Introduction to Seaborn

seaborn builds on top of Matplotlib, and it is mostly deployed with pandas.

import seaborn as sns

sns.__version__

'0.11.0'

sns.set()
sns.set_style('darkgrid')
sns.set_color_codes()

current_palette = sns.color_palette()
sns.palplot(current_palette)

png

Load the tips dataset, which included in Seaborn.

tips = sns.load_dataset("tips")

tips.head(10)

	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
5	25.29	4.71	Male	No	Sun	Dinner	4
6	8.77	2.00	Male	No	Sun	Dinner	2
7	26.88	3.12	Male	No	Sun	Dinner	4
8	15.04	1.96	Male	No	Sun	Dinner	2
9	14.78	3.23	Male	No	Sun	Dinner	2

Let’s visualize the data using relplot.

sns.relplot(
    data=tips,
    x="total_bill", y="tip",
    hue="smoker", style="smoker"
)

<seaborn.axisgrid.FacetGrid at 0x7fce7dbef470>

png

sns.relplot(
    data=tips,
    x="total_bill", y="tip", col="time",
    hue="smoker", style="smoker", size="size",
)

<seaborn.axisgrid.FacetGrid at 0x7fce7dbdc588>

png

dots = sns.load_dataset("dots")

sns.relplot(
    data=dots, kind="line",
    x="time", y="firing_rate", col="align",
    hue="choice", size="coherence", style="choice",
    facet_kws=dict(sharex=False),
)

<seaborn.axisgrid.FacetGrid at 0x7fce79fb2358>

png

Let’s visualize the dataset using catplot.

sns.catplot(x='day', y='total_bill', hue='smoker',
            col='time', aspect=.6,
            kind='swarm', data=tips)

<seaborn.axisgrid.FacetGrid at 0x7fce79ec3a20>

png

We can play with the Titanic dataset, which is already included in Seaborn.

titanic = sns.load_dataset('titanic')

t = sns.catplot(x='fare', y='survived', row='class', 
                kind='box', orient='h', height=1.5, aspect=4, 
                data=titanic.query('fare > 0'))
t.set(xscale='log');

png

Let’s visualize the Iris data using pairplot. The Iris dataset is already included in Seaborn.

iris = sns.load_dataset('iris')

sns.pairplot(iris, hue = 'species')

<seaborn.axisgrid.PairGrid at 0x7fce7dbc4d30>

png

Maybe we can change the color?

# Reference for color: https://seaborn.pydata.org/tutorial/color_palettes.html

sns.pairplot(iris, hue = 'species', palette='dark')

<seaborn.axisgrid.PairGrid at 0x7fce77d04748>

png

sns.pairplot(iris, kind='kde')

<seaborn.axisgrid.PairGrid at 0x7fce7777d390>

png

sns.pairplot(iris, kind='hist')

<seaborn.axisgrid.PairGrid at 0x7fce88e085c0>

png

Let’s visualize the flights dataset using heatmap. We will also use matplotlib.

from matplotlib import pyplot as plt

flights = sns.load_dataset('flights')
flights = flights.pivot('month', 'year', 'passengers')
plt.figure(figsize=(15, 15))
ax = sns.heatmap(flights, annot=True, fmt='d')

png

References

“seaborn: statistical data visualization”