seaborn.countplot

Bar graphs are useful for displaying relationships between categorical data and at least one numerical variable. seaborn.countplot is a barplot where the dependent variable is the number of instances of each instance of the independent variable.

dataset: IMDB 5000 Movie Dataset

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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	Sci-Fi	...	Adventure	Thriller
0	James Cameron	Action\|Adventure\|Fantasy\|Sci-Fi	178.0	1	...	1	0
1	Gore Verbinski	Action\|Adventure\|Fantasy	169.0	0	...	1	0
2	Sam Mendes	Action\|Adventure\|Thriller	148.0	0	...	1	1
3	Christopher Nolan	Action\|Thriller	164.0	0	...	0	1
4	Doug Walker	Documentary	NaN	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
# add an indicator whether a movie is short or long, split at 100 minutes runtime
df['islong'] = df.duration.transform(lambda x: int(x > 100))
df = df.loc[df.Category.isin(top_categories[:howmany])]
# sort in descending order
#df = df.loc[df.groupby('Category').transform(sum).sort_values('Count', ascending=False).index]

df.head()

	duration	Category	Count	islong
45	140.0	Crime	1	1
59	91.0	Crime	1	0
66	152.0	Crime	1	1
100	106.0	Crime	1	1
157	90.0	Crime	1	0

Basic plot

p = sns.countplot(data=df, x = 'Category')

png

color by a category

p = sns.countplot(data=df,
                  x = 'Category',
                  hue = 'islong')

make plot horizontal

p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong')

Saturation

p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1)

Targeting a non-default axes

import matplotlib.pyplot as plt
fig, ax = plt.subplots(2)
sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  ax=ax[1])

<matplotlib.axes._subplots.AxesSubplot at 0x7f5609f2d860>

Add error bars

import numpy as np
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=7*np.arange(num_categories))

add black bounding lines

import numpy as np
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=7*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)

Remove color fill

import numpy as np
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=7*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2,
                  fill=False)

import numpy as np
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=7*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)

sns.set(font_scale=1.25)
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=3*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)

help(sns.set)

Help on function set in module seaborn.rcmod:
set(context='notebook', style='darkgrid', palette='deep', font='sans-serif', font_scale=1, color_codes=False, rc=None)
    Set aesthetic parameters in one step.
    Each set of parameters can be set directly or temporarily, see the
    referenced functions below for more information.
    Parameters
    ----------
    context : string or dict
        Plotting context parameters, see :func:`plotting_context`
    style : string or dict
        Axes style parameters, see :func:`axes_style`
    palette : string or sequence
        Color palette, see :func:`color_palette`
    font : string
        Font family, see matplotlib font manager.
    font_scale : float, optional
        Separate scaling factor to independently scale the size of the
        font elements.
    color_codes : bool
        If ``True`` and ``palette`` is a seaborn palette, remap the shorthand
        color codes (e.g. "b", "g", "r", etc.) to the colors from this palette.
    rc : dict or None
        Dictionary of rc parameter mappings to override the above.

plt.rcParams['font.family'] = "cursive"
#sns.set(style="white",font_scale=1.25)
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=3*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)

/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['cursive'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))

plt.rcParams['font.family'] = 'Times New Roman'
#sns.set_style({'font.family': 'Helvetica'})
sns.set(style="white",font_scale=1.25)
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=3*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2)

bg_color = (0.25, 0.25, 0.25)
sns.set(rc={"font.style":"normal",
            "axes.facecolor":bg_color,
            "figure.facecolor":bg_color,
            "text.color":"black",
            "xtick.color":"black",
            "ytick.color":"black",
            "axes.labelcolor":"black",
            "axes.grid":False,
            'axes.labelsize':30,
            'figure.figsize':(20.0, 10.0),
            'xtick.labelsize':25,
            'ytick.labelsize':20})
#sns.set_style({'font.family': 'Helvetica'})
#sns.set(style="white",font_scale=1.25)
num_categories = df.Category.unique().size
p = sns.countplot(data=df,
                  y = 'Category',
                  hue = 'islong',
                  saturation=1,
                  xerr=3*np.arange(num_categories),
                  edgecolor=(0,0,0),
                  linewidth=2,
                  palette="Dark2")
leg = p.get_legend()
leg.set_title("Duration")
labs = leg.texts
labs[0].set_text("Short")
labs[1].set_text("Long")
leg.get_title().set_color('white')
for lab in labs:
    lab.set_color('white')
p.axes.xaxis.label.set_text("Counts")
plt.text(900,2.8, "Count Plot", fontsize = 95, color='white', fontstyle='italic')

<matplotlib.text.Text at 0x7f5609d0ac18>

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