Jupyter Matplotlib and Plotly

Introduction

This page provides an overview of using Matplotlib and Plotly in Jupyter notebooks for data visualization.

Matplotlib

Syntax

import matplotlib.pyplot as plt

# Your plotting code here

plt.show()

Example

import matplotlib.pyplot as plt
import numpy as np

# Generate data
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)

# Plotting
plt.plot(x, y, label='Sin Function')
plt.title('Matplotlib Example')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.show()

Output

Explanation

In this example, we generate a sine wave and plot it using Matplotlib. The plt.plot() function is used to create the plot, and additional functions like plt.title() and plt.xlabel() are used for customization.

Use

Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. It is widely used for a variety of plotting tasks.

Important Points

• Matplotlib provides a wide range of customization options.
• It supports various types of plots, including line plots, scatter plots, bar plots, and more.
• Matplotlib can be used for creating publication-quality figures.

Plotly

Syntax

import plotly.express as px

# Your plotting code here

fig.show()

Example

import plotly.express as px

# Generate data
df = px.data.iris()

# Plotting
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species', title='Plotly Example')
fig.show()

Output

Explanation

This Plotly example uses the Iris dataset to create a scatter plot. The px.scatter() function is used for quick plotting, and the resulting figure is displayed using fig.show().

Use

Plotly is a powerful library for creating interactive and dynamic visualizations. It supports a wide range of chart types and is well-suited for web-based applications.

Important Points

• Plotly is known for its interactive features, such as zooming, panning, and tooltips.
• It can be used for creating dashboards and web applications.
• Plotly supports 3D plotting and geographical maps.

Summary

In this Jupyter notebook page, we covered the basics of using Matplotlib and Plotly for data visualization. Matplotlib is a go-to library for static plots, while Plotly excels in creating interactive and dynamic visualizations. Depending on your needs, you can choose the library that best suits your data visualization requirements.