Excel, Tableau, Microsoft Power Bi, Google Sheets, SQL Business Intelligence Dashboards, and IBM Cognos Analytics are all great data visualization...
The two types of data visualizations are static and interactive visualizations. Static visualizations are just the final results or visual insights...
Charts, graphs, maps, bars, and plots are some of the approaches to visualizing data. There are many different charts such as Waterfall charts, bar...
A legend is the chart element that explains the symbols, textures, or colors used to differentiate data series
You can dynamically consolidate data by using 3D references in your own formulas
To capture one part of a computer screen, use the screen clipping command
When using an Advanced Filter, you need to create a criteria range
False, a data point is a slice
In this tutorial, we have plotted the tips dataset with the help of the four different plotting modules of Python namely Matplotlib, Seaborn, Bokeh, and Plotly. Each module showed the plot in its own unique way and each one has its own set of features like Matplotlib provides more flexibility but at the cost of writing more code whereas Seaborn being a high-level language provides allows one to achieve the same goal with a small amount of code. Each module can be used depending on the task we want to do.
This graph can be more meaningful if we can add colors and also change the size of the points. We can do this by using the c and s parameter respectively of the scatter function. We can also show the color bar using the colorbar () method.
In plotly, histograms can be created using the histogram () function of the plotly.express class.
Line Chart is used to represent a relationship between two data X and Y on a different axis. It is plotted using the plot () function. Let’s see the below example.
The histogram in Seaborn can be plotted using the histplot () function.