Visualizing data using Plotly

Visualizing data is essential in data analysis workflows because it simplifies complex information and highlights patterns while also improving communication and aiding in quality assessment. Additionally, data visualizations enable the detection of trends, anomalies, and relationships in data, serving as a foundational tool for exploratory data analysis.

There are many libraries available in Python to let you create visualizations, including, but not limited to, Matplotlib, Seaborn, Plotly, and Altair. Note that not all the data visualization libraries have built-in compatibility with Polars DataFrames.

In this recipe, we’ll explore the data by visualizing data using the plotly library. It is already compatible with Polars DataFrames.

Getting ready

You need to install plotly for this recipe. Use the following command to install it with pip:

>>> pip install plotly

You’ll also need the nbformat library to render Plotly...