Seaborn: Styling Your Plot

Styling Your Plot

1) Built‒in themes

•  Seaborn has five built‒in themes to style its plots: darkgrid, whitegrid, dark, white and ticks.

•  The default theme is darkgrid theme for its plots, but we can change this styling to better suit your presentation needs.

• To use these themes we pass the name of it to sns.set_style().

Example code

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

# read dataset

titanic = pd.read_csv("d:/titanic.csv")

print(titanic.head())

# create plot

sns.set_style("whitegrid")

sns.countplot(x = 'Pclass',hue='Sex', data = titanic)

plt.title('Survivors')

plt.show()

Output

Survivors

2) Scaling plots

• For scaling the plots there are four presets which sets the size of the plot and allows to customize the figure.

•  These size are ‒ paper, notebook, talk, and poster. The notebook style is the default. We can set the visual format, or context, using sns.set_context( )

•  Following code makes use of poster size for scaling plot.

Example code

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

# read dataset

titanic = pd.read_csv("d:/titanic.csv")

print(titanic.head())

# create plot

sns.set_style("whitegrid")

sns.set_context("poster")

sns.countplot(x = 'Pclass',hue='Sex', data = titanic)

plt.title('Survivors')

plt.show()

Output

3) Setting the color palette

• Using the set_palette() method is used to set the palette for plots.  

Syntax

set_palette(palette, n_colors=None, desat=None, color_codes=False)

Parameters

• Palette: The palette that is to be set.

• n_colors: Number of colors in the cycle.

• Desat: Proportion to desaturate each color by.

• color_codes: Takes Booleans values and remaps the shorthand color codes (such as "b," "g," "r," etc.) to the colours from this palette if True is passed.

Example code

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

#read dataset

titanic = pd.read_csv("d:/titanic.csv")

print(titanic.head())

# create plot

sns.set_context("paper")

sns.set_palette("flare")

sns.countplot(x = 'Pclass',hue='Sex', data = titanic)

plt.title('Survivors')

plt.show()

Output

•  There are various color palettes available in seaborn. Some commonly used color palette names are ‒ rocket, mako, flare, crest, viridis, plasma, inferno, magma, cividis.

•  Similarly we can set sequential color pattern using palette name as Greys, Reds, Greens, Blues, Oranges, Purples, BuGn, BuPu, GnBu, OrRd, PuBu, PuRd, RdPu, YIGn, PuBuGn, YlGnBu, YlOrBr and YIOrRd.

4) Setting title, X‒axis label and Y‒axis label

•  For setting title to the graph the set_title() method is used. Similarly we can set the labels to the x‒axis and y‒axis using the methods set_xlabel and set_ylabel.

Example code

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

# read dataset

titanic = pd.read_csv("d:/titanic.csv")

print(titanic.head())

# create plot

res=sns.countplot(x = 'Pclass',hue='Sex',data= titanic)

res.set_title("Titanic Survivors', fontdict={'size': 20, 'weight': 'bold'})

res.set_xlabel('Class', fontdict={'size': 10})

res.set_ylabel('count of Persons', fontdict={'size': 10})

plt.show()

Output

Review Questions

1.What is the use of Seaborn in Python? Enlist the features of it.

2.What is the difference between Matplotlib and Seaborn?

3.Write short note on ‒ Statistical data visualization.

4. Explain how to plot in Seaborn with suitable example for sample data set.

5.What is subplot in Seaborn? Give example.

6.How will you set title, X‒axis and Y‒axis labels to the plot in Seaborn?