seaborn.boxplot

Boxplots summarize numeric data over a set of categories. The data is divided into four groups called quartiles. A box is drawn connecting the innermost two quartiles, and a horizontal line is drawn at the position of the median (which always falls within the box). Usually, a second set of lines will be drawn some distance from the inner box denoting a “maximum” and “minimum” value for the data, and then values existing outside of these extrema are considered outliers and plotted as individual points. The location of these “whisker” lines is variable and generally some multiple of the innerquartile range (IQR), which is range of values covered by the inner box.

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	Thriller	Adventure	...	Documentary	Fantasy	Action
0	James Cameron	Action\|Adventure\|Fantasy\|Sci-Fi	178.0	0	1	...	0	1	1
1	Gore Verbinski	Action\|Adventure\|Fantasy	169.0	0	1	...	0	1	1
2	Sam Mendes	Action\|Adventure\|Thriller	148.0	1	1	...	0	0	1
3	Christopher Nolan	Action\|Thriller	164.0	1	0	...	0	0	1
4	Doug Walker	Documentary	NaN	0	0	...	1	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
7	100.0	Comedy	1
19	106.0	Comedy	1
35	104.0	Comedy	1
41	106.0	Comedy	1
43	103.0	Comedy	1

Basic plot

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

Change the order of categories

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

Desaturate

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

Adjust width of boxes

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

Change the size of outlier markers

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

Adjust the position of the whiskers as a fraction of IQR

p = sns.boxplot(data=df,
                x = 'Category',
                y = 'Duration',
                order = sorted(df.Category.unique()),
                whis=.2)

Add a notch to the box indicating a confidence interval for the median

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

p = sns.boxplot(data=df,
                x = 'Category',
                y = 'Duration',
                order = sorted(df.Category.unique()),
                notch=False,
                linewidth=2.5)

Finalize

sns.set(rc={"axes.facecolor":"#ccddff",
            "axes.grid":False,
            'axes.labelsize':30,
            'figure.figsize':(20.0, 10.0),
            'xtick.labelsize':25,
            'ytick.labelsize':20})
p = sns.boxplot(data=df,
                x = 'Category',
                y = 'Duration',
                palette = 'Paired',
                order = sorted(df.Category.unique()),
                notch=True)
plt.xticks(rotation=45)
l = plt.xlabel('')
plt.ylabel('Duration (min)')
plt.text(5.4,200, "Box Plot", fontsize = 95, color="black", fontstyle='italic')

<matplotlib.text.Text at 0x7f23580f4a58>

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