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 Crime Comedy Sci-Fi War Western Biography Reality-TV ... Sport Film-Noir Romance Short Family Animation Adventure Mystery Horror Thriller
0 James Cameron Action|Adventure|Fantasy|Sci-Fi 178.0 0 0 1 0 0 0 0 ... 0 0 0 0 0 0 1 0 0 0
1 Gore Verbinski Action|Adventure|Fantasy 169.0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 1 0 0 0
2 Sam Mendes Action|Adventure|Thriller 148.0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 1 0 0 1
3 Christopher Nolan Action|Thriller 164.0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 1
4 Doug Walker Documentary NaN 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 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
# 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')

png

make plot horizontal

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

png

Saturation

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

png

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>

png

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))

png

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)

png

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)

png

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)

png

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)

png

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]))

png

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)

png

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>

png

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