Investigating Netflix Movies and Guest Stars of the show 'The Office'

1. Welcome!

Markdown .

The Office! What started as a British mockumentary series about office culture in 2001 has since spawned ten other variants across the world, including an Israeli version (2010-13), a Hindi version (2019-), and even a French Canadian variant (2006-2007). Of all these iterations (including the original), the American series has been the longest-running, spanning 201 episodes over nine seasons.

In this notebook, we will take a look at a dataset of The Office episodes, and try to understand how the popularity and quality of the series varied over time. To do so, we will use the following dataset: datasets/office_episodes.csv, which was downloaded from Kaggle here.

This dataset contains information on a variety of characteristics of each episode. In detail, these are:

datasets/office_episodes.csv

episode_number: Canonical episode number.
season: Season in which the episode appeared.
episode_title: Title of the episode.
description: Description of the episode.
ratings: Average IMDB rating.
votes: Number of votes.
viewership_mil: Number of US viewers in millions.
duration: Duration in number of minutes.
release_date: Airdate.
guest_stars: Guest stars in the episode (if any).
director: Director of the episode.
writers: Writers of the episode.
has_guests: True/False column for whether the episode contained guest stars.
scaled_ratings: The ratings scaled from 0 (worst-reviewed) to 1 (best-reviewed).

!pip install --user seaborn
!pip install --user missingno

import numpy as np
import pandas as pd

import missingno as msno

import matplotlib.pyplot as plt
import seaborn as sns

Importing Data

df = pd.read_csv('datasets/office_episodes.csv')
df.head()

df.shape

df.describe()

df.info()

Checking null values

# msno.matrix(df)

(df.isnull().sum()/df.shape[0]) * 100 # converting null values in term of percentage

Only guest_stars feature has missing values with 84.57% of the values missing.

Exploratory Data Analysis

def show_values_on_bars(axs):
    def _show_on_single_plot(ax):
        for p in ax.patches:
            _x = p.get_x() + p.get_width() / 2
            _y = p.get_y() + p.get_height()
            value = '{:.2f}'.format(p.get_height())
            ax.text(_x, _y, value, ha="center")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)

Hidden output

*Popularity, Quality, and Guest Appearances