Seaborn | Python For Data Visualization | Distribution Plots | Seaborn Tutorial 1

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 | Distribution Plots
Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. This video deals with the distribution plots in seaborn which is used for examining univariate and bivariate distributions. In this video, we will be discussing 3 types of distribution plots namely.
1)joint plot
2)distplot
3)pair plot

Besides providing different kinds of visualization plots, seaborn also contains some built-in datasets. We will be using the tips dataset in this article. The “tips” dataset contains information about people who probably had food at a restaurant and whether or not they left a tip, their age, gender and so on. Let's have a look at it.

Displot:
It is used basically for a univariant set of observations and visualizes it through a histogram i.e. only one observation and hence we choose one particular column of the dataset.
Syntax:
distplot(a[, bins, hist, kde, rug, fit, ...])
Explanation:
KDE stands for Kernel Density Estimation and that is another kind of the plot in seaborn.
bins are used to set the number of bins you want in your plot and it actually depends on your dataset.
colour is used to specify the colour of the plot.

Jointplot:
It is used to draw a plot of two variables with bivariate and univariate graphs. It basically combines two different plots.
Syntax:
jointplot(x, y, data, kind, stat_func, ..)
Explanation:
kind is a variable that helps us play around with the fact as to how do you want to visualise the data. It helps to see what's going inside the jointplot. The default is scattered and can be hex, reg(regression) or KDE.
x and y are two strings that are the column names and the data that the column contains is used by specifying the data parameter.
here we can see tips on the y-axis and total bill on the x-axis as well as a linear relationship between the two that suggests that the total bill increases with the tips.

Pair plot:
It represents pairwise relation across the entire data frame and supports an additional argument called hue for categorical separation. What it does basically creates a jointplot between every possible numerical column and takes a while if the data frame is really huge.
Syntax:
pairplot(data, hue, hue_order, palette, …)
Explanation:
hue sets up the categorical separation between the entries if the dataset.
the palette is used for designing the plots.


Source Code & Link:
https://drive.google.com/drive/folders/1qLA5eo8dVjMJg-nirLcjGAZ46DmoV7ry?usp=sharing

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For more videos:
1) Seaborn Categorical Plots
https://youtu.be/NUUrkiMgQ5I

2)Seaborn Matrix plots
https://youtu.be/n28R6Qe1ckg

3)Plot Data from multiple columns from Excel file
https://youtu.be/sm9nkhrXUQI

4)How to Plot graph using python
https://youtu.be/j5jdp77ynAY

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