Plot Line in Python

Python's Matplotlib module is used for data visualization. A set of methods called pyplot, a submodule of matplotlib, aids in creating several charts. The relationship between two sets of data, X and Y, is shown using line plots on a distinct axis. Here, we'll look at a few Python line chart illustrations.

Simple Line Plots

To begin using the plotting methods, import the Matplotlib.pyplot library. Import the Numpy module as well, if necessary. Then specify the x and y data points.

Code

# Python program to plot a simple line using the matplotlib library

# Import the necessary libraries
import numpy as np
import matplotlib.pyplot as plt
  
# Define the X and Y dataset
# Points of X-Axis
X = np.array([2, 4, 6, 8, 10])
# Points of Y-Axis
Y = X*2 

# Plotting the line
plt.plot(X, Y) 

# Displaying the line chart
plt.show()  

Output:

The x and the y-axis are not labeled, as seen in the output image up top, since labeling is based on recognizing the dimensions of the graph. In the following instance, we will demonstrate how to add axis labels and Ident in plots.

Code

# Python program to plot a simple line using the matplotlib library

# Import the necessary libraries
import numpy as np
import matplotlib.pyplot as plt
  
# Define the X and Y dataset
# Points of X-Axis
X = np.array([2, 4, 6, 8, 10])
# Points of Y-Axis
Y = X*2 

# Plotting the line
plt.plot(X, Y) 

# Adding title and labels
plt.xlabel("X-axis Label")  # adding the X-axis label
plt.ylabel("Y-axis Label")  # adding the Y-axis label
plt.title("This is the title of of the plot")  # adding the title

# Displaying the line chart
plt.show()  

Output:

Multiple Charts

We can display more than one chart in the same container with the pyplot.figure() method, we may display multiple charts in a single box. This will enable us to compare several charts and manage the appearance and feel of the graphs.

Code

# Python program to plot multiple line charts in a single output

# Import the necessary libraries
import numpy as np
import matplotlib.pyplot as plt
  
# Define the X and Y dataset
# Points of X-Axis
X = np.array([2, 4, 6, 8, 10])
# Points of Y-Axis
Y = X*2 

# Plotting the first line
plt.plot(X, Y) 

# Adding title and labels
plt.xlabel("X-axis Label")  
plt.ylabel("Y-axis Label")  
plt.title("This is the first line plot") 

# Displaying the first line chart
plt.show()  

# The figure() method will help create a distinct figure that stores a different chart.
plt.figure()
X1 = [3, 6, 9, 12, 15]
Y1 = [5, 10, 15, 20, 25]
plt.plot(X1, Y1, '-.')

# Adding title and labels
plt.xlabel("X-axis Label")  
plt.ylabel("Y-axis Label")  
plt.title("This is the second line plot") 
  
# Displaying the second line chart with '-.' line
plt.show()

Output:

Dotted Line

Dots can represent lines, as seen in the illustration below. Use the scatter(x,y) technique rather than plot(x,y). The graph can also have points (randomly) plotted on it using the scatter(x,y) function.

Code

# Python program to show how to plot a scatter plot

# Importing the required libraries
import numpy as np
import matplotlib.pyplot as plt

# Creating the X and Y dataset
n = 256
X = np.random.normal(0, 5, n)
Y = np.random.normal(0, 5, n)
tan = np.arctan2(X, Y)

# Plotting the scatter plot
plt.scatter(tan, Y)

# Displaying the xticks and yticks
plt.xticks()
plt.yticks()

# Displaying the plot
plt.show()

Output:

Line Ticks

We can change the graph's ticks. Customize their color or place them on the y-axis. The line has a transparency level called the alpha value and can change the thickness.

Code

# Python program to show how to format the ticks of the axes of graphs

# Importing the libraries
import matplotlib.pyplot as plt
import numpy as np

# Creating the X and Y dataset
X = np.linspace(-2, 2, 50)
Y = 2*X - 3

# Creating our figure
plt.figure(figsize = (10, 7)) 
plt.plot(X, Y, color='b', linewidth = 8.0, alpha = 0.5)

# Getting the axis object
axis = plt.gca()

# Changing the spines
axis.spines['top'].set_color('red')
axis.spines['right'].set_color('red')

# Changing the position of the ticks on the axis
axis.xaxis.set_ticks_position('bottom')
axis.yaxis.set_ticks_position('left')

# Changing the position of spines
axis.spines['left'].set_position(('data', 0))
axis.spines['bottom'].set_position(('data', 0))

# Formatting the ticks
for t in axis.get_xticklabels() + axis.get_yticklabels():
    t.set_fontsize(10)
    t.set_bbox(dict(facecolor = 'grey', edgecolor = 'None', alpha = 0.4))

# Displaying the plot
plt.show()

Output:

Line with Asymptote

The line plot can include an asymptote. Use plt.annotate to accomplish it. A dotted line is also present in the following plot. To learn how the program functions, experiment with it.

