Seaborn | Python For Data Visualization | Categorical Plots | Seaborn Tutorial 3

1) Plotting graph using Seaborn | Python
This video will introduce you to graphing in python with Seaborn, which is the most popular statistical visualization library in Python.
Installation:
The easiest way to install seaborn is to use pip. Type following command in terminal.
pip install seaborn.

2)Seaborn | Categorical Plots
Plots are basically used for visualizing the relationship between variables. Those variables can be either be completely numerical or a category like a group, class or division. This video deals with categorical variables and how they can be visualized using the Seaborn library provided by Python.
Seaborn besides being a statistical plotting library also provides some default datasets. We will be using one such default dataset called ‘tips’. The ‘tips’ dataset contains information about people who probably had food at a restaurant and whether or not they left a tip for the waiters, their gender, whether they smoke and so on.
In this video, we will be discussing 3 types of distribution plots namely.
1)Count plot
2)Strip plot
3)swarm plot
4)Box plot

1)Count plot
A countplot basically counts the categories and returns a count of their occurrences. It is one of the most simple plots provided by the seaborn library.

Syntax:
countplot([x, y, hue, data, order, …])

Explanation
As it only returns the count based off a categorical column, we need to specify only the x parameter.

2)Boxplot
A boxplot is sometimes known as the box and whisker plot.It shows the distribution of the quantitative data that represents the comparisons between variables. boxplot shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution i.e. the dots indicating the presence of outliers.

Syntax:
boxplot([x, y, hue, data, order, hue_order, …])

Explanation
x takes the categorical column and y is a numerical column.”hue” parameter is used to further add a categorical separation.

3)Stripplot
It basically creates a scatter plot based on the category.

syntax:
stripplot([x, y, hue, data, order, …])

Explanation
One problem with strip plot is that you can’t really tell which points are stacked on top of each other and hence we use the jitter parameter to add some random noise.
jitter parameter is used to add an amount of jitter (only along the categorical axis) which can be useful when you have many points and they overlap, so that it is easier to see the distribution.
hue is used to provide an addition categorical separation
setting split=True is used to draw separate strip plots based on the category specified by the hue parameter.

4)Swarmplot
It is very similar to the stripplot except the fact that the points are adjusted so that they do not overlap.Some people also like combining the idea of a violin plot and a stripplot to form this plot. One drawback to using swarmplot is that sometimes they dont scale well to really large numbers and takes a lot of computation to arrange them. So in case we want to visualize a swarmplot properly we can plot it on top of a violinplot.

Syntax:
swarmplot([x, y, hue, data, order, …])

Seaborn Official documentation:

Source Code & Link

For more videos:
1) Seaborn Categorical Plots

2)Seaborn Matrix plots

3)Seaborn Distribution Plots

4)Plot Data from multiple columns from Excel file

5)How to Plot graph using python

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#Seaborntutorial #Seabornpython #Seabornpythontutorial
#Seaborncategoricalplots #Categoricalplots

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[majddabour]

God Bless you, thanks for this helpful video ♡