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Data Visualization using Violin Plot (Seaborn Library)
Kisah Nakal
April 17, 2019
Lets visualize our data with Violin Plot which is present in Seaborn library.
We can pass various parameters to violinplot like hue, split, inner (quartile, stick), scale, scale_hue, bandwidth (bw), palette, order etc.
Lets explore Violin Plot using Tips dataset.
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Step 2: Load Tips datasets
tips=sns.load_dataset('tips')
tips.head()
Step 3: Explore data using Violin Plot
sns.violinplot(x=tips['tip'])
sns.violinplot(x='day', y='total_bill', data=tips)
Add hue and split parameter
sns.violinplot(x='day', y='total_bill', data=tips, hue='sex')
sns.violinplot(x='day', y='total_bill', data=tips, hue='sex', split=True)
sns.violinplot(x='day', y='total_bill', data=tips, hue='sex', palette='RdBu')
sns.violinplot(x='day', y='total_bill', data=tips, hue='sex', order=['Sat', 'Sun', 'Thur', 'Fri'])
Add inner and scale parameter
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='quartile')
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='quartile', split='True')
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='quartile', split='True', scale='count')
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='stick', split='True', scale='count')
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='stick', split='True', scale='count', scale_hue=False)
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='stick', split='True', scale='count', scale_hue=False, bw=0.1)
You can download my Jupyter notebook from here. I recommend to also try above code with Iris dataset.
We can pass various parameters to violinplot like hue, split, inner (quartile, stick), scale, scale_hue, bandwidth (bw), palette, order etc.
Lets explore Violin Plot using Tips dataset.
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Step 2: Load Tips datasets
tips=sns.load_dataset('tips')
tips.head()
Step 3: Explore data using Violin Plot
sns.violinplot(x=tips['tip'])
sns.violinplot(x='day', y='total_bill', data=tips)
Add hue and split parameter
sns.violinplot(x='day', y='total_bill', data=tips, hue='sex')
sns.violinplot(x='day', y='total_bill', data=tips, hue='sex', split=True)
sns.violinplot(x='day', y='total_bill', data=tips, hue='sex', palette='RdBu')
sns.violinplot(x='day', y='total_bill', data=tips, hue='sex', order=['Sat', 'Sun', 'Thur', 'Fri'])
Add inner and scale parameter
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='quartile')
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='quartile', split='True')
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='quartile', split='True', scale='count')
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='stick', split='True', scale='count')
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='stick', split='True', scale='count', scale_hue=False)
sns.violinplot(x='day', y='total_bill', data=tips, hue='smoker', inner='stick', split='True', scale='count', scale_hue=False, bw=0.1)
You can download my Jupyter notebook from here. I recommend to also try above code with Iris dataset.
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