matplotlib
  1. matplotlib-introduction
PreviousNext

Introduction - ( Matplotlib Tutorial )

Heading h2

Syntax

import matplotlib.pyplot as plt
plt.plot(x, y)
plt.show()

Example

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

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

Output

A sine wave plot is displayed on executing the above code.

Explanation

Matplotlib is a popular Python library used for data visualization. It provides various plotting functions that allow users to create a wide range of 2D and 3D plots from data. The plot() function is one of the most basic and widely used functions in Matplotlib. It takes in two arrays, the x-coordinates and the y-coordinates, and plots them on a graph.

In the above example, we first create an array x using numpy.arange() function and compute the y values using the numpy.sin() function. We then pass the x and y arrays to the plot() function to plot the sine wave. Finally, the plot is displayed using the show() function.

Use

Matplotlib is used for creating high-quality visualizations of data. It is widely used in scientific, engineering, and financial applications. Matplotlib can be used to create a wide range of plots including line plots, scatter plots, bar plots, histograms, and many more.

Important Points

  • Matplotlib is a popular Python library used for data visualization
  • The plot() function is one of the most basic and widely used functions in Matplotlib
  • It takes in two arrays, the x-coordinates and the y-coordinates, and plots them on a graph
  • Matplotlib can be used to create a wide range of plots including line plots, scatter plots, bar plots, histograms, and many more.

Summary

In conclusion, Matplotlib is a powerful Python library for creating high-quality visualizations of data. The plot() function is one of the most basic and widely used functions in Matplotlib that can be used to create 2D line plots and visualize data. It is widely used in scientific, engineering, and financial applications to visualize data and understand complex relationships.

MatplotlibFeatures
Published on: