Line chart in Matplotlib Python (original) (raw)

Last Updated : 13 Aug, 2024

**Matplotlib is a data visualization library in Python. The **pyplot, a sublibrary of Matplotlib, is a collection of functions that helps in creating a variety of charts. **Line charts are used to represent the relation between two data X and Y on a different axis. In this article, we will learn about line charts and matplotlib simple line plots in Python.

Python Line chart in Matplotlib

Here, we will see some of the examples of a line chart in Python using Matplotlib:

Matplotlib Simple Line Plot

In this example, a simple line chart is generated using NumPy to define data values. The x-values are evenly spaced points, and the y-values are calculated as twice the corresponding x-values.

Python `

importing the required libraries

import matplotlib.pyplot as plt import numpy as np

define data values

x = np.array([1, 2, 3, 4]) # X-axis points y = x*2 # Y-axis points

plt.plot(x, y) # Plot the chart plt.show() # display

`

**Output:

Simple line plot between X and Y data

We can see in the above output image that there is no label on the x-axis and y-axis. Since labeling is necessary for understanding the chart dimensions. In the following example, we will see how to add labels, Ident in the charts.

Python `

import matplotlib.pyplot as plt import numpy as np

Define X and Y variable data

x = np.array([1, 2, 3, 4]) y = x*2

plt.plot(x, y) plt.xlabel("X-axis") # add X-axis label plt.ylabel("Y-axis") # add Y-axis label plt.title("Any suitable title") # add title plt.show()

`

**Output:

Simple line plot with labels and title

Line Chart with Annotations

In this example, a line chart is created using sample data points. Annotations displaying the x and y coordinates are added to each data point on the line chart for enhanced clarity.

Python `

import matplotlib.pyplot as plt

Sample data

x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10]

Create a line chart

plt.figure(figsize=(8, 6)) plt.plot(x, y, marker='o', linestyle='-')

Add annotations

for i, (xi, yi) in enumerate(zip(x, y)): plt.annotate(f'({xi}, {yi})', (xi, yi), textcoords="offset points", xytext=(0, 10), ha='center')

Add title and labels

plt.title('Line Chart with Annotations') plt.xlabel('X-axis Label') plt.ylabel('Y-axis Label')

Display grid

plt.grid(True)

Show the plot

plt.show()

`

**Output:

Multiple Line Charts Using Matplotlib

We can display more than one chart in the same container by using pyplot.figure() function. This will help us in comparing the different charts and also control the look and feel of charts.

Python `

import matplotlib.pyplot as plt import numpy as np

x = np.array([1, 2, 3, 4]) y = x*2

plt.plot(x, y) plt.xlabel("X-axis") plt.ylabel("Y-axis") plt.title("Any suitable title") plt.show() # show first chart

The figure() function helps in creating a

new figure that can hold a new chart in it.

plt.figure() x1 = [2, 4, 6, 8] y1 = [3, 5, 7, 9] plt.plot(x1, y1, '-.')

Show another chart with '-' dotted line

plt.show()

`

**Output:

Multiple Plots on the Same Axis

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

Python `

import matplotlib.pyplot as plt import numpy as np

x = np.array([1, 2, 3, 4]) y = x*2

first plot with X and Y data

plt.plot(x, y)

x1 = [2, 4, 6, 8] y1 = [3, 5, 7, 9]

second plot with x1 and y1 data

plt.plot(x1, y1, '-.')

plt.xlabel("X-axis data") plt.ylabel("Y-axis data") plt.title('multiple plots') plt.show()

`

**Output:

Fill the Area Between Two Lines

Using the pyplot.fill_between() function we can fill in the region between two line plots in the same graph. This will help us in understanding the margin of data between two line plots based on certain conditions.

Python `

import matplotlib.pyplot as plt import numpy as np

x = np.array([1, 2, 3, 4]) y = x*2

plt.plot(x, y)

x1 = [2, 4, 6, 8] y1 = [3, 5, 7, 9]

plt.plot(x, y1, '-.') plt.xlabel("X-axis data") plt.ylabel("Y-axis data") plt.title('multiple plots')

plt.fill_between(x, y, y1, color='green', alpha=0.5) plt.show()

`

**Output:

Fill the area between Y and Y1 data corresponding to X-axis data