seaborn.barplot

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
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 Reality-TV Family Biography Comedy Action Crime Sci-Fi ... Mystery Film-Noir Sport Adventure Drama Romance Western War Animation News
0 James Cameron Action|Adventure|Fantasy|Sci-Fi 178.0 0 0 0 0 1 0 1 ... 0 0 0 1 0 0 0 0 0 0
1 Gore Verbinski Action|Adventure|Fantasy 169.0 0 0 0 0 1 0 0 ... 0 0 0 1 0 0 0 0 0 0
2 Sam Mendes Action|Adventure|Thriller 148.0 0 0 0 0 1 0 0 ... 0 0 0 1 0 0 0 0 0 0
3 Christopher Nolan Action|Thriller 164.0 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 0 0
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
15136 100.0 Comedy 1 0
15148 106.0 Comedy 1 1
15164 104.0 Comedy 1 1
15170 106.0 Comedy 1 1
15172 103.0 Comedy 1 1

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 0x111017278>

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

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)

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 = 'white'
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,
            'font.size':20,
            '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)
leg = p.get_legend()
leg.set_title("")
labs = leg.texts
labs[0].set_text("Short")
labs[0].set_fontsize(25)
labs[0].set_size(30)
labs[1].set_text("Long")
leg.get_title().set_color('black')
p.axes.xaxis.label.set_text("Counts")
plt.text(900,2, "Bar Plot", fontsize = 95, color='Black', fontstyle='italic')

<matplotlib.text.Text at 0x112bbc400>

png

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