Hi. In this video we will see a few other interesting prototypes,
which can give us more information about underlying patterns and visualize
information more effectively.
We have already tried before the one dimensional density plot,
but this chart can be done also using two variables.
We see it here in this first example where now we see clearly
what are the most dense areas when analyzing the relationship between count and
service count variables.
Here we can see the density when filtering only normal connections with values
under 50. We sample the data here, uh,
and we did it also in the previous example because the density for
whole data set takes, uh, while to calculate
similar information we can get with the scatter plot histogram using
sea born's, uh, joint plot.
But we can see the distribution of both variables here
in, uh, one of our previous modules we used already the scatter plot from
seaborne with added regression line, just as a reminder.
But the body is also more interesting and it helps also a lot
is the lowest curve. Uh, that is, uh,
basically a locally elevated linear regression. Here,
it seems like a straight line, uh, but can filter some data out.
And uh, we see that it's basically a curve.
We can see basically much more clearly the extent of the linear relationship
of the variables.
Another useful chart type is the tree map,
which can be used to better see the counts of multiple categorical variables.
Uh,
but we can also map numeric variables to bin size or color as well.
The violin plots are combination of, uh, density plot and a box plot.
And the last chart type, which are use very frequently,
is the other plot. They are very useful, uh, when making, uh,
profiling of some groups or clusters, for example. Uh,
basically the numeric variables are plotted in a circle where each line is
a specific group.
I usually also pre-process the data with standard scaler,
where zero is the average and one is the standard deviation. Uh,
but you can use also other scaling methods like, uh,
robust scaler, for example, where you do not use average and standard deviation,
but quantiles,
we have to also reshape the data from wide to long format, uh,
using melt method from pandas.
And then we use the line polar method from Plotly.
In our next video, we will check two Jupiter lab extensions,
which help us a little with the plotting and exploratory analysis.