Offers over 40 different chart types, including scatter plots, box plots, and heatmaps. Generates interactive visualizations that can be embedded into web applications. Works seamlessly with Jupyter ...

A simple Python project using Matplotlib and Seaborn to visualize the Seaborn tips dataset. Includes a line plot, scatter plot, histogram, and box plot. Inspired by Microsoft Fabric’s data science ...

Statistical Plots: Seaborn excels at creating plots that visualize the distribution of data, such as box plots, violin plots, and pair plots. Aesthetics: Seaborn’s default themes and color palettes ...

This repository contains the materials for D-Lab's Python Data Visualization workshop. We recommend attending Python Fundamentals prior to taking this workshop. Anaconda is a useful package management ...

Data Visualization - Plotly and Cufflinks. Plotly is a library that allows you to create interactive plots that you can use in dashboards or websites (you can save them as html files or static images) ...

You can create box plots manually using software packages like Python's Matplotlib or R's ggplot2, or you can use specialized time-series visualization libraries that offer built-in functionality ...

Python-based Tools for Visualization. In this section, we will overview some of the famous python-based tools used for data visualization. Matplotlib. Matplotlib is a Python data visualization and 2-D ...

NumPy: Short for Numerical Python, NumPy provides support for arrays, matrices, and a large collection of mathematical functions to efficiently operate on these data structures. Matplotlib: This ...