NumPy – 85 – visualizzazioni con Seaborn – 1

Continuo da qui, copio qui.

Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired. There are several valid complaints about Matplotlib that often come up: No non faccio l’elenco; tanto è vecchio 😊

An answer to these problems is Seaborn. Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas DataFrames.

To be fair, the Matplotlib team is addressing this: it has recently added the plt.style tools discussed in Customizing Matplotlib: Configurations and Style Sheets [qui], and is starting to handle Pandas data more seamlessly. The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo. But for all the reasons just discussed, Seaborn remains an extremely useful addon.

Confronto tra Seaborn e Matplotlib
Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.

Although the result contains all the information we’d like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.

Now let’s take a look at how it works with Seaborn. As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib’s default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output. We can set the style by calling Seaborn’s set() method. By convention, Seaborn is imported as sns:

Ah, much better!

Esplorazione dei grafici di Seaborn
The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.

Let’s take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following could be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient.

istogrammi, KDE (finestre di Parzen) e grafici di densità
Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables. We have seen that this is relatively straightforward in Matplotlib:

Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with sns.kdeplot:

Histograms and KDE can be combined using distplot:

If we pass the full two-dimensional dataset to kdeplot, we will get a two-dimensional visualization of the data:

We can see the joint distribution and the marginal distributions together using sns.jointplot. For this plot, we’ll set the style to a white background:

There are other parameters that can be passed to jointplot —for example, we can use a hexagonally based histogram instead:

grafici di coppie (pair)
Mica sicuro della traduzione, nèh.

When you generalize joint plots to datasets of larger dimensions, you end up with pair plots. This is very useful for exploring correlations between multidimensional data, when you’d like to plot all pairs of values against each other.

We’ll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:

Visualizing the multidimensional relationships among the samples is as easy as calling sns.pairplot:

istogrammi sfaccettati
Sometimes the best way to view data is via histograms of subsets. Seaborn’s FacetGrid makes this extremely simple. We’ll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:

grafici factor
Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:

unione di distribuzioni
Similar to the pairplot we saw earlier, we can use sns.jointplot to show the joint distribution between different datasets, along with the associated marginal distributions:

The joint plot can even do some automatic kernel density estimation and regression:

grafici a barre
Time series can be plotted using sns.factorplot. In the following example, we’ll use the Planets data that we first saw in Aggregation and Grouping [qui]:

We can learn more by looking at the method of discovery of each of these planets:

OK, ci vorrebbe un monitor più largo 😊

For more information on plotting with Seaborn, see the Seaborn documentation, a tutorial, and the Seaborn gallery.

Non so voi ma io sono thunderstrucked, very assay 😯

:mrgreen:

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