seaborn.violinplot

Violinplots summarize numeric data over a set of categories. They are essentially a box plot with a kernel density estimate (KDE) overlaid along the range of the box and reflected to make it look nice. They provide more information than a boxplot because they also include information about how the data is distributed within the inner quartiles. dataset: IMDB 5000 Movie Dataset

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
plt.rcParams['figure.figsize'] = (20.0, 10.0)
plt.rcParams['font.family'] = "serif"

df = pd.read_csv('../../../datasets/movie_metadata.csv')

df.head()

	color	director_name	num_critic_for_reviews	duration	director_facebook_likes	actor_3_facebook_likes	actor_2_name	actor_1_facebook_likes	gross	genres	...	num_user_for_reviews	language	country	content_rating	budget	title_year	actor_2_facebook_likes	imdb_score	aspect_ratio	movie_facebook_likes
0	Color	James Cameron	723.0	178.0	0.0	855.0	Joel David Moore	1000.0	760505847.0	Action\|Adventure\|Fantasy\|Sci-Fi	...	3054.0	English	USA	PG-13	237000000.0	2009.0	936.0	7.9	1.78	33000
1	Color	Gore Verbinski	302.0	169.0	563.0	1000.0	Orlando Bloom	40000.0	309404152.0	Action\|Adventure\|Fantasy	...	1238.0	English	USA	PG-13	300000000.0	2007.0	5000.0	7.1	2.35	0
2	Color	Sam Mendes	602.0	148.0	0.0	161.0	Rory Kinnear	11000.0	200074175.0	Action\|Adventure\|Thriller	...	994.0	English	UK	PG-13	245000000.0	2015.0	393.0	6.8	2.35	85000
3	Color	Christopher Nolan	813.0	164.0	22000.0	23000.0	Christian Bale	27000.0	448130642.0	Action\|Thriller	...	2701.0	English	USA	PG-13	250000000.0	2012.0	23000.0	8.5	2.35	164000
4	NaN	Doug Walker	NaN	NaN	131.0	NaN	Rob Walker	131.0	NaN	Documentary	...	NaN	NaN	NaN	NaN	NaN	NaN	12.0	7.1	NaN	0

5 rows × 28 columns

For the bar plot, let’s look at the number of movies in each category, allowing each movie to be counted more than once.

# split each movie's genre list, then form a set from the unwrapped list of all genres
categories = set([s for genre_list in df.genres.unique() for s in genre_list.split("|")])
# one-hot encode each movie's classification
for cat in categories:
    df[cat] = df.genres.transform(lambda s: int(cat in s))
# drop other columns
df = df[['director_name','genres','duration'] + list(categories)]
df.head()

	director_name	genres	duration	Fantasy	...	Sci-Fi	Action	Thriller
0	James Cameron	Action\|Adventure\|Fantasy\|Sci-Fi	178.0	1	...	1	1	0
1	Gore Verbinski	Action\|Adventure\|Fantasy	169.0	1	...	0	1	0
2	Sam Mendes	Action\|Adventure\|Thriller	148.0	0	...	0	1	1
3	Christopher Nolan	Action\|Thriller	164.0	0	...	0	1	1
4	Doug Walker	Documentary	NaN	0	...	0	0	0

5 rows × 29 columns

# convert from wide to long format and remove null classificaitons
df = pd.melt(df,
             id_vars=['duration'],
             value_vars = list(categories),
             var_name = 'Category',
             value_name = 'Count')
df = df.loc[df.Count>0]
top_categories = df.groupby('Category').aggregate(sum).sort_values('Count', ascending=False).index
howmany=10
df = df.loc[df.Category.isin(top_categories[:howmany])]
df.rename(columns={"duration":"Duration"},inplace=True)

df.head()

	Duration	Category	Count
0	178.0	Fantasy	1
1	169.0	Fantasy	1
7	100.0	Fantasy	1
9	153.0	Fantasy	1
13	151.0	Fantasy	1

Basic plot

p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration')

The outliers here are making things a bit squished, so I’ll remove them since I am just interested in demonstrating the visualization tool.

df = df.loc[df.Duration < 250]

p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration')

Change the order of categories

p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration',
                   order = sorted(df.Category.unique()))

Change the order that the colors are chosen

Change orientation to horizontal

p = sns.violinplot(data=df,
                   y = 'Category',
                   x = 'Duration',
                   order = sorted(df.Category.unique()),
                   orient="h")

Desaturate

p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration',
                   order = sorted(df.Category.unique()),
                   saturation=.25)

Adjust width of violins

p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration',
                   order = sorted(df.Category.unique()),
                   width=.25)

Change the size of outlier markers

p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration',
                   order = sorted(df.Category.unique()),
                   fliersize=20)

Adjust the bandwidth of the KDE filtering parameter. Smaller values will use a thinner kernel and thus will contain higher feature resolution but potentially noise. Here are examples of low and high settings to demonstrate the difference.

p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration',
                   order = sorted(df.Category.unique()),
                   bw=.05)

p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration',
                   order = sorted(df.Category.unique()),
                   bw=5)

Finalize

sns.set(rc={"axes.facecolor":"#e6e6e6",
            "axes.grid":False,
            'axes.labelsize':30,
            'figure.figsize':(20.0, 10.0),
            'xtick.labelsize':25,
            'ytick.labelsize':20})
p = sns.violinplot(data=df,
                   x = 'Category',
                   y = 'Duration',
                   palette = 'spectral',
                   order = sorted(df.Category.unique()),
                   notch=True)
plt.xticks(rotation=45)
l = plt.xlabel('')
plt.ylabel('Duration (min)')
plt.text(4.85,200, "Violin Plot", fontsize = 95, color="black", fontstyle='italic')

<matplotlib.text.Text at 0x7f9c6e707e48>

p.get_figure().savefig('../../figures/violinplot.png')