Day 25 - Seaborn - Violinplot
By Jerin Lalichan Violinplot It is similar to the boxplot except that it provides a higher, more advanced visualization and uses the kernel density estimation to give a better description of the data distribution. A violin plot plays a similar role as a box and whisker plot. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike a box plot, in which all of the plot components correspond to actual data points, the violin plot features a kernel density estimation of the underlying distribution. This can be an effective and attractive way to show multiple distributions of data at once, but keep in mind that the estimation procedure is influenced by the sample size, and violins for relatively small samples might look misleadingly smooth.




The article gives a focused introduction to two commonly used Seaborn visualizations: line plots and bar plots. The explanation of line plots as a way to visualize continuous or time-series data provides a straightforward starting point for understanding when this chart type is useful.
ReplyDeleteThe discussion of bar plots is also helpful, particularly the explanation of using a categorical variable on the x-axis and a numerical variable on the y-axis to visualize aggregated values. These practical examples make the topic relevant for learners working through a Seaborn Course.
Together, the line plot and bar plot examples demonstrate how different chart types can be selected according to the structure and purpose of the data. These concepts provide a useful foundation for broader Data Visualization Training with Python.
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