Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
R, provides some great data visualization (ggplot2, leaflet) and dashboarding (using R Shiny) packages using which you can create beautiful visualizations. Python, on the other hand, lags behinds a bit in this aspect as matplotlib is not a great visualization package. Seaborn is a good alternative for creating static plots in python but doesn’t have the capability of making these interactive. With static plot, we cannot zoom into the interesting sections of the plots, hover over the plots to see the specific information and more.
Plotly is a Python library for creating interactive, publication-quality visualizations. Plotly not only makes the plots interactive, a functionality missing in matplotlib or seaborn, but also provides a variety of more charts such as : Statistical Charts such as tree plots, error bands, parallel categories diagram etc. Scientific Charts such as contour plots, log plots etc. Financial Charts such as funnel charts, candlestick charts etc Maps such as bubble map, density map etc. Bioinformatics and more.
In the following section, we will use gapminder data to visualize how the India and China progressed socially and economically over the years.