Seaborn — it is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. This article discusses sea bay propagation plots that are used to study 1D and 2D distributions. In this article, we will discuss 4 types of distribution plots, namely:
In addition to providing various kinds of visualization plots, Seaborn also contains several built-in datasets. We will be using the tips dataset in this article. The "tip" dataset contains information about people who likely had a meal at a restaurant and whether they tip, their age, gender, and so on. Let`s take a look at this.
Now let`s move on to the plots.
It is used mainly for a one-dimensional set of observations and renders it using a histogram, i.e. only one observation, and hence we select one specific column of the dataset.
distplot (a [, bins, hist, kde, rug, fit, ...])
Now, looking at this, we can say that most of the total given count lies between 10 and 20.
It is used to plot two variables with 2D and 1D plots. It basically brings together two different plots.
jointplot (x, y [, data, kind, stat_func, ...])
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# KDE shows the density where the points match the most p>
It represents a pair relationship in the entire dataframe and supports an additional argument called hue , for categorical separation. What it does is basically create a joint plot between every possible numeric column and takes some time if the data frame is really huge.
pairplot (data [, hue, hue_order, palette,…])
It displays data points in an array as rods on the axis. As with the distributed schedule, it spans one column. Instead of drawing a histogram, it creates strokes across the entire graph. If you compare it to the connecting plot, you can see that the merged plot counts dashes and shows it as cells.
rugplot (a [, height, axis, ax])