Line chart in Matplotlib Python (original) (raw)

Last Updated : 28 May, 2025

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

Matplotlib Simple Line Plot

**Example 1: 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 `

import matplotlib.pyplot as plt import numpy as np

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

plt.plot(x, y)
plt.show()

`

**Output

Simple line plot between X and Y data

**Explanation: This is a basic line chart where **x contains four points and **y is calculated as twice of each **x value. plt.plot() creates the line and plt.show() renders the plot.

**Example 2: 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

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

plt.plot(x, y) plt.xlabel("X-axis") # Label for the X-axis plt.ylabel("Y-axis") # Label for the Y-axis plt.title("Any suitable title") # Chart 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

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

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')

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

plt.grid(True) plt.show()

`

**Output

**Explanation: data points are marked with circles (marker='o') and labeled using **plt.annotate() to show their exact coordinates.

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

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

`

**Output

**Explanation: Here, two different line charts are plotted sequentially in separate figures using **plt.figure(). The first plot uses solid lines (default), and the second uses a dashed-dot pattern ('-.').

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

**Explanation: This plots two lines in the same figure and on the same axes. It allows direct comparison of different datasets. The second line uses a different style for visual distinction.

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

**Explanation: Highlights the area between two line plots using **plt.fill_between(). The alpha parameter adjusts transparency.