Code

# Python program to show how to plot the asymptotes

# Importing the libraries
import matplotlib.pyplot as plt
import numpy as np

# Creating the X and Y dataset
X = np.linspace(-2, 2, 50)
Y1 = 2*X + 2
Y2 = 2**X + 3

# Creating the figure
plt.figure(figsize = (10, 6))  
plt.plot(X, Y2)
plt.plot(X, Y1, color = 'blue', linewidth = 2.0, linestyle = '--')

# Getting the plot axis
axis = plt.gca()

# Setting the color of the spines
axis.spines['right'].set_color('red')
axis.spines['top'].set_color('red')

# Setting the position of the ticks
axis.xaxis.set_ticks_position('bottom')
axis.yaxis.set_ticks_position('left')

axis.spines['bottom'].set_position(('data', 0))
axis.spines['left'].set_position(('data', 0))

# Asymptotes
x0 = 1.5
y0 = 2*x0 + 2

# Plotting the curves
plt.scatter(x0, y0, s = 60, color = 'k')
plt.plot([x0, x0], [y0, 0], 'b-.', lw = 3)

# Annotating the figure
plt.annotate(r'$2x+1=%s$' % 
             y0, 
             xy = (x0, y0), 
             xycoords = 'data',
             xytext = (+30, -30),
             textcoords = 'offset points',
             fontsize = 15,  
             arrowprops = dict(arrowstyle = '->',connectionstyle = 'arc3,rad=.2')
            )

# Printing the text on the graph
plt.text(0, 3, 
         r'$This\ is\ a\ good\ value.\ \mu\ \sigma_i\ \alpha_t$',
         fontdict = {'size' : 16,'color' : 'g'})

# Displaying the plot
plt.show()

Output:

Line with Text Scale

It is not required to use a numerical scale. Textual elements like those in the instance below may also be present in the scale. We simply supply a list of text values to plt.yticks(). The y-axis is then used to display these data.

Code

# Python program to show how to give text ticks to the axis

# Importing the libraries
import matplotlib.pyplot as plt
import numpy as np

# Creating the X and Y dataset
X = np.linspace(-2, 2, 50)
Y1 = 2*X 
Y2 = 2**X 

# Creating a figure
plt.figure(num = 4, figsize=(10, 6))  
plt.plot(X, Y2)

# Plotting the chart
plt.plot(X, Y1, 
         color = 'red',  
         linewidth = 2.0,  
         linestyle = '--'  
        )

# Setting limits of x and y axis
plt.xlim((-1, 2))  
plt.ylim((1, 3))  

# Modifying the ticks
new_t = np.linspace(-2, 2, 6)
plt.xticks(new_t)
plt.yticks([-3, -1.5, 0, 1.5, 3],
          [r'$very\ bad$', r'$little\ bad$', r'$normal$', r'$little\ good$', r'$very\ good$'])

# Getting the axis
axis = plt.gca()
axis.spines['right'].set_color('red')
axis.spines['top'].set_color('red')

# Setting the ticks position
axis.xaxis.set_ticks_position('bottom')
axis.yaxis.set_ticks_position('left')

# Setting the position of spines
axis.spines['bottom'].set_position(('data', 0))
axis.spines['left'].set_position(('data', 0))

# Displaying the plot
plt.show()

Output:

Multiple Plots on the Same Axis

Here we will see how to add 2 plots within the same axis.

Code

# Python program to show how to plot multiple plots on one graph

# Importing the required libraries
import matplotlib.pyplot as plt
import numpy as np

# Creating the X and Y dataset
x = np.array([2, 4, 6, 8, 10])
y = x*3
  
# Plotting the X and Y data
plt.plot(x, y)
  
x_ = [3, 6, 9, 12, 15]
y_ = [4, 6, 10, 15, 20]
  
# Plotting the x1 and y1 data
plt.plot(x_, y_, '-.')
  
plt.xlabel("This is the X-axis data")
plt.ylabel("This is the Y-axis data")
plt.title('Multiple line plots')
plt.show()

Output: