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Introduction to Seaborn Plots

Rajesh Majumder

April 18, 2023

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.

Installation of Seaborn

Install it using this command:(write the command on the command python command prompt/ ANACONDA prompt)

pip install seaborn

or

conda install seaborn

Import Seaborn

Once Seaborn is installed, import it in your applications by adding the __import__ keyword:

Checking Seaborn Version

The version string is stored under __version__ attribute.

import seaborn as sns
print(sns.__version__)

0.11.2

## Import matplotlib

import matplotlib.pyplot as plt 

Distribution plots

• distplot
• jointplot
• pairplot

Data set

df= sns.load_dataset("tips")

df.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

df.dtypes  ## Data types

total_bill     float64
tip            float64
sex           category
smoker        category
day           category
time          category
size             int64
dtype: object

Correlation with Heatmap

A correlation heatmap uses colored cells, typically in a monochromatic scale, to show a 2D correlation matrix(table) between two discrete dimensions or event types, it is very important for EDA.

df.corr()

	total_bill	tip	size
total_bill	1.000000	0.675734	0.598315
tip	0.675734	1.000000	0.489299
size	0.598315	0.489299	1.000000

## HeatMap

sns.heatmap(df.corr())

<AxesSubplot:>

Univariate Analysis

Histogram And Kernel Density plot

sns.displot(df['tip'],kind='kde') ## Kernel Density
plt.title("Kernel Density Plot")

sns.displot(df['tip']) ## Histogram
plt.title("Histogram")

Text(0.5, 1.0, 'Histogram')

## Overlapping KD on Histogram using sns.distplot()

sns.distplot(df['tip'])

C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='tip', ylabel='Density'>

Two Dimensional Distribution Plot

1. Simple Histograms & KD Plot

sns.distplot(df['total_bill'],label="Total Bill")
sns.distplot(df['tip'],label="Tip")
plt.legend()

C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\User\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<matplotlib.legend.Legend at 0x2118673df70>

sns.kdeplot(df.iloc[:,0],label="Total Bill")
sns.kdeplot(df.iloc[:,1],label="Tip")
plt.legend()

<matplotlib.legend.Legend at 0x211865379a0>

Join Plot

It plots X-Y scatter plot along with Merginal distributions (Hist./KD) of X & Y.

sns.jointplot(x='total_bill',y='tip',data=df)

<seaborn.axisgrid.JointGrid at 0x2118b44d400>

sns.jointplot(x='total_bill',y='tip',data=df,kind='hex') ## kind='hex' draws Hexagon

<seaborn.axisgrid.JointGrid at 0x21189bf2b50>

sns.jointplot(x='total_bill',y='tip',data=df,kind='reg') ## kind='reg' draws regression Line

<seaborn.axisgrid.JointGrid at 0x2118c4ec9d0>

sns.jointplot(x='total_bill',y='tip',data=df,kind='kde') ## kind='kde' draws Kernel Density

<seaborn.axisgrid.JointGrid at 0x2118df0b610>

penguins = sns.load_dataset("penguins")
sns.jointplot(data=penguins, x="flipper_length_mm", y="bill_length_mm", hue="species")

<seaborn.axisgrid.JointGrid at 0x2118f8a4d30>

Pair Plot

A pair plot is also known as a scatterplot, in which one variable in the same data row is mattached with another variable's value,like this Pairs plots are just elaborations on this, showing all variables paired with all the other variables.

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x2118e317490>

sns.pairplot(df,hue="sex") ## Group By Sex

<seaborn.axisgrid.PairGrid at 0x2118e317520>

sns.pairplot(data=penguins, hue="species")

<seaborn.axisgrid.PairGrid at 0x2118bbd0e50>

Categorical Plors

• Boxplot
• Countclot
• barplot

1. Boxplot

sns.boxplot(x='smoker',y='total_bill',data=df)

<AxesSubplot:xlabel='smoker', ylabel='total_bill'>

sns.boxplot(x='total_bill',y='day',hue='smoker',data=df,palette='rainbow')

<AxesSubplot:xlabel='total_bill', ylabel='day'>

2. Countplot

sns.countplot(x='sex',data=df)

<AxesSubplot:xlabel='sex', ylabel='count'>

sns.countplot(y='day',data=df) ## Horizontal Countplot

<AxesSubplot:xlabel='count', ylabel='day'>

3. Barplot

sns.barplot(x='sex',y='tip',data=df)

<AxesSubplot:xlabel='sex', ylabel='tip'>

Further Reading: https://seaborn.pydata.org/tutorial/introduction.html