at the top of the figure.**Axes:**It is the region where data is plotted on a Matplotlib plot.**Axis:**These are the number-line-like objects. They take care of setting the graph limits and generating the ticks (the marks on the axis) and tick labels (strings labeling the ticks).**Grid:**It is the dotted line that divides the axes into multiple equal square spaces.**Label:**It is the notation for axis. For instance, we can define the x-axis label and y-axis label names.**Legend:**It is the notation for plots in the figure. It is often represented with distinct colors, shapes, and names.**Figure:**A figure in Matplotlib means the whole window in the user interface. Within this figure, there can be “subplots” that help to arrange plots in a regular grid where we need to specify the number of rows and columns and the number of the plot.

**Plotting your first Matplotlib Line Plot**

In this section, you will learn how to plot a simple line plot in Matplotlib.

First, import the Matplotlib library in Python using the knowledge gained from the previous chapter.

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline

Since we need some data to plot, let us define two Python lists representing the points of the line that we are about to plot.

# Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8]

Now, we can plot a line plot using the plot() method and show that plot using the show() method:

# plot() is used for plotting a line plot and show() is used for displaying the plot plt.plot(X, Y) plt.show()

Piecing all the code together, this is how you create a line plot in Matplotlib.

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline # Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8] # plot() is used for plotting a line plot and show() is used for displaying the plot plt.plot(X, Y) plt.show()

Congratulations, you just made your first plot in Matplotlib! Moving onwards, we will try to make this plot even more informative and interesting by using the various properties mentioned at the beginning of this chapter.

**Adding a Title and Axis Labels onto the Matplotlib plot**

You can provide a title and axis labels to the plot using the title(), xlabel() and ylabel() methods as shown below:

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline # Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8] # plot() is used for plotting a line plot plt.plot(X, Y) # Adding title, xlabel and ylabel plt.title('A Basic Line Plot') #Title of the plot plt.xlabel('X-axis') #X-Label plt.ylabel('Y-axis') #Y-Label # show() is used for displaying the plot plt.show()

**Changing the color and width of the line plot**

We can change the color and width of the line plot by defining the ‘color’ and ‘linewidth’ properties as follows:

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline # Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8] # plot() is used for plotting a line plot plt.plot(X, Y, color = 'green', linewidth = 5) # Adding title, xlabel and ylabel plt.title('A Basic Line Plot') #Title of the plot plt.xlabel('X-axis') #X-Label plt.ylabel('Y-axis') #Y-Label # show() is used for displaying the plot plt.show()

**Changing the figure size of the line plot**

The dimensions (width and height) of the line plot can be changed as following by defining a figure() method before plotting as follows:

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline # Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8] # Defining the figure shape fig = plt.figure(figsize = (10, 4)) # Width is 10 and height is 4 # plot() is used for plotting a line plot plt.plot(X, Y, color = 'green', linewidth = 5) # Adding title, xlabel and ylabel plt.title('A Basic Line Plot') #Title of the plot plt.xlabel('X-axis') #X-Label plt.ylabel('Y-axis') #Y-Label # show() is used for displaying the plot plt.show()

**Annotating Text and adding an arrow onto the Matplotlib Line Plot**

We can annotate text at a particular position of the plot using the annotate() method by specifying the following properties:

- xy: The point *(x,y)* to annotate.
- xytext: The position *(x,y)* to place the text at.
- arrowprops: Properties of the arrow as a dictionary.

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline # Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8] # Defining the figure shape fig = plt.figure(figsize = (10, 4)) # Width is 10 and height is 4 # plot() is used for plotting a line plot plt.plot(X, Y, color = 'green', linewidth = 5) # Adding annotations plt.annotate('This is a line', xy=(2.5, 5.5), xytext=(1.5, 7), arrowprops={'facecolor':'black', 'shrink':0.05}, ) # Adding title, xlabel and ylabel plt.title('A Basic Line Plot') #Title of the plot plt.xlabel('X-axis') #X-Label plt.ylabel('Y-axis') #Y-Label # show() is used for displaying the plot plt.show()

**Adding another line plot onto the same figure**

To add another line plot into the same figure, we can call the plot() method again for another set of data. Here, new data is added in the form of lists (A and B) and plotted using the plot() method.

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline # Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8] A = [1, 2, 3, 4] B = [1, 2, 3, 4] # Defining the figure shape fig = plt.figure(figsize = (10, 4)) # Width is 10 and height is 4 # plot() is used for plotting a line plot plt.plot(X, Y, color = 'green', linewidth = 5) plt.plot(A, B, color = 'red', linewidth = 5) # Adding annotations plt.annotate('This is a line', xy=(2.5, 5.5), xytext=(1.5, 7), arrowprops={'facecolor':'black', 'shrink':0.05}, ) # Adding title, xlabel and ylabel plt.title('A Basic Line Plot') #Title of the plot plt.xlabel('X-axis') #X-Label plt.ylabel('Y-axis') #Y-Label # show() is used for displaying the plot plt.show()

**Displaying a Legend on the Matplotlib Line Plot**

Legends are used when more than one plot is made in the same figure. A legend can be displayed using Matplotlib’s legend() method.

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline # Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8] A = [1, 2, 3, 4] B = [1, 2, 3, 4] # Defining the figure shape fig = plt.figure(figsize = (10, 4)) # Width is 10 and height is 4 # plot() is used for plotting a line plot plt.plot(X, Y, color = 'green', linewidth = 5) plt.plot(A, B, color = 'red', linewidth = 5) # Adding annotations plt.annotate('This is a line', xy=(2.5, 5.5), xytext=(1.5, 7), arrowprops={'facecolor':'black', 'shrink':0.05}, ) # Adding title, xlabel and ylabel plt.title('A Basic Line Plot') #Title of the plot plt.xlabel('X-axis') #X-Label plt.ylabel('Y-axis') #Y-Label # Giving labels to each line (to be shown in the legend) plt.legend(['Green Line', 'Red Line']) # show() is used for displaying the plot plt.show()

**Plotting subplots in Matplotlib**

Sometimes it is necessary to plot multiple plots within a single figure. In such cases, we use the subplots() function of Matplotlib which returns a tuple (`fig`

, `ax`

), giving a single figure `fig`

with an array of axes `ax`

.

Let us plot the two lines shown in the previous example in two different plots using subplots():

# Libraries/Modules import conventions import matplotlib.pyplot as plt # For interactive matplotlib sessions, turn on the matplotlib inline mode %matplotlib inline # Dummy Data X = [1, 2, 3, 4] Y = [2, 4, 6, 8] A = [1, 2, 3, 4] B = [1, 2, 3, 4] # Defining the figure shape fig, ax = plt.subplots(1, 2) # One row and two columns # plot() is used for plotting a line plot ax[0].plot(X, Y, color = 'green', linewidth = 5) ax[1].plot(A, B, color = 'red', linewidth = 5) # show() is used for displaying the plot plt.show()

Now you know the basics of a Matplotlib plot and you can start plotting your own line plots as needed.

Starting from the next chapter, we will learn how to plot other kinds of plots using Matplotlib and not just line plots. Head over to the next chapter to learn about the different available 2D plots in Matplotlib.