seaborn pairplot tutorial with example | seaborn pairplot in python

Seaborn pairplot in Python tutorial

Seaborn pairplot in Python is made when you want to visualize the relationship between two variables and variables. Pairplot is usually a grid of plots for each variable in data set and sepal width, height. It is the data set.4 measurements it create 4*4 plots.

Creating a Pairs Plot in Python:-

The function in R is the pair plot which makes high level scatter plot to capture relationship between multiple variables within dataframe.

To my knowledge python does not have any built-in functions which accomplish this so I turned to Seaborn, the statistical visualization library built on matplotlib, to accomplish this.
Example:-
           Import seaborn
           sb.set(font_size=1.35,style=”ticks”)#set styling preferences
           %matplotlib inline

Load the example iris dataset:-

Iris=sb.load_dataset(“iris”)
Iris.head()
Output:-

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

Iris.describe()

	sepal_length	sepal_width	petal_length	petal_width
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.057333	3.758000	1.199333
std	0.828066	0.435866	1.765298	0.762238
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

plot = sb.pa will show how different levels of a categorical variable by the color of plot elements
g=sns.pairplot(iris,hue=”species”)

seaborn pairplot tutorial with example

Use a different color palette:-

Irplot(iris,hue=’specie’)

pairplot in python

Plot pair wise relationships in a dataset.
The function will create a grid such as variable in data will share in y axis across and x axis across single column.
It is possible to show a plot to show distribution of data in column.
It is also possible to show a subset of variable or plot different variables on the rows and columns.
This is a high level interface for pair grid that is intended to make it easy to draw common style.

Parameters:

data : DataFrame
Tidy dataframe where each column is a variable and each row is an observation.
hue : string , optional
Variable in data to map plot aspects to different colors.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the hue variable. If a dict keys should be values in the hue variable.
vars : list of variable names optional
Variables within data to use otherwise use every column with a numeric datatype.
{x, y}_vars : lists of variable names, optional
Variables within data to use separately for the rows and columns of the figure.

kind

Kind of plot for the non-identity relationships.
diag_kind : {‘auto’, ‘hist’, ‘kde’}, optional
Kind of plot for the diagonal subplots. The default depends on whether "hue" is used or not.
markers : single matplotlib marker code or list, optional
Either the marker to use for all datapoints or a list of markers with a length the same as the number of levels in the hue variable so that differently colored points will also have different scatterplot markers.
Height : scalar, optional
Height of each facet.
aspect : scalar, optional
Aspect * height gives the width of each facet.
dropna : boolean, optional
Drop missing values from the data before plotting.
{plot, diag, grid}_kws : dicts, optional
Dictionaries of keyword arguments.

Returns:

grid : PairGrid
Returns the underlying Pair Grid instance for further tweaking.

Subplot grid for more flexible plotting of pair wise relationship.

Examples:-
Draw scatter plots for joint relationships and histograms for univariate distributions.
           import seaborn as sns;sns.set(style=”ticks”,color_codes=True)
           iris=sns.load_dataset(“iris”)
           g=sns.pairplot (iris)

Output:-

pairplot python