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Matplotlib Pyplot - GeeksforGeeks

Matplotlib Pyplot

Last Updated : 12 Jul, 2025

Pyplot is a submodule of the Matplotlib library in python and beginner-friendly tool for creating visualizations providing a MATLAB-like interface, to generate plots with minimal code.

How to Use Pyplot for Plotting?

To use Pyplot we must first download the Matplotlib module. For this write the following command: pip install matplotlib

The plot() method is one of the most essential functions in Pyplot used to create line graphs, which are the foundation of most visualizations. This method takes at least two arguments: the x-values and the y-values. Syntax:

matplotlib.pyplot.plot()

1. Using plot() for Line Graphs

Let's creating a basic line plot using plot(): plot function marks the x-coordinates(1, 2, 3, 4) and y-coordinates(1, 4, 9, 16) in a linear graph with specified scales.

Python


 import matplotlib.pyplot as plt
x=[1, 2, 3, 4]
y=[1, 4, 9, 16]
plt.plot(x,y)
plt.show()

Output:

Plotting a line using Matplotlib 2. Using pie() for Pie Charts

Use plt.pie() instead of plt.plot().

Python


 import matplotlib.pyplot as plt
labels = ['Python', 'Java', 'C++', 'JavaScript']
sizes = [40, 30, 20, 10]
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.show()

Output:

Pie chart using pyplot 3. Plotting Bar Charts Using bar()

Bar charts are useful for comparing quantities. Use the bar() method to create a vertical or horizontal bar chart by specifying the x and y values.

Python


 import matplotlib.pyplot as plt
categories = ['A', 'B', 'C', 'D']
values = [3, 7, 2, 5]
plt.bar(categories, values)
plt.show()

Output:

Bar Chart using Pyplot 4. Using scatter() for Scatter Plots

A scatter plot is useful for visualizing individual data points.

Python


 import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [5, 7, 9, 11, 13]
plt.scatter(x, y)
plt.show()

Output:

Scatter Plot using Pyplot 5. Working with Histograms Using hist()

Histograms are useful for visualizing the distribution of data.

Python


 import matplotlib.pyplot as plt
import numpy as np
data = np.random.randn(1000)
plt.hist(data, bins=30)
plt.show()

Output:

Histogram Using Pyplot

Now here, we have understood how to visualize basic plots using pyplot module. Now the next step is to customize these plots according to specific needs and Creating subplots ( multiple plots within a single figure). Refer to resources below:

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